“Cryptocurrency system using body activity data” pat. n. = WO2020060606A1

[ESG]

-I'm reposting it here at the level of information and research.

- If it can't stay here, you can move and or delete it.

-I will also warn the author, with the link to this one. because I appreciate his analysis/summary on the
subject/research.  (the date of international publication of, was on 26 march, 2020)
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The patent, titled “Cryptocurrency system using body activity data”, describes a SYSTEM in which a user’s body activity—measured through BIOSENSORS such as EEG sensors, fMRI scanners, heart-rate monitors, thermal sensors, optical sensors, or other devices—can be used as input data for verifying tasks in a cryptocurrency process. The system involves a task server providing activities to the user, a sensor that captures body activity during or after the task, and a cryptocurrency system that verifies whether the biosensor-derived data meets required conditions before awarding digital currency. This approach is presented as an alternative to conventional proof-of-work mining, aiming to reduce computational energy demands while integrating human body activity data into the verification process.

I have MANY posts on Biosensors in Covid tests and vaccines. They had to put biosensors in everyone to test the upcoming system.
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The patent explicitly allows both wearable/external sensors and in-body sensors.
In the detailed description, it defines “sensor” broadly as any device capable of detecting or measuring body activity. Examples include:
External / wearable: EEG headbands, smartwatches, fitness trackers, optical sensors, temperature sensors, fMRI, etc.
Implanted / in-body: subcutaneous sensors, implanted chips, electrodes, biosensors capable of detecting brain activity, blood flow, body chemistry, etc.
The claims are written broadly so that the protection covers any type of sensor, whether external or implanted, that can measure body activity data and feed it to the cryptocurrency system.
So in short: Yes, in-body sensors are covered as possible embodiments. The patent doesn’t restrict itself to wearables — it leaves the door open for sensors “in the body, on the body, or near the body.”
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Below is a detailed breakdown, section by section, of WO2020060606A1 – “Cryptocurrency system using body activity data” (Microsoft)
https://patents.google.com/patent/WO2020060606A1/en
Overview / Abstract
Proposes a system where body activity data (e.g., brain waves, body heat, etc.) of a user performing a task is used as part of a “mining-like” process in a cryptocurrency system.
The idea is to use the body’s activity (instead of or in addition to large computational work) as a proof‐of‐work (or analogous difficulty check) to verify that a user has done something (task) and then award cryptocurrency.
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Background
Talks about how existing cryptocurrency mining (proof of work) requires massive computational energy, solving difficult problems.
Raises the issue of energy costs, inefficiency.
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Summary of the Invention
Suggests replacing or augmenting conventional proof‐of‐work with human body activity while users perform tasks (e.g. watching ads, using services).
The system involves sensors sensing body activity, generating “body activity data”, a verification by the cryptocurrency system that these data satisfy certain conditions, and then awarding cryptocurrency.
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Definitions & Key Components
Body activity: could be anything measurable by sensors: fMRI, EEG, heart rate, brain waves, body heat, movement, etc.
Sensor: may be external or built into the user device (could be wearable or integrated) that captures body activity data.
User device: device used by user, communicatively coupled to sensor, possibly wearable, phone, computer, etc.
Task server: server providing tasks to user (ads, content, services, etc.)
Cryptocurrency system / network: receives data, verifies conditions, and awards cryptocurrency. Could be centralized or decentralized (e.g. blockchain).
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How It Works — Main Flow / Method
1. Task Issuance
The user is provided one or more tasks via the server. Tasks might be watching an ad, using a service, uploading content, etc.
2. Sensing Body Activity
While or after the user does the task, a sensor captures body activity (brain waves, movement, pulses, etc.).
3. Generating Body Activity Data
From the raw sensor data, the device (or a server) processes it: codification (sampling, extracting features, transforming, possibly filtering), maybe hashing etc.
For example, maybe extract frequency bands from EEG, use Fast Fourier Transform or similar to convert signals to a useful numeric form.
4. Verification by Cryptocurrency System
The system checks whether the generated body activity data meets certain conditions. Conditions might be: pattern in the hash, threshold, similarity to expected data, etc.
Could also include ensuring data is from a human (not synthetic / faked), re‐hashing, checking that the hash matches the pre‐image, checking statistical properties.
5. Awarding Cryptocurrency
If verification passes, the user is awarded cryptocurrency (or other rewards).
Possibly the task server or provider also gets rewarded for providing the task/service.
6. Blockchain / Logging
Blocks containing the transaction (task done, body activity data or its hash, user address, etc.) are added to the ledger / blockchain.
Network nodes validate, broadcast new blocks, etc.
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Additional Embodiments / Variations
Using vectors / embeddings: Instead of raw data or simple hashes, one embodiment uses vector representations (embeddings), e.g. converting fMRI voxels via ML algorithms (e.g. convolutional neural networks) into vectors.
Similarity checks: The system may have “legitimate vectors” or baseline vectors, and check whether the user's body activity vector is sufficiently similar (using cosine similarity, Euclidean distance etc.) to what’s expected for that task.
Difficulty adjustment: The “target range” or patterns required for verification can be adjusted over time to maintain desired difficulty.
Ensuring authenticity: Checking that data is human‐generated, perhaps by rehashing pre‐image data or comparing received hash vs re‐computed, etc.
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Figures & System Design
Fig. 1: Shows the environment – task server, user device, sensor, communication network, cryptocurrency system.
Fig. 2: Decentralized network view (nodes / compute resources etc.)
Fig. 3: Flow of the method (task → sensing → generate data → verify → reward).
Fig. 4-5: Details of generating body activity data and verifying it.
Fig. 6: Example of blockchain and how blocks include the body activity hash, previous hash, transactions etc.
Fig. 7: Variant using vectors/embeddings for body activity data.
Fig. 8: Example computing system that could implement these components.
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Advantages Claimed
Lower energy consumption compared to traditional proof‐of‐work (since body activity is used rather than brute computational hashing).
Possibly faster mining or verification (depending on task / user) than computational mining.
Also, since users are doing a task anyway, it could harness “useful work” (like viewing content) rather than purely wasteful hashing.
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Potential Issues / Considerations (not explicit in claims, but implied)
While the patent describes this system, implementing it raises a number of challenges:
How to ensure authenticity of body activity data (not spoofed, manipulated, or synthesized).
Privacy concerns: body activity (brain waves etc.) is very sensitive data.
Sensor accuracy, calibration, security.
User consent, regulation, health / safety.
Scalability: how many users, how many tasks, how to manage vector comparisons or similarity computations at scale.
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Claims (what the patent is legally seeking to protect)
While I won’t list all claims in full, the key protected ideas include:
A cryptocurrency system that receives body activity data from a user’s device, verifies whether it satisfies conditions, and awards cryptocurrency accordingly.
A method involving: providing tasks; sensing body activity; generating data; verifying; awarding.
A device that includes sensors, processor(s), memory, configured to do this task / generate and send body activity data.

 
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[ESG]

The first example that comes to mind is World Coin

In exchange for your retina data, they pay you with some shitcoin


https(//coinmarketcap(.)com/currencies/worldcoin-org/
website>>  world(.)org
whitepaper> https(//whitepaper(.)world(.)org/

[ESG]

The second example is
apps that pay you with tokens according to what you walk.

in web searches, you find lots and lots of apps,

so I'll leave STEPN as an example, because that's what always appears in the first results.

To receive tokens using the app, you need to make a very detailed registration,
sending personal data and facial recognition.

Once installed, it also collects heart rate data, GPS data...

You get tokens for walking and running.   There are two tokens: GMT, GST.

 STEPN withpaper>  https(//whitepaper(.)stepn(.)com/

[ESG]

. These two examples mentioned above use analog measurements
 and not biosensors mentioned in the patent, but it works in a similar way assisting
in the building an basement for future use in biosensors
however, if you enter the patent you will see that there are new projects and related patents.

[BitcoinBarrel]

All in all you're just another brick in the wall 

[ESG]

All in all you're just another brick in the wall 

Among many good bricks, there are always those that are better, and these suffer even more when they are broken and put in pieces on top of the others that were placed there, and with the passage of time,
 more new bricks are burned to be piled up on this wall that does not stop growing.

[ESG]

.
_All works that are in any way related to this topic, I will post here, in order to preserve the work of this and or others.

-In the case of this author, I did not need it, but as I said that I would have asked him for permission, I did so, and he happily replied that I did.>

>
-And I shared the link to this thread with him so he was aware.

This paper, “Future Trends of Artificial Intelligence in Human Biofield,” explores how artificial intelligence (AI) can be integrated with the study of the human biofield—the electromagnetic energy field surrounding the body that reflects a person’s physical, mental, and emotional state. It explains that while traditional tools like ECG and EEG measure specific biological signals, the human biofield as a whole remains unmapped and poorly understood due to its subtle, dynamic nature. The authors propose that AI’s capabilities in image processing, pattern recognition, and machine learning could help visualize, decode, and interpret this field to reveal hidden information about health, emotions, and consciousness. They outline potential applications ranging from medical diagnostics, wearable IoT devices, and biometric security, to human–computer interaction and emotion analysis, suggesting that the fusion of AI and biofield science could open a new era of noninvasive diagnostics, personalized health monitoring, and even human–machine communication.

Below is a section-by-section detailed overview of the paper titled “Future Trends of Artificial Intelligence in Human Biofield” by Gunjan Chhabra, Ajay Prasad, and Venkatadri Marriboyina (International Journal of Innovative Technology and Exploring Engineering, Vol. 8, Issue 10, August 2019):

https://www.academia.edu/78180054/Future_Trends_of_Artificial_Intelligence_in_Human_Biofield

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Abstract

The paper introduces the human biofield—a subtle energy field surrounding living organisms that reflects their physiological and psychological state. Despite evidence of its clinical potential, it remains unmapped and lacks reliable measurement techniques. The authors propose using Artificial Intelligence (AI) to analyze, interpret, and visualize this biofield, aiming to integrate it into diagnostic and therapeutic systems within Complementary and Alternative Medicine (CAM).
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I. Introduction

The introduction describes the human body as a nonlinear, self-organizing system that continuously exchanges energy with its environment. This energy—called the biofield—emerges from biochemical and electromagnetic activity.
Biofield signals span a broad range of frequencies, forming part of the field known as bioelectromagnetism.
These signals carry bio-information about health and emotional states.
Despite their importance, the human biofield has not yet been fully mapped or modeled because of weak signals and technological limitations.
AI, with its pattern recognition and data modeling abilities, could enable biofield analysis, bridging biological and computational sciences for health insights.
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Literature Review

This section traces the historical evolution of biofield research:
From ancient Vedic practices that used aura observation for health assessment, to Newton (1660) and Stephen Hales (1733) linking dynamic life energy to electricity.
Willem Einthoven’s ECG (1924) and Robert Becker’s studies on bioelectricity marked key milestones.
Kirlian photography (1939) visualized the “aura” through high-voltage photography, leading to modern Gas Discharge Visualization (GDV) and Resonant Field Imaging (RFI) technologies.
Researchers like Korotkov and others developed software for biofield imaging and pixel-based aura interpretation.
AI is proposed as the next step, offering tools to process complex, dynamic biofield patterns and extract meaningful data for psychological and physiological analysis.
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Human Biofield and Artificial Intelligence: Future Trends and Applications
The authors discuss several emerging applications at the intersection of AI and biofield analysis:

1. Clinical Applications

AI could process biofield data to:
Monitor mental health, emotional stress, and physical well-being.
Generate daily health reports via wearable biofield analyzers.
Recommend diet, exercise, and therapy adjustments autonomously.
By combining biofield data with Big Data and machine learning, the system could predict illness or mood states.
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2. IoT and Wearable Devices

Integrating biofield sensors into IoT and wearables could revolutionize telemedicine:
Devices could capture electromagnetic emissions from the body, analyze them with AI, and transmit health data to doctors in real time.
This would enhance early disease detection (e.g., cancer, stress disorders) and increase accuracy beyond current skin-sensor wearables.
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3. Aura as a Biometric Signature

The biofield could act as a unique human signature—an advanced biometric trait:
AI could distinguish dynamic aura patterns for authentication and identity verification.
This method may reduce biometric spoofing and enhance security systems.
The dynamic nature of the aura (changing with emotions and environment) introduces a new research field: Aura Dynamics.
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4. Social Applications

The biofield reflects emotional and psychological states:
AI-based aura interpretation could reveal emotions (e.g., red aura = anger) and detect criminal tendencies or mental instability.
Wearable headbands could monitor consciousness levels—detecting drunkenness or fatigue and preventing accidents.
Biofield “interference patterns” between people might indicate social compatibility, suggesting potential for AI-driven relationship analysis.
By 2030, biofields might even help differentiate humans from humanoid robots, since human biofields are biologically generated while robots emit artificial EM signals.
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5. Human-Computer Interaction (HCI)

If AI learns to decode biofields, direct communication between humans and AI systems could become possible—bypassing verbal or gesture inputs.
Real-time biofield-based HCI could allow AI bots to read emotional states and respond dynamically.
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6. Emotion Dynamics

AI could use biofield data to differentiate natural emotions (human) from artificial emotions (AI or robots).
A proposed “Dynamic Field Emotion Detector” would classify emotional authenticity using machine learning on biofield data.
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7. Other Applications

The paper envisions applications in:
Computer vision, interpreting aura colors to give lifestyle advice.
Performance enhancement, through continuous biofield monitoring for stress and productivity optimization.
Healthcare, sports, education, astrology, and self-development—where biofield data serves as a behavioral and physiological biomarker.
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II. Proposed Framework

A detailed algorithmic framework is proposed for visualizing and analyzing human biofields using AI and image processing.
Steps:
1. Capture an image of the person.
2. Preprocess to remove noise.
3. Enhance and normalize the image.
4. Convert it to grayscale.
5. Use machine learning to detect chakras.
6. Define a new color space for aura visualization.
7. Map this space to RGB values via learning models.
8. Apply linear regression to correlate aura colors with physiological and psychological states.
This framework forms a low-cost model for aura visualization, bridging ancient “aura reading” with AI-based imaging systems.
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III. Results and Discussion

Experiments using this framework successfully generated biofield color maps representing an individual’s aura.
Each color correlates with mental and physical conditions.
The resulting visualization can support medical diagnosis and personal health monitoring.
Future improvements could yield real-time emotional and health tracking systems.
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IV. Conclusion and Future Work

The study concludes that:
The biofield bridges the gap between health and consciousness, and disturbances may contribute to illness beyond chemical causes.
AI can decode this complex field, leading to breakthroughs in emotion tracking, stress analysis, identity verification, and non-invasive health diagnostics.
However, major challenges remain: lack of precise measurement instruments, biological modeling difficulties, and limited interdisciplinary collaboration.
The authors call for further research integrating AI, physics, biology, and neuroscience to make biofield technology viable.
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References

The paper cites over 30 sources ranging from classic biofield research (Rubik, Becker, Korotkov) to AI, bioinformatics, and color psychology studies, emphasizing the multidisciplinary nature of this work.
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In Summary

This paper envisions a fusion of AI, bioelectromagnetism, and human energy research to create systems capable of reading, interpreting, and even interacting with the human biofield. It connects ancient energetic concepts with modern AI and proposes a computational model for mapping the invisible aura as a diagnostic and social tool—heralding what the authors call a “revolution in medical examination and human-computer interaction.”

[ESG]

 This paper presents a wireless power and communication chip for implanted sensors, designed in CMOS and powered by inductive RF coupling instead of batteries or wires. Operating at 4 MHz, the system delivers up to 2 mA at 3.3 V to implants and can transmit data back by simply modulating coil impedance. Tests showed it works reliably across 28 mm distances and remains stable even when water-based materials mimic human tissue. In short, it demonstrates a breakthrough step toward fully wireless, battery-free implantable devices for medical monitoring and research.
Below is  a section-by-section detailed overview of the paper *“Power Harvesting and Telemetry in CMOS for Implanted Devices”*:
https://isn.ucsd.edu/pub/papers/biocas04_tele.pdf
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Abstract

The paper introduces a CMOS-based chip that enables wireless powering and communication for implanted sensors. Using inductive coupling, the chip delivers up to 2 mA at 3.3 V without the need for batteries or wires. Tests showed it works at coil distances up to 28 mm, and performance remains stable even when water-based materials (to mimic body tissue) are placed between coils.
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1. Introduction

Problem: Implanted microdevices (e.g., neural or chemical electrodes) often require wires through the skin for power and data, limiting use in long-term or free-moving studies.
Alternative energy sources (solar, vibration, etc.) are unsuitable for implants.
Solution proposed: RF power harvesting with inductive coupling, similar to RFID tags, which allows both power delivery and data telemetry.
Design: A CMOS chip operating at 4 MHz (since RF energy in the 1–10 MHz range penetrates the body with minimal loss).
Functionality: Provides regulated power, clocking, reference voltages, and data link to sensors.
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2. System Architecture

The chip comprises five main sub-modules:
1. Rectifier
Full-wave rectifier using PMOS transistors.
Converts coil’s AC into DC voltage.
Requires at least ~7 V AC on the coil to produce 3.3 V regulated output.
Protected from overvoltage by optional off-chip Zener diode.
2. Regulator
Provides a stable 3.3 V supply (up to 2 mA).
Uses transconductance amplifier with feedback stabilization.
Needs ~100 µA quiescent current.
3. Voltage Reference
Generates an 800 mV reference voltage independent of supply.
Since implant temperature is constant, no need for bandgap references.
Implemented with CMOS devices and startup circuit.
4. Clock Recovery
Extracts a 4 MHz clock from the incoming RF waveform.
Provides additional divided clocks (e.g., 1 MHz) for sensor needs.

5. Data Encoding & Modulation

Accepts sensor data in NRZ format.
Encoded with modified Miller coding (pulse per logical “1”).
Data transmission by coil impedance modulation using a resistor switched by NMOS.
Requires very little extra power.
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3. Measurement Results

Fabrication: Chip built in 0.5 µm CMOS via MOSIS.
Testing setup: Class-E transmitter driving a 5 cm coil; receiver coil 2 cm diameter.
3.1 Air Coupling Tests
Distance tested: 10 mm → 100 mm.
With load set to draw 0.7 mA, the chip operated reliably up to 28 mm separation.
3.2 Load Regulation
Maximum source current depends on coil distance.
Voltage drop behavior is consistent across distances until regulator cutoff.
3.3 Coupling & Interference
Biological tissue effects tested using water-bearing colloids.
Results: Slight efficiency loss, but chip functionality unaffected.
Demonstrates robustness in tissue-like environments.
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4. Discussion

Dual regulators (digital + analog) provided slightly higher than intended output (3.4 V & 3.5 V vs. 3.3 V). This mismatch attributed to transistor sizing.
Future improvements: better transistor design and optimization of coil size.
Ongoing work includes FEM (finite element method) modeling of tissue interference for improved coil performance.
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5. Acknowledgments

Supported by NIH grant MH62444. Fabrication by MOSIS foundry.
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6. References

Cites key prior work on:
Neural implants,
Wireless EEG and neural recording systems,
Smart Dust energy harvesting,
RF powering for implants,
RF behavior in biological tissue, and
CMOS circuit design methods.
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✅ In summary: This paper presents one of the early CMOS RF power harvesting and telemetry systems for implanted medical devices, demonstrating reliable wireless powering and bidirectional communication at clinically relevant distances, even under tissue-like conditions.

[ESG]

~
Abstract

The paper introduces a CMOS-based chip that enables wireless powering and communication for implanted sensors. Using inductive coupling, the chip delivers up to 2 mA at 3.3 V without the need for batteries or wires. Tests showed it works at coil distances up to 28 mm, and performance remains stable even when water-based materials (to mimic body tissue) are placed between coils.
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~

 It seems to be a very fictitious thing, but no, this is not new, I had a friend, who about five years ago, he had surgery implanting a device in the brain and ear to hear again, he explained to me, that they cut the nerve in both ears, because one he extorted more or less and the other a lot of noise, and then they connected the receptor to this nerve internally, and on the outside, in the ear goes a device that, It captures the sound waves and transmits it already decoded to the sensor inside the head. This device on the outside has a battery, but the one inside does not, and I asked him, and he said that, that it is connected to the nerve of which it already has an electrical conduction that will keep this receiver working without needing batteries....

[ESG]

YOUR SMARTWATCH IS ABLE TO MONITOR YOUR BRAINWAVES, AS WELL AS THE BRAINWAVES OF THE PERSON NEXT TO YOU

 This patent describes a wearable device that can read brain activity from two people at the same time—one through normal contact sensors touching the wearer’s skin, and the other through non-contact sensors that can pick up brain-wave signals from someone nearby without touching them at all. The device then analyzes those signals to figure out things like attention, stress, alertness, emotion, or other mental states, and can send feedback to the wearer or another device. In simple terms, it’s a dual-person brain-monitoring system that lets you understand someone else’s mental state from a distance and also track your own, all through a single wearable... looking at the images provided within the patent, you can clearly see that the wearable shown is a smartwatch. LAYMANS terms will be located at the end of the patent breakdown.

 Provided below is a section-by‐section breakdown of the patent US20180008145A1 – “Dual EEG non-contact monitor with personal EEG monitor for concurrent brain monitoring and communication”.
https://patents.google.com/patent/US20180008145A1/en

1. Title & Basic Info

 Title: Dual EEG non-contact monitor with personal EEG monitor for concurrent brain monitoring and communication.
Inventors: Peter Anthony Freer, Gwen Kathryn Freer.
Assignee: Freer Logic Inc.
Filing date: June 23, 2017.
Publication date: January 11, 2018.
Priority date: July 5, 2016.
Status: Granted (linked later to US10694946B2) as of June 30, 2020.
Simple summary of what the patent concerns:
A device (or system) that can monitor brain-electrical activity (EEG) from a person wearing it, and simultaneously monitor EEG from another nearby person without physical contact (non‐contact). Further, it can transmit or communicate states (like cognitive/alertness/emotion) inferred from those EEG signals.

2. Field & Background

 What field/sub-field: Monitoring brain electrical activity (EEG) for physiological or cognitive states; specifically non-contact EEG (i.e., the sensor doesn’t have to touch the head/body) and contact EEG combined.
Background/Problem addressed:
Conventional EEG often requires electrodes on the scalp/hair, which can be intrusive, uncomfortable, limit movement, or require special preparations.
There is interest in monitoring cognitive/psychophysiological states (attention, stress, alertness, drowsiness, identity) in more natural settings than labs.
The challenge: How to get EEG‐type signals without direct contact, how to filter out unwanted signals (noise, heart rate, movement artifacts), and how to do simultaneous monitoring of two people in one device.
Why this invention:
It proposes an apparatus that combines a “contact” EEG circuit (worn by person1) and a “non-contact” directional EEG circuit (aimed at person2, without touching) in one unit. Hence “dual EEG” (two persons concurrently) + communication/feedback of inferred states.

3. Summary of the Invention (Abstract / Summary)

 In simple terms:
A wearable device for person A that has sensors in contact with person A to pick up EEG.
Simultaneously, that same device (or unit) has a set of non-contact directional sensors aimed at person B (or someone else nearby) that pick up EEG signals without touching person B.
A processor analyses both sets of signals.
It determines “states” of the persons (e.g., attention, stress, cognitive load, identity).
It may feedback or communicate these states to person A (or to other devices) via wireless communication, haptics, visual/aural display.
Also the device supports transmission of signals from person B’s brain to person A (or between persons) via some stimulation/feedback means (e.g., electromagnetic coils, peripheral nervous system stimulation) for “thought transfer” or brain-to-brain interface.
Key points to watch:
The “non-contact” feature: sensors that do not touch the skin/hair of person2.
Dual monitoring: one contact, one non-contact, simultaneously.
The communication interface: not just read EEG but infer meaningful “state” (attention, emotion etc) and transmit/feedback that.
Application scenarios: from simple monitoring (alertness, attention) to more exotic as “mind-reading” or brain-to-brain transfer.

4. Detailed Description (Major Embodiments)

 The patent gives several embodiments; I’ll summarise major ones in plain language:

4.1 Apparatus Configuration (Figures referenced)

The invention describes a device (call it apparatus 20) which houses:
A contact EEG circuit (24) with electrodes that do touch/sit on person A’s skin (below the head).
A non-contact EEG directional circuit (25) with electrodes/sensors (27,29) aimed at person B (or another person) without skin contact.
Wireless transmitter/receiver system to send signals.
Processor/Microcontroller to perform analysis, filtering, communication.

4.2 Signal Processing & Filtering

For the contact EEG: typical analog front end: amplifier → low-pass filter → ADC → transmitter. Example given: low pass cut-off ~22 Hz to focus on relevant brain wave bands (below 50 Hz).
For non-contact EEG: similar amplifier chain, but with sensors placed a distance away. Raw signals include EEG components plus unwanted signals (heart, environment, power-line noise). Therefore filtering, adaptive DSP, active cancellation of unwanted components (e.g., heart rate) are described.
Example: isolating theta waves (4-8 Hz) and beta waves (12-16 Hz) to infer attention‐levels:
A decrease in theta + an increase in beta may indicate higher attention.
The system may detect presence of second person (via heart rate component) to know sensors are pointed correctly.

4.3 Dual Person Monitoring + Communication

The device allows simultaneous monitoring of person A (wearer) and person B (non‐contact target).
Person A may get feedback about person B’s state (like “is paying attention”, “is stressed”, “likes me”, etc) via visual/haptic/aural cues.
In further embodiments: the system can transmit “thoughts” (or inferred cognitive signals) from person B via wireless to person A, and then send stimulation (electro-magnetic coil/peripheral nerve stim) to person A’s body to convey that information. This is touted as a foundation for brain-to-brain interface (“Mind reader” tech) in speculative sense.

4.4 Example Use‐Cases

Wearable for day‐to‐day cognitive monitoring: attention, drowsiness, stress, cognitive load, gaming, neurofeedback.
Social scenarios: Person A wearing device can discreetly monitor person B in proximity (e.g., on a date) to infer “is person B interested / paying attention”.
Security/surveillance: At airport, etc, monitoring mental/cognitive state of individual without physical contact.

5. Claims

(Only summarising key claims in plain language — For full legal text consult the patent.)
1. A device comprising:
a non-contact EEG directional circuit to detect EEG signals from a first person without contacting the skin;
a contact EEG circuit to detect EEG signals from a second person by skin contact;
a processor that analyzes both signals to determine a “state” of each person;
a feedback / communication interface that gives an indication of that state to at least one person.
2. The device where the “state” includes emotional, cognitive, alertness, attention, stress, etc.
3. The device where the non‐contact sensor may be directed at body or head of second person, from a distance, without touching skin/hair/clothing.
4. Methods: concurrently monitoring EEG of two persons using one device, with one contact sensor, one non‐contact sensor, processing and filtering to isolate relevant brainwave bands (theta, beta, etc).
5. Additional claims about wireless communications, feedback means (haptics, visual, auditory), identity detection (matching EEG signatures to database), and transferring signals as “thoughts” via electro-magnetic stimulation to second person’s peripheral nervous system.
6. Advantages & Differentiation
What’s improved / new:
Non‐contact EEG sensor capability: no skin contact needed for one person in the monitoring pair.
Dual‐person simultaneous monitoring in one device rather than separate devices.
Integrated analysis of cognitive/psychophysiological states (attention, emotion, identity) rather than just raw EEG.
Feedback/communication element: turning those states into actionable signals (to wearer, to another device).
Potential for brain-to-brain communication (though more speculative) i.e., using EEG + stim to convey information between people.
Practical deployment: making EEG monitoring less intrusive, more mobile and socially acceptable (e.g., wearable, discreet).
7. Potential Applications
Wearable cognitive‐state monitors for attention, drowsiness, stress in work, driving, education.
Social/consumer use: wearable device that senses how another person is responding (interest, attention) — e.g., dating, social interactions.
Gaming/VR/AR: adapt experience based on player’s cognitive/emotional state.
Neurofeedback/brain training: monitor brainwave bands (theta, beta) for feedback loops.
Security/biometric: identity verification via EEG signature, remote brain‐state sensing in public environments.
Future brain communication interfaces: sharing “thoughts” or brain‐states between people via non-verbal/non‐vocal means.
8. Limitations / Considerations
Non‐contact EEG is technically challenging: signals are weak, many interfering sources (heart rate, motion, power-line noise). The patent discusses filtering and cancellation, but real‐world robustness may vary.
Privacy/ethical issues: monitoring someone’s brain state without contact poses significant consent/privacy concerns.
Brain‐to‐brain “thought transfer” remains speculative and may require further invasive or precisely controlled stimulation; practical usability may be limited.
Wearables must handle motion artifacts, ambient noise, variable distances/angles for non‐contact sensor. The claims assume “proximity” and “directed at” second person.
Regulatory/medical device implications if used in clinical or attention monitoring contexts.
9. Summary (LAYMANS)

 “Imagine a wearable smartwatch (or body sensor) that listens in on your brainwaves and simultaneously also picks up brainwave patterns from someone else standing nearby — even without touching them. It analyzes both sets of signals and tells you something like: ‘They’re paying attention’, ‘They’re stressed’, or even ‘They like you’. Further down the line, it dreams of letting one person transmit thoughts or brain-states directly to another. This patent describes the hardware and method to make that possible — combining contact sensors, non-contact sensors, signal filtering, wireless communication and feedback.”  HIVEMIND

From Crystal Stone:

 With Freer logic solutions, one can also drive autonomously while the vehicle reads your brainwaves, behaviors and emotional state...even brain to brain communication. Watch the first video on the website below.

From their website:

 "Welcome to a world where breakthrough neurotechnology can be used by anyone, at any time, and anywhere."

 This should be of concern. All is data for the machine. The Merging of Man and Machine: the BioCyber interface.

https://freerlogic.com/#solutions

[ESG]

...yes, yes, I always come here, and I see that the subject is about reward in cryptocurrencies using data from the human body, of course, practically from the first post, almost all of them are from 'Jay Wilson', an endless copy and paste and blahblabla and such.

 - But, as I said, I like his work of analyzing works related to the surveillance and control of the human being in various aspects, using diverse and recent technologies, and exactly for this reason, I make a point of copying and pasting here, his works related to the theme, because I don't know if he has profiles on other networks, But, on Facebook, I know that over time, this wonderful work of his will be lost, censored and or something.

 If only approximately 5% of the population these days are interested in Bitcoin, imagine the percentage of people who seek to know about published scientific works, of course, many have not yet been and or will never be published, but those that are published, a minimum part of the population analyzes them, and it is not easy, because for me, to be able to take a work already chewed and understood, and transcribing it here, using the tools of the forum, so that somehow this work is not lost, is a pleasure. even more related to the theme of control.

 So, what I will be copying and pasting here in this post, is a work that refers to what is being implemented in India, which has the same objective as always, control, totalitarianism and surveillance, and the worst, it serves as an example and can be used wherever this system wants to be adopted. Everything is tested first as an example, and then implemented on a large scale... We see that 'they have accelerated the tests in the control and surveillance part in recent years...

And then, linking the following work with the topic of the topic, we can say that, in the near future, if money will be only in electronic form to expand control, we can then say that, if you are not vaccinated, you will not be able to be attached to the country's payment/monetary system, and or, you will not be able to register in systems where you are rewarded for activities rewarded by electronic currencies... That is, in this case Bitcoin will be a way out, however, within a surveillance state, this will have to be done/used in a very private way, practically the service of a secret agent with a wallet and provided for security, knowing the person with whom he will relate to then receiving and or making payments with Bitcoin and or private currency, Like Monero or Zcash for example. and or example of an output from the shadow of the control.

Advanced Face Recognition based Non-Vaccination Population Finder and Alert System

 This paper proposes an 'Aadhaar-based facial recognition system' designed to automatically identify non-vaccinated individuals during the COVID-19 pandemic by matching a person’s face against India’s Aadhaar database and retrieving their linked vaccination status. Using deep learning, specifically Convolutional Neural Networks (CNNs), the system detects and analyzes facial features, verifies identity, and determines whether a person has received one or more COVID-19 vaccine doses. The authors present the system as a solution to vaccine hesitancy, counterfeit vaccination certificates, and the need for reliable verification, arguing that facial recognition can make vaccination authentication contactless, real-time, and scalable. The paper details how the facial recognition pipeline works—from image capture to feature extraction, classification, and comparison—and concludes that their approach achieves high accuracy and could even be expanded to other national services in the future.

 Provided below is a  section-by-section breakdown of the PDF “Advanced Face Recognition based Non-Vaccination Population Finder and Alert System”.
https://journalppw.com/index.php/jpsp/article/view/4593

 Abstract:

 The paper proposes a system that uses Aadhaar-based facial recognition to identify non-vaccinated citizens and alert them using AI. It uses Convolutional Neural Networks (CNNs) for face recognition to determine a person’s vaccination status. The system aims to authenticate identity through facial recognition and verify whether someone has received a COVID-19 vaccine.

1. INTRODUCTION

A. Overview of Vaccines

 Explains how vaccines work (active vs. passive immunity), how they stimulate B-cells, and mentions routes of administration (injection, oral, nasal).

B. COVID-19 Background

 Summarizes the emergence of SARS-CoV-2, the global pandemic, how vaccines train the immune system, and lists WHO-approved vaccines as of 2021 (AstraZeneca, Pfizer, Moderna, Covaxin, etc.). Also reiterates preventive measures (distance, masks, hygiene).

C. COVID-19 Vaccination in India

 India’s vaccines mostly require two doses. Hesitancy increased due to myths, particularly in rural areas, after people became infected between doses. Mentions counterfeit vaccination certificates and the need to reliably verify vaccination status. The authors propose using Aadhaar-based facial recognition to identify unvaccinated individuals.

D. Problem Identified

 Vaccine hesitancy, misinformation, and counterfeit vaccine certificates cause difficulties in public-health verification. Facial recognition is proposed as a method to reliably identify people and determine their vaccination status.

E. Image & Video Classification / Segmentation
Reviews how CNNs outperform humans in image classification tasks and explains their usefulness in facial recognition.

F. Project Scope

 India is adopting Aadhaar-based facial recognition for vaccination verification. The system aims to make vaccination procedures contactless but notes concerns: misidentification, exclusion of vulnerable groups, and normalization of surveillance.

G. Project Objective

 To replace fingerprint/iris biometric scanners with Aadhaar-based facial recognition at vaccination sites to detect and avoid non-vaccination.

II. METHODOLOGY

 Summarizes several research studies on face-recognition algorithms, challenges (especially with darker skin tones), and improved computational models. The authors describe:

A hybrid algorithm (Gaussian + Explicit Rule) improving recognition accuracy for dark-skinned individuals.
CNN-based multimodal emotion recognition.
Deepfake detection using hybrid forensic frameworks.
Attendance management systems using LDA.
Heterogeneous face recognition (HFR) using domain-invariant features.
Faster R-CNN improvements.
New multi-scale face detectors (YOMO).
Use of cascaded CNNs for more robust, real-time detection.
Super-resolution techniques for low-resolution facial images.
All studies highlight advances in CNN-based recognition that justify the model used in this project.

III. SYSTEM ANALYSIS

 The system retrieves a person’s COVID-19 vaccination status after facial recognition. The core model is a Deep Convolutional Neural Network (DCNN).

A. Convolutional Layer

 Extracts features using sliding kernels to create feature maps.

B. Pooling Layer

 Reduces dimensionality to avoid overfitting and retain essential information.

C. ReLU Activation

Converts negative values to zero to improve training efficiency.

D. Fully Connected Layer

Classifies features into output categories using Softmax.

E. Advantages

Stores detected faces
Marks vaccination status (Dose 1, etc.)
Multiple face detection
Real-time video analysis
Generates vaccination certificates with photo + QR code

IV. SYSTEM IMPLEMENTATION

A. COVID-19 Vaccination Finder Web App

Uses India’s CoWIN system. Aadhaar-based facial recognition is used for beneficiary verification for vaccination. Facial images are sent to UIDAI for identity matching.

B. Face Recognition Module

Steps:

1. Face Enrollment
2. Image Acquisition (webcam or ATM camera)
3. Frame extraction (20–30 frames/sec)
4. Preprocessing: grayscale, resizing, noise removal
5. Binarization
6. Face Detection using Region Proposal Network (RPN)
7. Face Segmentation using Region Growing method
Explains how RPN anchor boxes receive labels (1 = face, -1 = no face).

C. Feature Extraction Module

 Extracts numerous facial measurements (forehead height, eye distance, jaw shape, slopes, nose width, lip sizes, etc.).
Also lists three categories of features:
Intensity (statistical features)
Shape features
Texture features (GLCM, GLRLM, GLSZM, etc.)

D. Face Classification (DCNN)

 CNN layers (convolution, ReLU, normalization, pooling) produce feature maps. Dropout reduces overfitting. Softmax is used for classification into identity categories.

E. Face Identification

 Feature vectors from the detected face are compared against the Aadhaar-linked face database.
If matched → system retrieves COVID-19 vaccination status.
If unmatched → classified as unknown.

F. Prediction

 Uses Hamming Distance to compare features and display match accuracy.

G. Non-Vaccination Finder

 Identifies non-vaccinated individuals by comparing facial data to Aadhaar-linked records and checking vaccination status.

H. Notification

 If a match indicates non-vaccination, alerts are sent to responsible authorities or relevant regions.

I. Performance Analysis
Defines TP, FP, FN, TN for evaluating accuracy. The model achieved 99.84% accuracy in face detection/recognition.

V. RESULTS AND DISCUSSION

 Compares DCNN to traditional machine-learning techniques:
SVM
LDA
PCA
MLP
CRBF
DRBM
DBNN
DCNN outperforms all others in recognition accuracy.

VI. CONCLUSION
 
 The system can match faces to a database to verify someone's vaccination status through Aadhaar-linked facial recognition. It works by extracting and comparing facial features using DCNN. The classifier achieved higher performance than other state-of-the-art models. It also states this could be used as proof of vaccination in healthcare or other contexts.

VII. FUTURE ENHANCEMENT
 
Future goals:

 Improve resistance to spoofing and fake faces

 Apply the technology to voting systems in India

[ESG]

 First, mobile devices came as a novelty, you could communicate with people anywhere as long as you had enough batteries and had a transmitter tower nearby.
 Unlike its mobile predecessor, which I think would be the HTs, where these did not identify you, you just transmitted information from one to another, sometimes with repeater towers, PY, PX, the cellular devices, came with identification from which you can reach the owner. And these days, in addition to identifying you, it, the device, identifies your location, and a lot, a lot of information about the userhabits, routes, bank details, friendship circles, and recently, a few years, biosensors,...
 So, first it comes as an option, then it becomes an obligation, and nowadays, it has become mandatory to live in a society commanded by a corrupt force, in which it has the objective of total control of each citizen.
 I, personally, avoid as much as possible to carry a cell phone with me, unless it is very necessary, the cell phone these days, has turned into an iron ball chained to our legs, and I don't feel that way, so even a watch I don't use, but I know that I am within the field of the Broadcast network... And those who are not within this field today are a race, since the Broadcast network extends around the entire globe, and this is the main key to its goal of control.

 -so as always here, one more copy and paste from Mr. Jay Wilson.

This paper is a review of how Wireless Body Area Networks (WBANs) are being implemented using Android smartphones, showing how smartphones have become central hubs for monitoring the human body through wearable/implantable sensors. It explains that WBANs consist of low-power sensors placed on, worn around, or attached to the body to measure vital signs, activities, or environmental data, which are then wirelessly transmitted—most commonly via Bluetooth or BLE—to Android phones for processing and further communication. By surveying prior research, the authors show that most Android-based WBAN systems focus on medical and healthcare applications, such as ECG monitoring, blood pressure measurement, epidemic control, fall detection, and Parkinson’s tremor analysis, while a smaller portion targets non-medical uses like activity recognition, step counting, and pedestrian navigation. Overall, the paper concludes that Android’s open-source ecosystem, built-in sensors, and wireless capabilities make smartphones a powerful, flexible, and cost-effective platform for WBAN development, capable of acting as sensor nodes, data processors, gateways, and alert systems for both healthcare and everyday applications .

Below is a detailed, section-by-section overview of the PDF
“The Emerging Wireless Body Area Network on Android Smartphones: A Review”


What this paper is:

 A literature review paper published in IOP Conference Series: Materials Science and Engineering (AASEC 2017), reviewing how Wireless Body Area Networks (WBANs) are being implemented using Android smartphones.

Main goal:

 To summarize existing WBAN research that uses Android phones, focusing on:

Purpose of the system (medical vs non-medical)
Types of sensors used
Android devices involved (smartphones, smartwatches)
Connectivity methods (Bluetooth, BLE, Wi-Fi)

Abstract

 The abstract states that society is entering an era where human bodies can be digitally monitored. WBANs consist of sensors worn on, attached to, or implanted in the body to monitor health and activity.

The paper reviews WBAN research specifically using Android smartphones, analyzing:

Device purpose/Sensor types/Android hardware/Connectivity methods.

Key takeaway:

Most studies focus on healthcare monitoring, but Android smartphones are shown to be powerful WBAN platforms, capable of processing sensor data and acting as gateways or even sensor nodes themselves .

1. Introduction

This section explains what WBANs are and why they matter.

Key points:

WBANs are collections of low-power sensors (nodes) attached to or placed in the human body.
Sensors monitor vital signs, activities, or environmental parameters.
Data is processed and transmitted wirelessly for further analysis.
WBANs are a specialized form of wireless sensor networks (WSNs).
Standards mentioned:
IEEE 802.15.4
IEEE 802.15.6
Bluetooth Low Energy (BLE)
These standards emphasize:
Low power consumption
Low cost
Low data rates
Safe operation on or in the human body
Role of smartphones:
Smartphones act as gateways or sinks for WBAN nodes.
Many smartphones already include sensors (accelerometer, gyroscope, heart-rate sensor).
Android’s open-source nature makes it ideal for WBAN development.
Connectivity options include Bluetooth (most common) and Wi-Fi when higher data rates or longer range are needed.

The section concludes by stating the paper’s intent: to categorize and analyze Android-based WBAN research to guide future researchers .

2. Android-Based WBAN for Medical Purposes

This is the largest and most important section, showing that medical monitoring dominates WBAN research.

Main medical applications reviewed:

Epidemic control
Uses vital signs (heart rate, temperature) and social interaction data
Predicts and tracks disease spread using smartphone-based WBANs
Blood pressure monitoring
Uses pressure sensors and Bluetooth
Android phone receives and displays data
Accuracy compared against medical-grade devices (>97%)
E-health platforms
Integration of smartphones, smartwatches, and tablets
Measures heart rate, breathing rate, and body temperature
Provides workout or health recommendations
Emergency monitoring systems
ECG, heart rate, temperature sensors
Alerts via SMS, email, buzzer during critical conditions
Antenna design for WBAN
Focus on low-cost antennas integrated with ECG sensors
Designed specifically for reliable on-body communication
ECG monitoring for cardiac conditions
Continuous ECG monitoring
Alerts sent to doctors and hospitals
Non-contact wearable health devices
ECG, temperature, accelerometer, BLE
Fall detection and emergency alerts
Parkinson’s disease monitoring
Uses accelerometer and gyroscope data from smartwatches
Android phone acts as central data collector
Quantifies tremors to identify disease stage

Key takeaway:

Android smartphones are used as:

Data collectors/Data processors/Communication hubs/Alert systems for emergencies.

3. Non-Medical Android-Based WBAN

This section shows WBAN use beyond healthcare, focused on daily life and activity tracking.

Non-medical applications:

Step counting
Uses smartphone accelerometers
Designed to work regardless of how the phone is carried
Activity recognition
Uses smartwatch accelerometer and gyroscope
Continuous authentication for security
Pedestrian navigation
Uses accelerometer, gyroscope, magnetometer, and barometric pressure
Enables accurate 3D indoor/outdoor navigation

Key point:

Android smartphones and wearables can accurately interpret human motion and behavior, making them suitable for lifestyle, security, and navigation applications .

4. Results and Discussion

This section synthesizes the reviewed studies.

Main findings:

WBAN research falls into two categories:
Medical
Non-medical
Android is capable of:
Handling WBAN network algorithms
Managing communication between sensors, users, and servers
Smartphones serve as:
Gateways
Sensor nodes
Data processing units

Table 1:

Summarizes all reviewed studies by:

Year
Author
Category
Purpose
Sensor types
Devices used (smartphone, smartwatch)
This table clearly shows the dominance of medical monitoring in Android-based WBAN research .

5. Conclusion

The conclusion states that:

WBANs represent a new paradigm in healthcare and lifestyle monitoring.
Android’s popularity and open-source nature accelerate WBAN development.

Android smartphones are validated as:

Reliable WBAN platforms
Alternatives to dedicated medical gateways
Future WBAN systems can increasingly rely on smartphones for real-time, mobile, body-centric sensing .
Overall Takeaway
This paper shows that Android smartphones are central to modern WBAN systems, especially in healthcare. They collect data from on-body sensors, process it locally, send it wirelessly, and trigger alerts when needed. Most research focuses on medical monitoring (ECG, blood pressure, temperature), but non-medical uses like activity tracking and navigation are growing. Android is portrayed as a key enabler of wearable and body-centric digital health systems.

[ESG]

 ·
 This paper looks at how tiny fluorescent sensors made from single-walled carbon nanotubes behave when they are placed inside a living body.

 The researchers focus on sensors that are encapsulated in soft hydrogel materials and can be injected or implanted under the skin, rather than being bulky devices.

 Using mouse experiments, they study how the body reacts to these injected, in-vivo nanosensors over time—specifically looking at inflammation, immune response, and scar-like tissue formation around the sensors.

 The key takeaway is that the way the hydrogel is designed (how tightly it’s cross-linked and how porous it is) strongly affects how much inflammation occurs and how long the nanotube-based fluorescent sensors continue to work.

 Overall, the paper shows that injectable single-walled nanotube sensors can function inside the body, but their long-term performance depends heavily on how well they are engineered to minimize the body’s natural foreign-body response.
 
 Important!

 All formulations eventually developed fibrous capsules — a hallmark of foreign-body response.

 Provided below is a section-by-section overview of the paper:
 
 In-Vivo fluorescent nanosensor implants based on hydrogel-encapsulation: investigating the inflammation and the foreign-body response
 
Published: Journal of Nanobiotechnology, April 24, 2023.
https://link.springer.com/article/10.1186/s12951-023-01873-8

1. Abstract

Key points:

Nanosensors (tiny fluorescent sensors) show promise for in-vivo biosensing, imaging, and monitoring biological signals.
 However, tissue responses (inflammation, foreign-body reaction) are critical in determining whether implanted nanosensors function and how long they last.
 
 The study implants five formulations of nanosensors encapsulated in PEGDA (polyethylene glycol diacrylate) hydrogels into mice to explore how formulation affects inflammation and sensor performance.
 
 Major observation: Higher crosslinking density in hydrogels → faster resolution of acute inflammation, and tissue response impacts sensor lifetime.

 Why it matters: This sets up the problem — nanomaterials are useful, but interactions with biology are complex and can undermine performance.

2. Introduction

 Nanosensors are increasingly used for in vivo applications (signaling pathways, analyte detection, continuous monitoring).
 
 They can be delivered as liquids or solid implants with hydrogels.
 Hydrogels are thought to improve compatibility with tissues, but how hydrogel formulation affects tissue response hasn’t been well studied.

 The paper uses single-walled carbon nanotube (SWNT) sensors encapsulated in hydrogels as a model system to systematically examine this.

 Framing the question: What design rules minimize inflammation and extend functional lifetime of implanted nanosensors?
 

3. Methods and Materials

Subsections:
 
a. Materials

Describes the SWNT sources, hydrogel components (PEGDA of two different molecular weights), initiators, and solvents used.

b. Hydrogel Synthesis & Characterization

SWNTs were wrapped with specific polymers to recognize target analytes and then encapsulated in PEGDA hydrogels via UV cross-linking.
Differences in crosslink density, pore size, and mechanical properties were measured.

c. Mouse Implantation and Tissue Collection

Hydrogels were implanted subcutaneously in mice (immunocompetent and various immunocompromised lines).

At designated times (1, 7, 14, 28 days), tissue was collected for histological analysis.

d. Degradation Product Analysis

Explains how hydrogel degradation products were monitored (Raman, FTIR, NMR, chromatography).

e. Statistical Analysis

Standard measures and significance assessments are described.
Why this matters: Establishes a rigorous and reproducible experimental platform linking nano-formulation to host biology.

4. Results and Discussion

This is the core of the paper — linking formulation properties to biological outcomes.

a. Hydrogel & Sensor Characterization

Describes the spectral properties of two types of SWNT sensors (responsive to progesterone and riboflavin).

Presents pore sizes and mechanical stiffness of hydrogel formulations (smaller pores → stiffer gels).

b. In Vivo Tissue Response

Hydrogels were explanted at different time points; histological scores were quantified:
Acute inflammation

Fibrosis (capsule formation)
Edema (swelling)
Neovascularization

Observed that more highly cross-linked hydrogels (smaller pores) tended to show faster resolution of inflammation.

In early time points, tissue responses varied somewhat with SWNT wrapping — suggesting both hydrogel structure and wrapping chemistry affect inflammation.

c. Fibrous Capsule Formation

All formulations eventually developed fibrous capsules — a hallmark of foreign-body response.
The organization of the capsule varied with formulation, indicating healing speed differs with composition.

d. Functional Impact on Sensor Performance

Explanted hydrogels were challenged with analyte in vitro after various implantation durations.
Results show sensor sensitivity declined and response slowed over time, consistent with inflammatory processes interfering with detection.
Different mouse strains did not show clear trends, but all had functional neutrophils indicating acute inflammation affects sensor function.

5. Conclusions

Main findings:

Tissue responses are strongly formulation-dependent.

Higher hydrogel crosslinking density generally led to faster inflammation resolution.
Hydrogel design not only affects biocompatibility but also how long the sensors stay functional.

Implications:

Design strategies should balance encapsulation efficiency, pore size, and sensor accessibility against inflammatory potential.

Tissue response must be factored into in vivo nanosensor design — not just sensor chemistry and optics.

6. Figures & Tables (Key Extras)

The paper includes:

Images of sensor fluorescence spectra.
Hydrogel property graphs (modulus, pore size).
Representative histology over time.
Quantitative tissue response scores.
These support the narrative linking material design to biological outcomes.

7. Supplementary Information

There is supplementary data available online (not covered here) that includes additional raw histology, extended tables, or detailed protocols.

[ESG]

 This paper is a comprehensive review of how nanosensors
—ultra-small sensors built from nanomaterials—
are transforming healthcare by enabling real-time, highly sensitive monitoring and disease management.
 It explains what nanosensors are, how they work, and why materials like graphene, carbon nanotubes (including single-walled carbon nanotubes), quantum dots, metal nanoparticles, and nanowires make it possible to detect extremely small biological signals that traditional sensors often miss.
 The paper walks through the main types of nanosensors (optical, electrochemical, biological/biosensors, magnetic, and mechanical) and shows how they are being used in early cancer detection, cardiovascular monitoring, tuberculosis diagnosis, glucose monitoring in diabetes, therapeutic drug monitoring, plant pathogen detection, and nanomedicine-based drug delivery.
 It also describes how these sensors are integrated into point-of-care devices, wearables, implantable systems, wireless networks, IoT platforms, and AI-driven digital health ecosystems for continuous monitoring.

 Finally, the paper discusses fabrication methods, miniaturization, power and data transmission, market trends, and openly addresses remaining challenges such as biocompatibility, long-term stability, regulation, data privacy, scalability, and ethics, concluding that nanosensors are poised to play a central role in future personalized and real-time healthcare systems.

 Provided below is a section by section overview of the paper “Nanosensors in healthcare:
 transforming real-time monitoring and disease management with cutting-edge nanotechnology”

Journal: RSC Pharmaceutics (2025)

Type: Comprehensive review article

1. Introduction

 This section establishes why nanosensors matter in modern healthcare. Nanosensors are devices that detect physical, chemical, or biological changes at the nanoscale
Their development accelerated in the early 2000s due to advances in nanomaterials and materials science Compared to traditional sensors, nanosensors offer:

Extremely high sensitivity
Real-time monitoring
Ability to detect very small biological changes early

Healthcare uses highlighted:

Early disease detection
Continuous physiological monitoring
Personalized medicine

Examples mentioned:

Glucose monitoring (diabetes)
Cancer biomarkers
Cardiovascular monitoring
Infectious disease detection
The authors emphasize that miniaturization + biocompatibility + adsorption properties are key reasons nanosensors work so well

The introduction also notes:

Rapid growth in publications
Increasing focus on wearable, implantable, and point-of-care (POC) systems
A push toward real-time, continuous health data

2. Methodology

 This section explains how the authors conducted the review.

2.1 Literature Search Method

Followed PRISMA guidelines

Databases searched:

PubMed /ScienceDirect/Independent journals/Time range: 2014–2024

Initial records identified: ~400

Final papers included: 68 high-quality studies

Inclusion Criteria/Peer-reviewed/English language/Focused on medical or clinical nanosensor applications/Exclusion Criteria/Non-medical uses (food safety, environmental sensing)/Classification Strategy.

Nanosensors are classified by:

Signal transduction mechanism/Optical/Electrochemical/Biological/Magnetic/Mechanical.

Application domain:

Diagnostics/Monitoring/Drug delivery.

Purpose of this framework:

Identify research gaps/Track technological trends/Help clinicians and engineers align sensor types with medical needs

3. Nanosensors: An Overview

This is the core taxonomy section of the paper.

3.1 Optical Nanosensors

Detect changes in:

Fluorescence/Absorbance/Refractive index.

Subtypes:

Surface Plasmon Resonance (SPR) – gold/silver nanoparticles
Fluorescent nanosensors – quantum dots
Raman scattering sensors – molecular fingerprinting

Used for:

Rapid disease detection/Low-cost alternatives to large lab instruments.

3.2 Electrochemical Nanosensors

Detect changes in:

Current/Voltage/Impedance.

Key advantages:

High sensitivity/Low sample volume/Cost-effective/Portable.

Subtypes:

Enzymatic sensors – enzyme-catalyzed reactions
Non-enzymatic sensors – direct interaction with nanomaterials

Common materials:

Carbon nanotubes/Graphene/Noble metals (Au, Pt)/Conducting polymers.

Applications:

Glucose/Lactate/Urea/Disease biomarkers/Wearable POC devices.

3.3 Biological Nanosensors (Biosensors)

Combine nanotechnology with biological recognition elements.

Subtypes:

DNA-based sensors/Aptamer-based sensors/Enzyme-based sensors/Immunosensors/Cell-based sensors.

Used for:

Pathogen detection/Genetic mutation detection/Cancer diagnostics/Rapid virus testing.

3.4 Magnetic Nanosensors

Operate via:

Changes in magnetic properties/Magnetoresistance effects.

Mechanisms include:

GMR (Giant Magnetoresistance)/TMR (Tunnel Magnetoresistance)/Hall effect.

Applications:

MRI enhancement/Targeted drug delivery/Bioimaging/Molecular detection.

3.5 Mechanical Nanosensors

Detect:

Mass/Force/Motion.

Subtypes:

Cantilever-based sensors/Resonant sensors/Nanowire/nanotube sensors/Piezoelectric sensors.

Limitations:

Reduced sensitivity in fluids/Less widely used in healthcare (for now).

3.6 Nanosensors in POC, Wearables, and Drug Delivery

Key points:

POC devices reduce diagnosis time and errors/Wearables enable non-invasive continuous monitoring

Integration with:

AI/Machine learning/IoT/Enables digital healthcare ecosystems.

Drug delivery:

Smart, targeted release/Cancer/Cardiovascular diseases/Diabetes.

4. Applications of Nanosensors in Health Monitoring

4.1 Early Cancer Detection

Detects:

Tumor biomarkers/Circulating tumor cells (CTCs)/Tumor-derived exosomes.

Advantage over PET/CT/MRI:

Earlier detection/Lower cost/Higher sensitivity/Emphasis on blood, saliva, urine screening.

4.2 Cardiovascular Disease Monitoring

Detects:

Disease-specific biomarkers/Pressure changes in vessel walls.

Enables:

Early diagnosis/POC cardiovascular testing/Reduced organ toxicity vs conventional methods

4.3 Tuberculosis Diagnosis

Uses:

AgNPs/AuNPs/Quantum dots/NiO nanoparticles.

Techniques:

SPR/SERS/Fluorescence.

4.4 Glucose Monitoring (Diabetes)

Focus on:

Implantable and injectable nanosensors/Longer lifetime/Higher accuracy

Examples:

Enzyme-loaded nanocomposites/Graphene-based electrodes.

4.5 Plant Disease and Stress Monitoring

Uses single-walled carbon nanotubes (SWCNTs)

Detects:

Hydrogen peroxide (H₂O₂) as stress marker

Enables:

Early pathogen detection/Remote monitoring via near-infrared fluorescence

4.6 Therapeutic Drug Monitoring

Critical for:

Transplant patients/Immunosuppressant dosing

Enables:

Real-time dosage adjustment/Reduced toxicity

4.7 Pharmaceutical Applications & Nanomedicine

Nanoparticles for:

Gene therapy/Controlled drug release

Biosensors regulate:

Gene expression/Drug delivery timing/Mentions GFP reporter systems

4.8 Market Landscape

Current market size: $637–700 million
Projected by 2032: $2.37–3.1 billion
Electrochemical nanosensors dominate (>25%)

Strong growth in:

IoT integration/AI-driven analysis/North America & Europe.

5. Technology & Mechanisms

5.1 Materials & Fabrication

Carbon nanotubes (SWCNTs & MWCNTs)/Graphene/Quantum dots/Nanowires.

Fabrication methods:

CVD/VLS/Exfoliation/Electrodeposition.

5.2 Sensor Design & Characteristics

Key properties:

High surface-to-volume ratio/Electrical conductivity/Opticalresponsiveness/Multiplexed/detection/Biocompatibility.

5.3 Miniaturization & Wearables

Enables:

Skin-conformal sensors/Implantable systems

Measures:

Heart rate/Respiration/Blood pressure/Movement.

5.4 Wireless Communication

Bluetooth/Wi-Fi/Cellular/

Enables:

Remote monitoring/Real-time alerts/Data streaming to phones & hospitals

5.5 Real-Time Monitoring & IoT

Continuous data streams/AI & ML used for interpretation/Preventive healthcare emphasis.

5.6 Power Sources

Batteries/

Energy harvesting:

Thermal/Mechanical/RF/Solar.

6. Open Issues

Major challenges identified:

Lack of regulatory standards/Long-term biocompatibility unknowns/Data overload & integration issues/Cost and scalability/Ethical & privacy concerns

7. Conclusion & Future Outlook

Nanosensors are positioned as foundational tools for future healthcare

Strong emphasis on:

Early diagnosis/Personalized medicine/Continuous monitoring

Future depends on:

Better materials/Regulation/AI integration/Ethical governance.

[ESG]

 This paper explains and clearly separates Augmented Human (AH) technologies from Transhumanism (TH), two terms that are often confused but represent very different approaches to human enhancement. It argues that Augmented Human focuses on practical, near-term, and regulated improvements to human abilities using existing or emerging technologies—such as wearable and implantable sensors, brain–computer interfaces, prosthetics, AI-assisted systems, and even gene-editing tools like  CRISPR when used cautiously for therapeutic or restorative purposes—while keeping human identity fundamentally intact.
 In contrast, Transhumanism is described as a far more speculative, ideological, and future-oriented movement that seeks to radically redesign or transcend the human condition, often envisioning superhuman intelligence, extreme longevity or immortality, mind uploading, and human–AI fusion, frequently relying on unproven assumptions and higher ethical and societal risks.
 Overall, the paper argues that although AH and TH overlap in technologies and goals, AH is grounded in pragmatic engineering and medicine aimed at "improving quality of life," whereas TH is driven by broader philosophical visions that challenge what it means to be human and raise profound ethical, social, and existential concerns.
 
 Provided below is a section-by-section overview of the paper titled

 "Augmented Human and Transhuman: What is the Difference?"

https://link.springer.com/article/10.1007/s41133-025-00089-9

 Abstract
 
 The paper states that augmented human technologies and transhumanism both aim to enhance humans using technology (Al, implants, bioengineering, sensors, etc.), but they differ fundamentally in goals, scope, risks, and philosophical assumptions.

 The paper's goal is to clarify terminology, compare AH and TH, and discuss realism, ethics,  societal impacts, and Al's role in both approaches.

1. Introduction

 This section explains that humans have always enhanced themselves (tools, glasses, prosthetics), but modern technologies now allow direct physical and cognitive augmentation.

 Key points:

• AH and TH both attempt to enhance humans, but belong to different conceptual domains
• TH is described as much broader, more speculative, and more ideologically loaded
• The literature is vast and confusing, with overlapping terms (human enhancement, posthumanism, Human 2.0, etc.)
• Prior work has defined AH or TH separately, but rarely compared them systematically
• The paper explicitly aims to fill this gap by contrasting them side-by-side

2. Augmented Human and Transhumanism (Definitions Section)

2.1 Augmented Human (AH)

AH is defined as pragmatic, near-term human enhancement using existing or emerging technologies.

Core characteristics:

 Focuses on restoration, assistance, or moderate extension of human abilities Technologies include:

• Glasses, prosthetics, cochlear implants
• Wearables and implants
• Brain-computer interfaces (BCI)
• Exoskeletons
• Nootropic drugs
• Emphasis on safety, regulation, reversibility, and low risk
• Goal is improving quality of life, not redefining humanity
• Closely linked to human-computer integration and assistive technology
Examples discussed:
• New senses (360° vision, infrared vision)
• Augmented cognition via physiological and neural sensing
• Augmented action (strength, endurance via exoskeletons)

2.2 Transhumanism (TH)

 TH is described as a far more radical and speculative movement.
Core characteristics:

• Aims to re-engineer or transcend the human condition
• Willing to use experimental, risky, irreversible, or future technologies
Often inspired by:
• Philosophy
• Science fiction
• Futurism
• Ideological visions
Common TH goals:
• Radical life extension or immortality
• Superhuman intelligence
• Human-Al merging
• Mind uploading
Includes cultural movements such as:
• DIY body hackers ("grinders")
• Aesthetic or non-medical modification
• Discusses fringe forms like transableism (desire to remove healthy limbs)

2.3 Posthumanism

 Posthumanism is treated as an extension of transhumanism.

Key ideas:

 Seeks to move beyond biological humanity entirely
 
 Envisions:

• Humans existing only in digital or virtual form
• Full human-machine merger
Introduces distinctions between:
• Cyborgs (part-human, part-machine)
• Posthumans (no longer biologically human)
• Androids (fully artificial, not human)
The paper explicitly treats TH and posthumanism together due to overlap.

3. Differences and Similarities Between AH and TH

 This is a core analytical section.

Shared Features

• Both AH and TH:
• Aim to improve quality of life
• Use Al, biotech, digital tools
• Raise ethical concerns (consent, equality, unintended consequences)
Key Differences
AH:
• Near-term
• Regulated
• Incremental
• Engineering-driven
• Human identity remains intact

TH:

• Far-future
• Speculative
• Ideological
• Radical transformation
• Human identity may be replaced or erased

The paper includes Table 1, which directly
compares AH vs TH across:

• Scope
• Tools
• Risks
• Philosophical basis
• Timeframe

4. Role of Al in AH and TH

 Al is described as central to both, but used differently.
In AH:

• Pattern recognition
• Assistive systems
• Brain-computer decoding
In TH:
• Speculation about superintelligence
• Singularity
• Mind uploading
• Artificial consciousness

 The paper emphasizes that Al consciousness and mind uploading are not proven, and depend on philosophical assumptions, not empirical science.

5. Realism of Transhumanism

 This section critically evaluates TH claims.
 
 Key points:

• Technological progress is real, but extrapolation is unreliable
• Many TH claims rely on unproven assumptions
• Comparisons are made to historical over-optimism
• The paper stresses uncertainty, unknown limits, and "black swans"
• Concludes that TH is largely optimism-driven rather than evidence-driven

6. Underlying Philosophies of Transhumanism

 This section is explicitly philosophical.

Main claims:

• TH relies heavily on materialism (humans as machines)
• Some roots trace back to eugenics

Philosophical assumptions shape:

• Ethics
• Goals
• Risk tolerance

Bad philosophical premises can lead to catastrophic outcomes.
AH is less affected because it is engineering-focused, not ideological.

7. Ethical and Societal Risks (AH, TH, and AI)

 This is a major cautionary section.

Discussed risks:

• Loss of dignity and humanity
• Inequality and elite-only enhancements
• Al bias and manipulation
• Privacy violations (especially BCIs)
• Authoritarian misuse of human-embedded technologies
•(***) Genetic irreversibility (e.g., CRISPR)
• Alignment problems in Al

The paper strongly emphasizes that capability = desirability.

8. Conclusions

Final takeaways:

• AH and TH overlap but are not the same
• AH = pragmatic, therapeutic, incremental
• TH = ideological, speculative, transformative
• TH raises existential risks and societal challenges
• Smaller, safer human augmentations are more realistic and ethically manageable
• Distinguishing AH from TH is crucial for policy, research, and public understanding

I want you to realize... the technologies mentioned for human augmentation in this paper, are the same  technologies present in Covid tests and Vaccinations...

***about mention on irreversible gene edit, researshers dicovered how to reverse...


{•(***) Genetic irreversibility (e.g., CRISPR) =

-I needed to intervene on this point, because in 2021 researchers found a new method of gene editing in which it is said to be a reversible technique, information about>

"
New, reversible CRISPR method can control
gene expression while leaving underlying
DNA sequence unchanged

April 9 2021

 A new CRISPR method allows researchers to silence most genes in the human
genome without altering the underlying DNA sequence -- and then reverse the
changes.

Credit: Jennifer Cook-Chrysos/Whitehead Institute

Over the past decade, the CRISPR-Cas9 gene editing system has
revolutionized genetic engineering, allowing scientists to make targeted
changes to organisms' DNA. While the system could potentially be
useful in treating a variety of diseases, CRISPR-Cas9 editing involves
cutting DNA strands, leading to permanent changes to the cell's genetic
material.
Now, in a paper published online in Cell on April 9, researchers describe
a new gene
 editing technology called CRISPRoff that allows researchers
to control gene expression with high specificity while leaving the
sequence of the DNA unchanged. Designed by Whitehead Institute
Member Jonathan Weissman, University of California San Francisco
assistant professor Luke Gilbert, Weissman lab postdoc James Nuñez
and collaborators, the method is stable  enough to be inherited through
hundreds of cell divisions, and is also fully reversible.

Citation: New, reversible CRISPR method can control gene expression while leaving underlying

DNA sequence unchanged (2021, April 9) retrieved 3 January 2026 from

https://phys.org/news/2021-04-reversible-crispr-method-gene-underlying.html " }

-That is, in summary, if you edit your gene from your eyes to blue when they are brown in nature, and you don't like the result blue eyes, you can get brown eyes again... Just one example.

[ESG]

  In-Body THz Graphene antennas for 6G emit radiation..👇

 This paper presents the first experimental demonstration of a working terahertz (THz) antenna made from monolayer CVD graphene, showing that graphene can actually radiate (emit radiation) at THz frequencies rather than only in simulations. The authors design a stacked graphene-on-hBN patch antenna that operates around 250 GHz, achieving measurable emission and confirming that graphene antennas can be smaller than metal antennas while remaining compatible with standard CMOS back-end-of-line fabrication. The work is motivated by future 6G short-range communication needs, specifically chip-scale and intra-chip wireless networks where wired interconnects become a bottleneck, and it explicitly points to intrabody and chip-to-chip networks as target applications where extreme miniaturization and short-range THz links are required. By proving that a graphene stack antenna can function at THz frequencies with real materials, the paper establishes a practical hardware foundation for embedded, in-body chips and ultra-dense chip-scale communication networks envisioned for next-generation systems.

Provided below is a section-by-section overview of the paper "A THz graphene-on-hBN stack patch antenna for future 6G communications"

https://www.nature.com/articles/s41598-025-16695-x

What this paper is about:

 This paper reports the first experimental demonstration of a working terahertz (THz) antenna made from monolayer CVD graphene, not just simulations. The authors show that stacking two graphene layers with a thin dielectric in between makes graphene antennas actually emit radiation at THz frequencies, which had not been experimentally proven before.

Abstract

 Future 6G systems need very high frequencies (mm-wave and THz) for extreme data rates.
Graphene antennas are predicted to be smaller and tunable, but until now, this was only theoretical.
 The authors build a two-layer graphene "stack" antenna on hexagonal boron nitride (hBN).
It resonates around 250.7 GHz, with a measured gain of -9.5 dB.
The design is compatible with CMOS manufacturing, making it practical for real 6G
hardware.

Introduction

Why this work matters

6G aims for Tbps data rates, requiring THz frequencies.
THz signals suffer from high losses, so they're best for short-range applications like:

• Chip-to-chip links
• Intra-chip wireless interconnects
• Highly integrated systems
Graphene is attractive because:
• It supports surface plasmon polaritons (SPPs) at THz
• These allow antennas to be physically smaller than metal ones
• Graphene conductivity (and frequency) can be tuned electrically
Until this paper, no monolayer graphene THz antenna had been experimentally shown to radiate

Prior work & problem statement👇

What was missing before:

• Most graphene THz antennas existed only in simulations
Experimental graphene antennas were:
• At low GHz frequencies (where graphene has no advantage), or
• Made of many graphene layers, behaving more like graphite
Real graphene (especially CVD graphene) has:
• Lower mobility
• Lower conductivity than ideal theoretical models
This gap explains why 10+ years of predictions never became experiments
Antenna design & simulation
What they built:
• A patch antenna on a polyimide substrate
The radiating element is a stack:
• Bottom graphene layer
• Thin Al2O, dielectric (80 nm)
• Top graphene layer
The graphene sits on hBN, which:
• Reduces unwanted doping
• Improves electrical performance
Why stacking helps
• Two graphene layers electromagnetically couple
• This compensates for low graphene quality
• The stack achieves similar performance to a single graphene layer with half the required conductivity

Key takeaway

Stack design makes graphene antennas practical with real-world materials
Graphene conductivity modeling:
How they model graphene

• Uses the Kubo formula
At THz frequencies:
• Intraband conductivity dominates
• Interband effects are negligible
Performance depends on:
• Chemical potential (doping level)
• Relaxation time (material quality)
Important result
• Higher chemical potential → higher resonance frequency
• Stack design allows lower material quality to
still work

Simulation results:

Comparison

• Metal antenna (palladium)
• Single-layer graphene antenna
• Graphene stack antenna
Findings
• Graphene antennas resonate at lower frequencies than metal ones of the same size
• This proves graphene antennas can be smaller
• Stack antenna shows higher gain than single-layer graphene
• Efficiency is still lower than metal, but clearly measurable

Antenna fabrication:

How it was made

•Fabricated on 50 µm Kapton polyimide
•Uses standard BEOL-compatible steps
•Graphene transferred using PMMA wet transfer
•hBN used as a buffer layer
• Al2O3 deposited using ALD with seed layer
•Backside metal ground plane added
• Important experimental detail
•Graphene on hBN had 4x lower resistance than graphene on Kapton
•Confirms hBN significantly improves performance

Measurement setup (far-field THz testing):

What they measured

• S-parameters (S11, S21)
• Radiation gain
• Far-field emission

Setup:

• THz VNA with frequency extenders (220-325 GHz)
• Graphene antenna as emitter
• Standard horn antenna as receiver
• True far-field measurement, not near-field
guessing

Measurement results:

Key experimental proof

• Graphene antenna does radiate THz signals
• Measured resonance at 250.7 GHz
Measured gain:
• -9.5 dB at normal incidence
• Up to -6.7 dB considering radiation angle
• Metal antenna still outperforms graphene
• Sample-to-sample variation is high due to graphene quality

Discussion:

What the authors admit

Performance is limited by:

• CVD graphene quality
• Material inhomogeneity
• Simulations often assume unrealistically good graphene
• Despite this, stack configuration works
• Single-layer graphene antennas did not emit measurably
• Stack design is the key breakthrough

Conclusion

Main takeaways:

First experimental THz emission from monolayer CVD graphene Demonstrates:

• 10% size reduction vs metal antennas
• Stack antennas outperform single-layer graphene
• CMOS-compatible, BEOL-integrable design
• With better graphene manufacturing, performance could rival metal antennas
• Confirms long-standing graphene antenna predictions are physically real

 This paper proves experimentally that stacked monolayer graphene antennas can emit terahertz radiation, validating years of theory and opening the door to miniaturized, tunable 6G antennas built with real manufacturing processes.

[ESG]

Graphene for The Internet of Bio-Nano Things..👇

 This perspective paper explains how graphene
and related materials (GRMs) can serve as the key physical building blocks for the Internet of Bio-Nano Things (loBNT)-a communication framework that connects biological systems and artificial micro-/ nanoscale devices, especially inside the human body, to conventional digital networks. The authors describe how traditional wireless technologies are unsuitable at these scales and show that molecular communication, terahertz (THz) electromagnetic signaling, and ultrasonic communication are better suited for intrabody environments. They argue that graphene's unique electrical, optical, mechanical, and biochemical properties make it ideal for implementing nano-transceivers, bio-cyber interfaces, implantable biosensors, neural interfaces, smart drug-delivery systems, and self-powered energy harvesting components. Overall, the paper positions graphene as a unifying material that can bridge the biochemical domain of living systems with the electromagnetic domain of the Internet, enabling practical, implantable loBNT networks for continuous health monitoring, diagnosis, and therapy rather than remaining purely theoretical concepts.

Provided below is a section-by-section breakdown of the paper:

"Graphene and related materials for the Internet of Bio-Nano Things" APL Materials, Perspective, August 2023

https://pubs.aip.org/.../Graphene-and-related-materials...

Abstract

The paper explains that the Internet of Bio-Nano Things (loBNT) is a networking system made of biological entities and artificial micro/ nanoscale devices operating inside environments like the human body. Traditional wireless tech does not work well at this scale, so new communication methods (molecular, terahertz, ultrasonic) are needed. The authors argue that graphene and related materials (GRMs) are uniquely suited to build the transceivers, interfaces, and energy systems required to make loBNT practical.

I. Introduction - What loBNT Is and Why It Matters

This section introduces loBNT as part of the broader Internet of Everything (loE) vision.

loBNT networks include:

• Engineered bacteria
• Nanosensors
• Implantable or injectable devices
• In-body nanonetworks

These systems communicate using biochemical, electrochemical, molecular, terahertz (THz), or ultrasonic signals, not conventional radio waves. The authors emphasize intrabody applications, such as continuous health monitoring, smart drug delivery, and artificial biochemical networks inside the body. The section clearly frames loBNT as a bridge between biology and digital networks via bio-cyber interfaces.

II. Graphene and Related Materials (GRMs)

This section explains what graphene is and why it is important.

A. Properties of GRMs

• Graphene is a single-atom-thick carbon sheet with:
• Extremely high electrical conductivity
• Huge surface-to-volume ratio (very sensitive to molecules)
• Mechanical flexibility (good for wearables and implants
• Biocompatibility

The paper explains different forms:

• Graphene
• Graphene oxide (GO)
• Reduced graphene oxide (rGO)
• Graphene nanoribbons

These properties make GRMs ideal for biosensors, implantable electronics, neural interfaces, and nano-communication devices.

B. Synthesis of GRMs

The authors review how graphene is made:

• Top-down methods (exfoliation, liquid-phase processing)
• Bottom-up methods (chemical vapor deposition, epitaxial growth)

They explain tradeoffs between quality, scalability, and device performance, which directly affects biomedical and loBNT applications.

III. GRM-Based Micro/Nanoscale Transceivers

This is the core technical section describing how graphene enables communication.

A. Molecular Communication (MC) Transceivers

Molecular communication is presented as the most biologically compatible method for intrabody networks.

• Receivers: Graphene bioFETs detect molecules (DNA, proteins, biomarkers).
• Transmitters: Graphene membranes and hydrogels release molecules in a controlled way.

These systems are explicitly described as in-body nanonetwork components, suitable for health monitoring and biosensing inside tissues.

B. THz-Band Nanocommunication Transceivers

This section explains terahertz electromagnetic communication at the nanoscale.
Key points:

• THz waves allow extreme miniaturization
• Graphene supports plasmonic antennas and detectors
• THz communication is non-ionizing and considered biologically "safe"

The authors discuss graphene-based nano-antennas, modulators, and detectors that can operate inside biological environments.

C. Ultrasonic Nanocommunications

Here the paper discusses ultrasound-based communication for intrabody use.

• Sound travels well in water-rich tissues
• Graphene enables photoacoustic ultrasound generation
• Suitable for high-data-rate, low-damage in-body signaling

Graphene composites (rGO-PDMS, CNT hybrids) are shown to enable miniaturized ultrasonic transmitters and receivers.

D. Multi-Modal Transceivers

• This section introduces the idea of "universal transceivers":
• Devices that support multiple communication modes
• Can translate between molecular, electrical, acoustic, and EM signals
• Reduce hardware complexity

Graphene is presented as the key material enabling this cross-domain signal conversion inside loBNT systems.

IV. GRM-Based Bio-Cyber Interfaces

This section focuses on connecting in-body networks to the Internet.

A. GRM-Based Biosensors

Graphene biosensors act as bio-cyber interfaces by converting biochemical signals into electrical or wireless signals.

Examples include:

• Implantable and wearable biosensors
• Contact-lens glucose sensors
• Non-invasive sensing of biomarkers linked to internal processes

These sensors allow internal molecular networks to communicate with external digital systems.

B. GRM-Based Neural Interfaces

The paper discusses graphene neural interfaces for:

• Brain-machine communication
• Gut-brain axis monitoring
• Optogenetic stimulation
• Neurotransmitter sensing

Graphene enables high-resolution, flexible, implantable neural interfaces that can both read and influence neural activity.

C. GRM-Based Drug Delivery Systems

Graphene is used in stimuli-responsive drug delivery:

• Electrically triggered
• Light-activated
• Magnetically controlled

These systems are framed as molecular communication transmitters, releasing drugs as information-encoded molecules in response to external commands.

V. GRM-Based Micro/Nanoscale Energy Harvesting and Storage

This section explains how loBNT devices stay powered.
Graphene enables energy harvesting from:

• Body motion
• Blood flow
• Heat
• Light
• Sound

It also enables micro-supercapacitors for energy storage. This is critical for self-powered, long-term implantable nanosystems.
Overall Takeaway

This paper is a roadmap showing how graphene makes the Internet of Bio-Nano Things physically possible, especially for in-body, implantable, and intrabody nanonetworks. It connects:

• Molecular communication
• THz and ultrasonic signaling
• Bio-cyber interfaces
° Neural interfaces
• Energy harvesting

into a single graphene-enabled architecture for future in-body networks

[ESG]

 This paper reviews the idea of "smart dust," which refers to extremely tiny sensor devices- about the size of grains of sand or smaller-that can work together as a network to detect and map chemicals in real time. The authors explain how existing sensor technologies, nanomaterials, wireless power, and communication methods can be miniaturized so these particles can be scattered in the environment or even used inside the human body. In simple terms, the paper shows how smart dust could continuously monitor complex chemical mixtures, such as pollutants in the air or water, or biological markers inside the body, without needing bulky lab equipment. For in-body applications, the authors discuss biodegradable and biocompatible versions of these sensors that could be implanted, injected, or ingested to track ions, metabolites, or disease-related chemicals, then "safely" dissolve after use. Overall, the paper lays out a roadmap for turning today's wearable and implantable sensors into invisible, self-powered sensing particles that could transform environmental monitoring, healthcare diagnostics, and early disease detection.

Provided below is a section-by-section overview of the paper "Smart Dust for Chemical Mapping" - Indrajit Mondal & Hossam Haick (Advanced Materials, 2025

https://pmc.ncbi.nlm.nih.gov/articles/PMC12075923/

Overall purpose of the paper:

 This is a review paper, not a single experiment. It explains how "smart dust" - sub-millimeter autonomous sensing particles - can be built, powered, networked, and made "safe" in order to map chemicals in real time across space and time.

The paper's core physical idea is:

Tiny, self-powered sensor particles working together as a swarm to detect, identify, and map chemical mixtures in environments, including inside the human body.

1. Introduction - Why smart dust is needed

What this section explains:
Modern problems (pollution, chronic disease, pandemics, chemical exposure) require real-time chemical information, not delayed lab tests.
Chemical threats are often mixtures, not single compounds.

Traditional tools (GC-MS, spectrometers) are powerful but:

• Large
• Stationary
• Require sample collection and trained operators

Key motivation:

• We lack continuous, spatially resolved chemical data.
• Smart dust is proposed to fill this gap by enabling distributed chemical sensing everywhere.

Important point:

• The paper explicitly connects smart dust to healthcare, in-vivo diagnostics, pandemics (COVID-19), and environmental exposure monitoring.

2. Smart Dust - What it physically is

What smart dust actually consists of Each smart dust "mote" includes:

• Micro/nano chemical sensors
• Minimal electronics
• Wireless communication element (antenna)
• Wireless or harvested power source
• Physical scale
° Sub-millimeter (grain-of-sand size or smaller)

Key capabilities:

• Operate alone or as part of a swarm
• Communicate with nearby motes and external receivers
• Can be airborne, implanted, ingested, or dispersed in environments

Important clarification:

 This is not sci-fi nanobots - these are microfabricated sensor systems, closer to MEMS + nano-materials.

3. Transforming Existing Sensors into Smart Dust

This entire section explains how known sensor technologies are adapted to become smart dust compatible.

3.1 Enhancing Sensitivity

Goal Detect very small chemical changes with extremely small sensors.

Sensor types discussed:

• Chemiresistive gas sensors (metal oxides, graphene, CNTs)
• Electrochemical sensors
• Optical sensors (SERS, Raman)
• Triboelectric sensors

Key idea:

• Sensors must remain sensitive even when shrunk to sub-mm scale.
• Nanomaterials (graphene, MoS2, CNTs) compensate for size reduction

3.2 Enhancing Selectivity

Problem Small sensors easily get confused by mixed chemicals.

Solutions described:

• Doping sensor materials
• Surface functionalization
• Cross-reactive sensor arrays + Al
• Machine learning pattern recognition

Advanced concept:

• Spin-orbit coupling (SOC) for chiral molecule discrimination (important for biomedical sensing)
Meaning Smart dust doesn't just "detect something" - it learns chemical fingerprints.

3.3 Wearable & Implantable Sensors Smart Dust

This section is very important for in-body applications

What it says:

• Wearables and implants are precursors to smart dust.

Technologies like:

• Microneedle sensors
• Implantable neural probes
• Flexible electronics already demonstrate the feasibility.

In-body relevance

Smart dust could:

• Monitor sweat, interstitial fluid, blood chemistry
• Detect ions, metabolites, biomarkers
• Operate autonomously inside the body

Key distinction

• Wearables = human-scale
• Smart dust = invisible, distributed, autonomous

3.4 Sustainable & Biodegradable Smart Dust

Major concern addressed:

• What happens when billions of particles are deployed?

Solutions:

• Biodegradable polymers (PLA, PLGA, chitosan, cellulose)
• Biodegradable metals (Mg, Zn)
• Dissolvable electronics
• Edible electronics (for ingestible sensors)

Key takeaway:

• The paper explicitly argues smart dust must be biodegradable and biocompatible, especially for medical use.

3.5 Miniaturization

How they shrink systems:

• MEMS fabrication
• Origami-style self-folding structures
• Vertical stacking
• 3D micro-printing (multiphoton lithography)

Why this matters:

• Smaller size → less power → easier deployment
• Enables airborne swarms and minimally invasive implants

Challenge acknowledged:

• Smaller sensors = weaker signals → compensated with nanomaterials and signal
amplification

3.6 Wireless Powering

Why batteries don't work:

• Too big
• Toxic
• Limited lifetime
• Powering methods discussed
• Inductive coupling (near-field)
• Capacitive coupling
• Far-field RF/microwave
• Acoustic (ultrasound)
• Piezoelectric & triboelectric harvesting

In-body relevance:

• Acoustic and inductive powering are highlighted for implantable smart dust.

3.7 Wireless Communication & Storage

How data gets out:

• NFC
• Backscatter RF
• Optical scattering
• SAW (surface acoustic wave) systems

Key idea:

• Smart dust doesn't store much data locally
Data is transmitted to:
• Nearby receivers
• Cloud systems
• Al pipelines

Result:

• Real-time chemical maps over space and time

Final takeaway

This paper lays out a technical roadmap for turning tiny chemical sensors into autonomous, networked, biodegradable "dust" that can map chemicals anywhere - including inside the human body.
It is explicitly compatible with in-vivo sensing, implantables, pandemic monitoring, and continuous health surveillance, while also acknowledging engineering, safety, and environmental limits