Human Body Digital Twin: A Master Plan

The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development. The roadmap covers the development of various components, such as wearable devices, data collection, data analysis, and decision-making systems. The article also highlights the necessary support, security, cost, and ethical considerations that must be addressed in order to ensure responsible and effective implementation of the human body DT. The proposed roadmap provides a framework for guiding future development and offers a unique perspective on the future of the human body DT, facilitating new interdisciplinary research and innovative solutions in this rapidly evolving field.Comment: 3 figure

Machine learning methods for automatic silent speech recognition using a wearable graphene strain gauge sensor

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene’s unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/

Machine Learning Methods for Automatic Silent Speech Recognition Using a Wearable Graphene Strain Gauge Sensor.

Silent speech recognition is the ability to recognise intended speech without audio information. Useful applications can be found in situations where sound waves are not produced or cannot be heard. Examples include speakers with physical voice impairments or environments in which audio transference is not reliable or secure. Developing a device which can detect non-auditory signals and map them to intended phonation could be used to develop a device to assist in such situations. In this work, we propose a graphene-based strain gauge sensor which can be worn on the throat and detect small muscle movements and vibrations. Machine learning algorithms then decode the non-audio signals and create a prediction on intended speech. The proposed strain gauge sensor is highly wearable, utilising graphene's unique and beneficial properties including strength, flexibility and high conductivity. A highly flexible and wearable sensor able to pick up small throat movements is fabricated by screen printing graphene onto lycra fabric. A framework for interpreting this information is proposed which explores the use of several machine learning techniques to predict intended words from the signals. A dataset of 15 unique words and four movements, each with 20 repetitions, was developed and used for the training of the machine learning algorithms. The results demonstrate the ability for such sensors to be able to predict spoken words. We produced a word accuracy rate of 55% on the word dataset and 85% on the movements dataset. This work demonstrates a proof-of-concept for the viability of combining a highly wearable graphene strain gauge and machine leaning methods to automate silent speech recognition.EP/S023046/

Emerging Indoor Photovoltaic Technologies for Sustainable Internet of Things

Funder: Priority Academic Program Development of Jiangsu Higher Education Institutions; Id: http://dx.doi.org/10.13039/501100012246Funder: 111 Project; Id: http://dx.doi.org/10.13039/501100013314Funder: Joint International Research Laboratory of Carbon‐Based Functional Materials and DevicesFunder: European Union; Id: http://dx.doi.org/10.13039/501100000780Abstract: The Internet of Things (IoT) provides everyday objects and environments with “intelligence” and data connectivity to improve quality of life and the efficiency of a wide range of human activities. However, the ongoing exponential growth of the IoT device ecosystem—up to tens of billions of units to date—poses a challenge regarding how to power such devices. This Progress Report discusses how energy harvesting can address this challenge. It then discusses how indoor photovoltaics (IPV) constitutes an attractive energy harvesting solution, given its deployability, reliability, and power density. For IPV to provide an eco‐friendly route to powering IoT devices, it is crucial that its underlying materials and fabrication processes are low‐toxicity and not harmful to the environment over the product life cycle. A range of IPV technologies—both incumbent and emerging—developed to date is discussed, with an emphasis on their environmental sustainability. Finally, IPV based on emerging lead‐free perovskite‐inspired absorbers are examined, highlighting their status and prospects for low‐cost, durable, and efficient energy harvesting that is not harmful to the end user and environment. By examining emerging avenues for eco‐friendly IPV, timely insight is provided into promising directions toward IPV that can sustainably power the IoT revolution

Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency

Our research presents a wearable Silent Speech Interface (SSI) technology that excels in device comfort, time-energy efficiency, and speech decoding accuracy for real-world use. We developed a biocompatible, durable textile choker with an embedded graphene-based strain sensor, capable of accurately detecting subtle throat movements. This sensor, surpassing other strain sensors in sensitivity by 420%, simplifies signal processing compared to traditional voice recognition methods. Our system uses a computationally efficient neural network, specifically a one-dimensional convolutional neural network with residual structures, to decode speech signals. This network is energy and time-efficient, reducing computational load by 90% while achieving 95.25% accuracy for a 20-word lexicon and swiftly adapting to new users and words with minimal samples. This innovation demonstrates a practical, sensitive, and precise wearable SSI suitable for daily communication applications.Comment: 5 figures in the article; 11 figures and 4 tables in supplementary informatio

Double-Framed Thin Elastomer Devices

Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 μm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 μm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications.Elastomers and, in particular, polydimethylsiloxane (PDMS) are widely adopted as biocompatible mechanically compliant substrates for soft and flexible micro-nanosystems in medicine, biology, and engineering. However, several applications require such low thicknesses (e.g., <100 μm) that make peeling-off critical because very thin elastomers become delicate and tend to exhibit strong adhesion with carriers. Moreover, microfabrication techniques such as photolithography use solvents which swell PDMS, introducing complexity and possible contamination, thus limiting industrial scalability and preventing many biomedical applications. Here, we combine low-adhesion and rectangular carrier substrates, adhesive Kapton frames, micromilling-defined shadow masks, and adhesive-neutralizing paper frames for enabling fast, easy, green, contaminant-free, and scalable manufacturing of thin elastomer devices, with both simplified peeling and handling. The accurate alignment between the frame and shadow masks can be further facilitated by micromilled marking lines on the back side of the low-adhesion carrier. As a proof of concept, we show epidermal sensors on a 50 μm-thick PDMS substrate for measuring strain, the skin bioimpedance and the heart rate. The proposed approach paves the way to a straightforward, green, and scalable fabrication of contaminant-free thin devices on elastomers for a wide variety of applications

Real-time, noise and drift resilient formaldehyde sensing at room temperature with aerogel filaments

Formaldehyde, a known human carcinogen, is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases remains challenging, especially for low-power sensors suffering from noise and baseline drift. We report a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. By optimising the morphology and doping of the printed structures, we achieve a record-high response of 15.23 percent for 1 parts-per-million formaldehyde and an ultralow detection limit of 8.02 parts-per-billion consuming only 130 uW power. Based on measured dynamic response snapshots, we also develop an intelligent computational algorithm for robust and accurate detection in real time despite simulated substantial noise and baseline drift, hitherto unachievable for room-temperature sensors. Our framework in combining materials engineering, structural design and computational algorithm to capture dynamic response offers unprecedented real-time identification capabilities of formaldehyde and other volatile organic compounds at room temperature.Comment: Main manuscript: 21 pages, 5 figure. Supplementary: 21 pages. 13 Figures, 2 tabl

Nanofabrication of Conductive Metallic Structures on Elastomeric Materials.

Existing techniques for patterning metallic structures on elastomers are limited in terms of resolution, yield and scalability. The primary constraint is the incompatibility of their physical properties with conventional cleanroom techniques. We demonstrate a reliable fabrication strategy to transfer high resolution metallic structures of <500 nm in dimension on elastomers. The proposed method consists of producing a metallic pattern using conventional lithographic techniques on silicon coated with a thin sacrificial aluminium layer. Subsequent wet etching of the sacrificial layer releases the elastomer with the embedded metallic pattern. Using this method, a nano-resistor with minimum feature size of 400 nm is fabricated on polydimethylsiloxane (PDMS) and applied in gas sensing. Adsorption of solvents in the PDMS causes swelling and increases the device resistance, which therefore enables the detection of volatile organic compounds (VOCs). Sensitivity to chloroform and toluene vapor with a rapid response (~30 s) and recovery (~200 s) is demonstrated using this PDMS nano-resistor at room temperature

A Compact SPICE Model for Organic TFTs and Applications to Logic Circuit Design

This work introduces a compact DC model developed for organic thin film transistors (OTFTs) and its SPICE implementation. The model relies on a modified version of the gradual channel approximation that takes into account the contact effects, occurring at nonohmic metal/organic semiconductor junctions, modeling them as reverse biased Schottky diodes. The model also comprises channel length modulation and scalability of drain current with respect to channel length. To show the suitability of the model, we used it to design an inverter and a ring oscillator circuit. Furthermore, an experimental validation of the OTFTs has been done at the level of the single device as well as with a discrete-component setup based on two OTFTs connected into an inverter configuration. The experimental tests were based on OTFTs that use small molecules in binder matrix as an active layer. The experimental data on the fabricated devices have been found in good agreement with SPICE simulation results, paving the way to the use of the model and the device for the design of OTFT-based integrated circuits.This work was supported in part by the MIUR by means of the national Program PON R&C 2007-2013 and in part by project “Elettronica su Plastica per Sistemi Smart disposable” PON02 00355 3416798

Nano-to-microporous networks via inkjet printing of ZnO nanoparticles/graphene hybrid for ultraviolet photodetectors

Inkjet-printed photodetectors have gained enormous attention over the past decade. However, device performance is limited without postprocessing, such as annealing and UV exposure. In addition, it is difficult to manipulate the surface morphology of the printed film using an inkjet printer because of the limited options of low viscosity ink solutions. Here, we employ a concept involving the control of the inkjet-printed film morphology via modulation of cosolvent vapor pressure and surface tension for the creation of a high-performance ZnO-based photodetector on a flexible substrate. The solvent boiling point across different cosolvent systems is found to affect the film morphology, which results in not only distinct photoresponse time but also photodetectivity. ZnO-based photodetectors were printed using different solvents, which display a fast photoresponse in low-boiling point solvents because of the low carbon residue and larger photodetectivity in high-boiling point solvent systems due to the porous structure. The porous structure is obtained using both gas–liquid surface tension differences and solid–liquid surface differences, and the size of porosity is modulated from nanosize to microsize depending on the ratio between two solvents or two nanomaterials. Moreover, the conductive nature of graphene enhances the transport behavior of the photocarrier, which enables a high-performance photodetector with high photoresponsivity (7.5 × 102AW–1) and fast photoresponse (0.18 s) to be achieved without the use of high-boiling point solvents