Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification

Enhanced Sensitivity in Photovoltaic 2D MoS2/Te Heterojunction VOC Sensors

Volatile organic compound (VOC) sensors have a broad range of applications including healthcare monitoring, product quality control, and air quality management. However, many such applications are demanding, requiring sensors with high sensitivity and selectivity. 2D materials are extensively used in many VOC sensing devices due to their large surface-to-volume ratio and fascinating electronic properties. These properties, along with their exceptional flexibility, low power consumption, room-temperature operation, chemical functionalization potential, and defect engineering capabilities, make 2D materials ideal for high-performance VOC sensing. Here, a 2D MoS2/Te heterojunction is reported that significantly improves the VOC detection compared to MoS2 and Te sensors on their own. Density functional theory (DFT) analysis shows that the MoS2/Te heterojunction significantly enhances the adsorption energy and therefore sensing sensitivity of the sensor. The sensor response, which denotes the percentage change in the sensor's conductance upon VOC exposure, is further enhanced under photo-illumination and zero-bias conditions to values up to ≈7000% when exposed to butanone. The MoS2/Te heterojunction is therefore a promising device architecture for portable and wearable sensing applications.

Visual Recognition of Volatile Organic Compounds by Photonic Nose Integrated with Multiple Metal-Organic Frameworks

The photonic nose inspired by the olfactory system is an integrated detection platform constructed by multiple sensing units as channels. However, in the detection of volatile organic compounds (VOCs), the sensing results that cannot be directly readable and the poor ability to distinguish analytes with similar chemical properties are the main challenges faced by this sensor. Here, 8 metal-organic frameworks (MOF)-based photonic crystals are used as the basic sensing units to construct a photonic nose detection platform. The microscopic adsorption of VOCs by MOFs enables the photonic crystals (PCs) to produce macroscopic structural color output, and further makes the photonic nose have specific color fingerprints for different VOCs, the response time of all PCs to VOCs can be within 1 s. Through the color fingerprint, the visual identification of VOCs produced by 5 common solvent vapors is realized, and 9 VOCs with similar chemical properties are further distinguished. In addition, the application potential of the photonic nose in the actual environment is verified by identifying different contents of benzene in the paint. It is envisaged that the MOF-based photonic nose has great reference value for the development of intelligent and multi-component synergistic functional gas sensors.

Artificial Intelligence-Enhanced Waveguide "Photonic Nose"- Augmented Sensing Platform for VOC Gases in Mid-Infrared

On-chip nanophotonic waveguide sensor is a promising solution for miniaturization and label-free detection of gas mixtures utilizing the absorption fingerprints in the mid-infrared (MIR) region. However, the quantitative detection and analysis of organic gas mixtures is still challenging and less reported due to the overlapping of the absorption spectrum. Here,an Artificial-Intelligence (AI) assisted waveguide "Photonic nose" is presented as an augmented sensing platform for gas mixture analysis in MIR. With the subwavelength grating cladding supported waveguide design and the help of machine learning algorithms, the MIR absorption spectrum of the binary organic gas mixture is distinguished from arbitrary mixing ratio and decomposed to the single-component spectra for concentration prediction. As a result, the classification of 93.57% for 19 mixing ratios is realized. In addition, the gas mixture spectrum decomposition and concentration prediction show an average root-mean-square error of 2.44 vol%. The work proves the potential for broader sensing and analytical capabilities of the MIR waveguide platform for multiple organic gas components toward MIR on-chip spectroscopy.

Predicting Chronological Age via the Skin Volatile Profile

Skin volatile emissions offer a noninvasive insight into metabolic activity within the body as well as the skin microbiome and specific volatile compounds have been shown to correlate with age, albeit only in a few small studies. Building on this, here skin volatiles were collected and analyzed in a healthy participant study (n = 60) using a robust headspace-solid phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) workflow. Following processing, 18 identified compounds were deemed suitable for this study. These were classified according to gender influences and their correlations with age were investigated. Finally, 6 volatiles (of both endogenous and exogenous origin) were identified as significantly changing in abundance with participant age (p < 0.1). The potential origins of these dysregulations are discussed. Multiple linear regression (MLR) analysis was employed to model age based on these significant volatiles as independent variables, along with gender. Our analysis shows that skin volatiles show a strong predictive ability for age (explained variance of 68%), stronger than other biochemical measures collected in this study (skin surface pH, water content) which are understood to vary with chronological age. Overall, this work provides new insights into the impact of aging on the skin volatile profiles which comprises both endogenously and exogenously derived volatile compounds. It goes toward demonstrating the biological significance of skin volatiles and will help pave the way for more rigorous consideration of the healthy "baseline" skin volatile profile in volatilomics-based health diagnostics development going forward.

Porous Conductive Textiles for Wearable Electronics

Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.

Machine Learning-Enabled Intelligent Gesture Recognition and Communication System Using Printed Strain Sensors

Gesture contains abundant and complicated information in daily life; as a consequence, gesture recognition attracts a wide range of application prospects and academic values as an important way of achieving human-machine interactions (HMIs). Here, we report an intelligent system consisting of a smart glove made by printed CNT-graphene/PDMS strain sensors. The smart glove shows excellent fitness, comfort, and lightness for human hands. Inspired by machine learning strategies, several objects and gestures can be well classified and implemented by a customized artificial neural network. Several data sets of different sign language gestures and object-grabbing gestures were established, and the result shows that the intelligent system can achieve an average accuracy of 97% and up to 99.4% for a number of gesture groups. Moreover, a robot hand is connected to this system, which is able to react to the motion of human hands with certain gestures where simple sign communication is achieved. These features provide a feasible practical application scheme for gesture recognition in HMIs.

Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar

Biochar nanoparticles have recently attracted attention, owing to their environmental behavior and ecological effects. However, biochar has not been shown to contain carbon quantum dots (< 10 nm) with unique photovoltaic properties. Therefore, this study utilized several characterization techniques to demonstrate the generation of carbon quantum dots in biochar produced from 10 types of farm waste. The generated carbon quantum dots had a quasi-spherical morphology and high-resolution lattice stripes with lattice spacings of 0.20-0.23 nm. Moreover, they contained functional groups with good hydrophilic properties, such as amino and hydroxyl groups, and elemental O, C, and N on the surface. A crucial determinant of the photoluminescence properties of carbon quantum dots is their fluorescence quantum yield. Therefore, the relationship between the biochar preparation parameters and the fluorescence quantum yield was investigated using six machine learning analytical models based on 480 samples. Among the models, the gradient-boosting decision-tree regression model exhibited the best predictive performance (R2 > 0.9, RMSE <0.02, and MAPE <3), and was used for the analysis of feature importance; compared to the properties of the raw material, the production parameters had a greater effect on the fluorescence quantum yield. Additionally, four key features were identified: pyrolysis temperature, residence time, N content, and C/N ratio, which were independent of farm waste type. These features can be used to accurately predict the fluorescence quantum yield of carbon quantum dots in biochar. The relative error range between the predicted and the experimental value of fluorescence quantum yield is 0.00-4.60 %. Thus, the prediction model has the potential to predict the fluorescence quantum yield of carbon quantum dots in other types of farm waste biochar, and provides fundamental information for the study of biochar nanoparticles.

Ultrasensitive Photothermal Spectroscopy: Harnessing the Seebeck Effect for Attogram-Level Detection

Molecular-level spectroscopy is crucial for sensing and imaging applications, yet detecting and quantifying minuscule quantities of chemicals remain a challenge, especially when they surface adsorb in low numbers. Here, we introduce a photothermal spectroscopic technique that enables the high selectivity sensing of adsorbates with an attogram detection limit. Our approach utilizes the Seebeck effect in a microfabricated nanoscale thermocouple junction, incorporated into the apex of a microcantilever. We observe minimal thermal mass exhibited by the sensor, which maintains exceptional thermal insulation. The temperature variation driving the thermoelectric junction arises from the nonradiative decay of molecular adsorbates' vibrational states on the tip. We demonstrate the detection of photothermal spectra of physisorbed trinitrotoluene (TNT) and dimethyl methylphosphonate (DMMP) molecules, as well as representative polymers, with an estimated mass of 10-18 g.

Triboelectric-induced ion mobility for artificial intelligence-enhanced mid-infrared gas spectroscopy

Isopropyl alcohol molecules, as a biomarker for anti-virus diagnosis, play a significant role in the area of environmental safety and healthcare relating volatile organic compounds. However, conventional gas molecule detection exhibits dramatic drawbacks, like the strict working conditions of ion mobility methodology and weak light-matter interaction of mid-infrared spectroscopy, yielding limited response of targeted molecules. We propose a synergistic methodology of artificial intelligence-enhanced ion mobility and mid-infrared spectroscopy, leveraging the complementary features from the sensing signal in different dimensions to reach superior accuracy for isopropyl alcohol identification. We pull in "cold" plasma discharge from triboelectric generator which improves the mid-infrared spectroscopic response of isopropyl alcohol with good regression prediction. Moreover, this synergistic methodology achieves ~99.08% accuracy for a precise gas concentration prediction, even with interferences of different carbon-based gases. The synergistic methodology of artificial intelligence-enhanced system creates mechanism of accurate gas sensing for mixture and regression prediction in healthcare.

Unique Photoactivated Time-Resolved Response in 2D GeS for Selective Detection of Volatile Organic Compounds

Volatile organic compounds (VOCs) sensors have a broad range of applications including healthcare, process control, and air quality analysis. There are a variety of techniques for detecting VOCs such as optical, acoustic, electrochemical, and chemiresistive sensors. However, existing commercial VOC detectors have drawbacks such as high cost, large size, or lack of selectivity. Herein, a new sensing mechanism is demonstrated based on surface interactions between VOC and UV-excited 2D germanium sulfide (GeS), which provides an effective solution to distinguish VOCs. The GeS sensor shows a unique time-resolved electrical response to different VOC species, facilitating identification and qualitative measurement of VOCs. Moreover, machine learning is utilized to distinguish VOC species from their dynamic response via visualization with high accuracy. The proposed approach demonstrates the potential of 2D GeS as a promising candidate for selective miniature VOCs sensors in critical applications such as non-invasive diagnosis of diseases and health monitoring.

An Advanced Strategy to Enhance TENG Output: Reducing Triboelectric Charge Decay

The Internet of Things (IoT) is poised to accelerate the construction of smart cities. However, it requires more than 30 billion sensors to realize the IoT vision, posing great challenges and opportunities for industries of self-powered sensors. Triboelectric nanogenerator (TENG), an emerging new technology, is capable of easily converting energy from surrounding environment into electricity, thus TENG has tremendous application potential in self-powered IoT sensors. At present, TENG encounters a bottleneck to boost output for large-scale commercial use if just by promoting triboelectric charge generation, because the output is decided by the triboelectric charge dynamic equilibrium between generation and decay. To break this bottleneck, the strategy of reducing triboelectric charge decay to enhance TENG output is focused. First, multiple mechanisms of triboelectric charge decay are summarized in detail with basic theoretical principles for future research. Furthermore, recent advances in reducing triboelectric charge decay are thoroughly reviewed and outlined in three aspects: inhibition and application of air breakdown, simultaneous inhibition of air breakdown and triboelectric charge drift/diffusion, and inhibition of triboelectric charge drift/diffusion. Finally, challenges and future research focus are proposed. This review provides reference and guidance for enhancing TENG output.

Identification of Antibiotic Resistance in ESKAPE Pathogens through Plasmonic Nanosensors and Machine Learning

Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention and control of nosocomial infections. However, current techniques like genotype identification and antibiotic susceptibility testing are generally time-consuming and require large-scale equipment. Herein, we develop a rapid, facile, and sensitive technique to determine the antibiotic resistance phenotype among ESKAPE pathogens through plasmonic nanosensors and machine learning. Key to this technique is the plasmonic sensor array that contains gold nanoparticles functionalized with peptides differing in hydrophobicity and surface charge. The plasmonic nanosensors can interact with pathogens to generate bacterial fingerprints that alter the surface plasmon resonance (SPR) spectra of nanoparticles. In combination with machine learning, it enables the identification of antibiotic resistance among 12 ESKAPE pathogens in less than 20 min with an overall accuracy of 89.74%. This machine-learning-based approach allows for the identification of antibiotic-resistant pathogens from patients and holds great promise as a clinical tool for biomedical diagnosis.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

mHealth Technology as a Help Tool during Breast Cancer Treatment: A Content Focus Group

PURPOSE

To assess the usability and preferences of the contents of mHealth software developed for breast cancer patients as a tool to obtain patient-reported outcomes (PROMs), improve the patient's knowledge about the disease and its side effects, increase adherence to treatment, and facilitate communication with the doctor.

INTERVENTION

an mHealth tool called the Xemio app provides side effect tracking, social calendars, and a personalized and trusted disease information platform to deliver evidence-based advice and education for breast cancer patients.

METHOD

A qualitative research study using semi-structured focus groups was conducted and evaluated. This involved a group interview and a cognitive walking test using Android devices, with the participation of breast cancer survivors.

RESULTS

The ability to track side effects and the availability of reliable content were the main benefits of using the application. The ease of use and the method of interaction were the primary concerns; however, all participants agreed that the application would be beneficial to users. Finally, participants expressed their expectations of being informed by their healthcare providers about the launch of the Xemio app.

CONCLUSION

Participants perceived the need for reliable health information and its benefits through an mHealth app. Therefore, applications for breast cancer patients must be designed with accessibility as a key consideration.

Ratio Fluorescent Detecting of Tryptophan and Its Metabolite 5-Hydroxyindole-3-acetic Acid Relevant with Depression via Tb(III) Modified HOFs Hybrids: Further Designing Recyclable Molecular Logic Gate Connected by Back Propagation Neural Network

Exploring intelligent fluorescent materials with high reliability and precision to diagnose diseases is significant but remains a great challenge. Herein, based on coordination post-synthetic modification, a Tb³⁺ functionalized ME-PA (Tb@1) is prepared, which can emit brilliant green fluorescence through ligand-to-mental charge transfer-assisted energy transfer (LMCT-ET) process from ME-PA to Tb³⁺ ions. Tb@1 can simultaneously distinguish Tryptophan (Try) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA), two effective indicators for depression, in ratio and colorimetric mode. And this sensor behaves the advantages of high efficiency and sensitivity, as well as excellent reusability and anti-interference. The PET process from ME to Try and 5-HIAA, and the competitive absorption between analytes and Tb@1 may be relevant to sensing mechanism. In realistic serum or urine environment, the detection limits of Tb@1 for Try and 5-HIAA are 0.0183 and 0.0149 mg L^-1 respectively. Moreover, in conjunction with back propagation neural network (BPNN), two dual-output molecular logic gates that can be calculated circularly are further designed, which realizes intelligent control of the electronic component to identify the existence of two biomarkers and judge their concentrations from fluorescence images. This work offers a novel approach to modulate logic circuits based on ML-assisted HOF fluorescent sensor, with promising application for a precise and pictorial depression diagnosis.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Larger-Than-Unity External Optical Field Confinement Enabled by Metamaterial-Assisted Comb Waveguide for Ultrasensitive Long-Wave Infrared Gas Spectroscopy

Nanophotonic waveguides that implement long optical pathlengths on chips are promising to enable chip-scale gas sensors. Nevertheless, current absorption-based waveguide sensors suffer from weak interactions with analytes, limiting their adoptions in most demanding applications such as exhaled breath analysis and trace-gas monitoring. Here, we propose an all-dielectric metamaterial-assisted comb (ADMAC) waveguide to greatly boost the sensing capability. By leveraging large longitudinal electric field discontinuity at periodic high-index-contrast interfaces in the subwavelength grating metamaterial and its unique features in refractive index engineering, the ADMAC waveguide features strong field delocalization into the air, pushing the external optical field confinement factor up to 113% with low propagation loss. Our sensor operates in the important but underdeveloped long-wave infrared spectral region, where absorption fingerprints of plentiful chemical bonds are located. Acetone absorption spectroscopy is demonstrated using our sensor around 7.33 μm, showing a detection limit of 2.5 ppm with a waveguide length of only 10 mm.

Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy

Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.

Infrared spectroscopy with plasmonic nanoantennas is limited by small overlap between molecules and hot spots, and sharp resonance peaks. The authors demonstrate spectral multiplexing of hook nanoantennas with gradient dimensions as ultrasensitive vibrational probes in a continuous ultra-broadband region and utilize machine learning for enhanced sensing performance.

What can AI-TENG do for Low Abundance Biosensing?

Biosensing technology helps prevent, diagnose, and treat diseases and has attracted more and more researchers in recent years. Artificial intelligence-based triboelectric nanogenerators (AI-TENG) are promising for applications in biosensors due to their myriad of merits, including high efficiency and precision, low cost, light weight, and self-powered. This article aims to show how artificial intelligence and triboelectric nanogenerators have been combined to develop biosensors. We first focus on the working principle of triboelectric nanogenerators and the method of combining them with artificial intelligence. Secondly, we highlight the representative research work of AI-TENG in biomolecules sensing, organic compounds, and complex mixture of cells. Finally, this paper concludes with a summary and prospect on the existing challenges and possible solutions in the application of AI-TENG to the field of biosensors.

Skin bioelectronics towards long-term, continuous health monitoring

Skin bioelectronics are considered as an ideal platform for personalised healthcare because of their unique characteristics, such as thinness, light weight, good biocompatibility, excellent mechanical robustness, and great skin conformability. Recent advances in skin-interfaced bioelectronics have promoted various applications in healthcare and precision medicine. Particularly, skin bioelectronics for long-term, continuous health monitoring offer powerful analysis of a broad spectrum of health statuses, providing a route to early disease diagnosis and treatment. In this review, we discuss (1) representative healthcare sensing devices, (2) material and structure selection, device properties, and wireless technologies of skin bioelectronics towards long-term, continuous health monitoring, (3) healthcare applications: acquisition and analysis of electrophysiological, biophysical, and biochemical signals, and comprehensive monitoring, and (4) rational guidelines for the design of future skin bioelectronics for long-term, continuous health monitoring. Long-term, continuous health monitoring of advanced skin bioelectronics will open unprecedented opportunities for timely disease prevention, screening, diagnosis, and treatment, demonstrating great promise to revolutionise traditional medical practices.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.

Simultaneous Biomechanical and Biochemical Monitoring for Self-Powered Breath Analysis

The high moisture level of exhaled gases unavoidably limits the sensitivity of breath analysis via wearable bioelectronics. Inspired by pulmonary lobe expansion/contraction observed during respiration, a respiration-driven triboelectric sensor (RTS) was devised for simultaneous respiratory biomechanical monitoring and exhaled acetone concentration analysis. A tin oxide-doped polyethyleneimine membrane was devised to play a dual role as both a triboelectric layer and an acetone sensing material. The prepared RTS exhibited excellent ability in measuring respiratory flow rate (2-8 L/min) and breath frequency (0.33-0.8 Hz). Furthermore, the RTS presented good performance in biochemical acetone sensing (2-10 ppm range at high moisture levels), which was validated via finite element analysis. This work has led to the development of a novel real-time active respiratory monitoring system and strengthened triboelectric-chemisorption coupling sensing mechanism.

Flexible Plasmonic Biosensors for Healthcare Monitoring: Progress and Prospects

The noble metal nanoparticle has been widely utilized as a plasmonic unit to enhance biosensors, by leveraging its electric and/or optical properties. Integrated with the "flexible" feature, it further enables opportunities in developing healthcare products in a conformal and adaptive fashion, such as wrist pulse tracers, body temperature trackers, blood glucose monitors, etc. In this work, we present a holistic review of the recent advance of flexible plasmonic biosensors for the healthcare sector. The technical spectrum broadly covers the design and selection of a flexible substrate, the process to integrate flexible and plasmonic units, the exploration of different types of flexible plasmonic biosensors to monitor human temperature, blood glucose, ions, gas, and motion indicators, as well as their applications for surface-enhanced Raman scattering (SERS) and colorimetric detections. Their fundamental working principles and structural innovations are scoped and summarized. The challenges and prospects are articulated regarding the critical importance for continued progress of flexible plasmonic biosensors to improve living quality.

Recent Progress in the Energy Harvesting Technology-From Self-Powered Sensors to Self-Sustained IoT, and New Applications

With the fast development of energy harvesting technology, micro-nano or scale-up energy harvesters have been proposed to allow sensors or internet of things (IoT) applications with self-powered or self-sustained capabilities. Facilitation within smart homes, manipulators in industries and monitoring systems in natural settings are all moving toward intellectually adaptable and energy-saving advances by converting distributed energies across diverse situations. The updated developments of major applications powered by improved energy harvesters are highlighted in this review. To begin, we study the evolution of energy harvesting technologies from fundamentals to various materials. Secondly, self-powered sensors and self-sustained IoT applications are discussed regarding current strategies for energy harvesting and sensing. Third, subdivided classifications investigate typical and new applications for smart homes, gas sensing, human monitoring, robotics, transportation, blue energy, aircraft, and aerospace. Lastly, the prospects of smart cities in the 5G era are discussed and summarized, along with research and application directions that have emerged.

A Motion Capturing and Energy Harvesting Hybridized Lower-Limb System for Rehabilitation and Sports Applications

Lower-limb motion monitoring is highly desired in various application scenarios ranging from rehabilitation to sports training. However, there still lacks a cost-effective, energy-saving, and computational complexity-reducing solution for this specific demand. Here, a motion capturing and energy harvesting hybridized lower-limb (MC-EH-HL) system with 3D printing is demonstrated. It enables low-frequency biomechanical energy harvesting with a sliding block-rail piezoelectric generator (S-PEG) and lower-limb motion sensing with a ratchet-based triboelectric nanogenerator (R-TENG). A unique S-PEG is proposed with particularly designed mechanical structures to convert lower-limb 3D motion into 1D linear sliding on the rail. On the one hand, high output power is achieved with the S-PEG working at a very low frequency, which realizes self-sustainable systems for wireless sensing under the Internet of Things framework. On the other hand, the R-TENG gives rise to digitalized triboelectric output, matching the rotation angles to the pulse numbers. Additional physical parameters can be estimated to enrich the sensory dimension. Accordingly, demonstrative rehabilitation, human-machine interfacing in virtual reality, and sports monitoring are presented. This developed hybridized system exhibits an economic and energy-efficient solution to support the need for lower-limb motion tracking in various scenarios, paving the way for self-sustainable multidimensional motion tracking systems in near future.

Triboelectric Nanogenerator-Based Sensor Systems for Chemical or Biological Detection

The rapid advances in the Internet of things and wearable devices have created a massive platform for sensor systems that detect chemical or biological agents. The accelerated development of these devices in recent years has simultaneously aggravated the power supply problems. Triboelectric nanogenerators (TENGs) represent a thriving renewable energy technology with the potential to revolutionize this field. In this review, the significance of TENG-based sensor systems in chemical or biological detection from the perspective of the development of power supply for biochemical sensors is discussed. Further, a range of TENGs are classified according to their roles as power supplies and/or self-powered active sensors. The TENG powered sensor systems are further discussed on the basis of their framework and applications. The working principles and structures of different TENG-based self-powered active sensors are presented, along with the classification of the sensors based on these factors. In addition, some representative applications are introduced, and the corresponding challenges are discussed. Finally, some perspectives for the future innovations of TENG-based sensor systems for chemical/biological detection are discussed.

Self-powered technology based on nanogenerators for biomedical applications

Biomedical electronic devices have enormous benefits for healthcare and quality of life. Still, the long-term working of those devices remains a great challenge due to the short life and large volume of conventional batteries. Since the nanogenerators (NGs) invention, they have been widely used to convert various ambient mechanical energy sources into electrical energy. The self-powered technology based on NGs is dedicated to harvesting ambient energy to supply electronic devices, which is an effective pathway to conquer the energy insufficiency of biomedical electronic devices. With the aid of this technology, it is expected to develop self-powered biomedical electronic devices with advanced features and distinctive functions. The goal of this review is to summarize the existing self-powered technologies based on NGs and then review the applications based on self-powered technologies in the biomedical field during their rapid development in recent years, including two main directions. The first is the NGs as independent sensors to converts biomechanical energy and heat energy into electrical signals to reflect health information. The second direction is to use the electrical energy produced by NGs to stimulate biological tissues or powering biomedical devices for achieving the purpose of medical application. Eventually, we have analyzed and discussed the remaining challenges and perspectives of the field. We believe that the self-powered technology based on NGs would advance the development of modern biomedical electronic devices.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.