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

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.

Application of MOX Sensors to Determine the Emission of Volatile Compounds in Corn Groats as a Function of Vertical Pressure in the Silo and Moisture Content of the Bed

This study was focused on the analysis of the emission of volatile compounds as an indicator of changes in the quality degradation of corn groats with 14% and 17% moisture content (wet basis) using an electronic nose (Agrinose) at changing vertical pressure values. The corn groats were used in this study in an unconsolidated state of 0 kPa (the upper free layer of bulk material in the silo) and under a consolidation pressure of 40 kPa (approximately 3 m from the upper layer towards the bottom of the silo) and 80 kPa (approximately 6 m from the upper layer towards the bottom of the silo). The consolidation pressures corresponded to the vertical pressures acting on the layers of the bulk material bed in medium-slender and low silos. Chromatographic determinations of volatile organic compounds were performed as reference tests. The investigations confirmed the correlation of the electronic nose response with the quality degradation of the groats as a function of storage time. An important conclusion supported by the research results is that, based on the determined levels of intensity of volatile compound emission, the electronic nose is able to distinguish the individual layers of the bulk material bed undergoing different degrees of quality degradation.

Maximizing Triboelectric Nanogenerators by Physics-Informed AI Inverse Design

Triboelectric nanogenerators offer an environmentally friendly approach to harvesting energy from mechanical excitations. This capability has made them widely sought-after as an efficient, renewable, and sustainable energy source, with the potential to decrease reliance on traditional fossil fuels. However, developing triboelectric nanogenerators with specific output remains a challenge mainly due to the uncertainties associated with their complex designs for real-life applications. Artificial intelligence-enabled inverse design is a powerful tool to realize performance-oriented triboelectric nanogenerators. This is an emerging scientific direction that can address the concerns about the design and optimization of triboelectric nanogenerators leading to a next generation nanogenerator systems. This perspective paper aims at reviewing the principal analysis of triboelectricity, summarizing the current challenges of designing and optimizing triboelectric nanogenerators, and highlighting the physics-informed inverse design strategies to develop triboelectric nanogenerators. Strategic inverse design is particularly discussed in the contexts of expanding the four-mode analytical models by physics-informed artificial intelligence, discovering new conductive and dielectric materials, and optimizing contact interfaces. Various potential development levels of artificial intelligence-enhanced triboelectric nanogenerators are delineated. Finally, the potential of physics-informed artificial intelligence inverse design to propel triboelectric nanogenerators from prototypes to multifunctional intelligent systems for real-life applications is discussed.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

Boosting Output Performance of Sliding Mode Triboelectric Nanogenerator by Shielding Layer and Shrouded-Tribo-Area Optimized Ternary Electrification Layered Architecture

Sliding mode triboelectric nanogenerator (S-TENG) is effective for low-frequency mechanical energy harvesting owing to their more efficient mechanical energy extraction capability and easy packaging. Ternary electrification layered (TEL) architecture is proven useful for improving the output performance of S-TENG. However, the bottleneck of electric output is the air breakdown on the interface of tribo-layers, which seriously restricts its further improvement. Herein, a strategy is adopted by designing a shielding layer to prevent air breakdown on the central surface of tribo-layers. And the negative effects of air breakdown on the edge of sliding layer are averted by increasing the shrouded area of tribo-layers on slider. Output charge of this shielding-layer and shrouded-tribo-area optimized ternary electrification layered triboelectric nanogenerator (SS-TEL-TENG) achieves 3.59-fold enhancement of traditional S-TENG and 1.76-fold enhancement of TEL-TENG. Furthermore, even at a very low speed of 30 rpm, output charge, current, and average power of the rotation-type SS-TEL-TENG reach 4.15 µC, 74.9 µA, and 25.4 mW (2.05 W m-2 Hz-1 ), respectively. With such high-power output, 4248 LEDs can be lighted brightly by SS-TEL-TENG directly. The high-performance SS-TEL-TENG demonstrated in this work will have great applications for powering ubiquitous sensor network in the Internet of Things (IoT).

Amplified Performance of Charge Accumulation and Trapping Induced by Enhancing the Dielectric Constant via the Cyano Group of 3D-Structured Textile for a Triboelectric Multi-Modal Sensor

To further improve the output performance of triboelectric devices, reducing charge attenuation and loss has become a hot research topic. Particularly, textiles have emerged as one of the promising research directions for triboelectric devices owing to their special internal structure and large specific surface area. In the present work, polyacrylonitrile fibers are fabricated with two distinct structures to provide a higher dielectric constant due to the strong polar properties brought about by higher dipole moment of the CN group. In addition, the complex and closely connected structure of the textile increases specific internal surface area. As a friction layer, the output voltage is shown to increase to 625% of the initial value (from 8 to 60 V) after the application of friction for a short time due to accumulation property. When acting as a trapping layer, the charge loss after injection is effectively prevented due to excellent charge trapping effect. After 24 h, the triboelectric output performance remains at ≈70% of the initial value (decreasing from 320 to 220 V), which is more than 20 times that of the polytetrafluoroethylene film, which decreases from 125 to 19 V. The device is realized for the advanced application of multi-modal sensors.

Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration

Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.

Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform

The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.

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.

Intelligent Cubic-Designed Piezoelectric Node (iCUPE) with Simultaneous Sensing and Energy Harvesting Ability toward Self-Sustained Artificial Intelligence of Things (AIoT)

The evolution of artificial intelligence of things (AIoT) drastically facilitates the development of a smart city via comprehensive perception and seamless communication. As a foundation, various AIoT nodes are experiencing low integration and poor sustainability issues. Herein, a cubic-designed intelligent piezoelectric AIoT node iCUPE is presented, which integrates a high-performance energy harvesting and self-powered sensing module via a micromachined lead zirconate titanate (PZT) thick-film-based high-frequency (HF)-piezoelectric generator (PEG) and poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) nanofiber thin-film-based low-frequency (LF)-PEGs, respectively. The LF-PEG and HF-PEG with specific frequency up-conversion (FUC) mechanism ensures continuous power supply over a wide range of 10-46 Hz, with a record high power density of 17 mW/cm³ at 1 g acceleration. The cubic design allows for orthogonal placement of the three FUC-PEGs to ensure a wide range of response to vibrational energy sources from different directions. The self-powered triaxial piezoelectric sensor (TPS) combined with machine learning (ML) assisted three orthogonal piezoelectric sensing units by using three LF-PEGs to achieve high-precision multifunctional vibration recognition with resolutions of 0.01 g, 0.01 Hz, and 2° for acceleration, frequency, and tilting angle, respectively, providing a high recognition accuracy of 98%-100%. This work proves the feasibility of developing a ML-based intelligent sensor for accelerometer and gyroscope functions at resonant frequencies. The proposed sustainable iCUPE is highly scalable to explore multifunctional sensing and energy harvesting capabilities under diverse environments, which is essential for AIoT implementation.

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.

Fractal structured charge-excitation triboelectric nanogenerators for powering portable electronic devices

Effective power management on the outputs of triboelectric nanogenerators (TENGs) is critical for their practical applications due to the large impedance and unbalanced load matching. Recently proposed voltage multiplying circuits for external-charge excitation and self-charge excitation are usually unstable and require reversal for device restarting and common switched-capacitor-converters usually cause large switching losses. In this work, we fabricated a fractal structured charge-excitation circuit for TENGs using diodes and capacitors. The fractal switched-capacitor-converter coupled with voltage regulator diodes can greatly boost the output charge and current of the TENG without reverse starting. The managed output performance of the TENG can be controlled by the electronic component parameters and external operating frequency. Through the component and condition optimization, the fractal structured charge-excitation TENG (FSC-TENG) can achieve nearly 5.8 times charge boosting and almost 16.8 times power boosting in the pulsed mode. Furthermore, the FSC-TENG successfully drove a hygrothermograph and was integrated into a yoga mat for harvesting human-body motion energy to power an electronic watch and a pedometer. The FSC-TENG with good charge accumulation properties and stability is a promising candidate for practical self-powered applications.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Triboelectric Nanogenerators as Power Sources for Chemical Sensors and Biosensors

The recent advances of portable sensors in flexible and wearable form factors are drawing increasing attention worldwide owing to their requirement applications ranging from health monitoring to environment monitoring. While portability is critical for these applications, real-time data gathering also requires a reliable power supply-which is largely met with batteries. Besides the need for regular charging, the use of toxic chemicals in batteries makes it difficult to rely on them, and as a result different types of energy harvesters have been explored in recent years. Among these, triboelectric nanogenerators (TENGs) provide a promising platform for harnessing ambient energy and converting it into usable electric signals. The ease of fabrication and possibility to develop TENGs with a diverse range of easily available materials also make them attractive. This review focuses on the TENG technology and its efficient use as a power source for various types of chemical sensors and biosensors. The paper describes the underlying mechanism, various modes of working of TENGs, and representative examples of their utilization as power sources for sensing a multitude of analytes. The challenges associated with their adoption for commercial solutions are also discussed to stimulate further advances and innovations.

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.

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

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.

Interface Defect Detection and Identification of Triboelectric Nanogenerators via Voltage Waveforms and Artificial Neural Network

To provide a robust working environment for TENGs, most TENGs are designed as sealed structures that isolate TENGs from the external environment, and thus their operating conditions cannot be directly monitored. Here, for the first time, we propose an artificial neural network for interface defect detection and identification of triboelectric nanogenerators via training voltage waveforms. First, interface defects of TENGs are classified and their causes are discussed in detail. Then we build a lightweight artificial neural network model which shows high sensitivity to voltage waveforms and low time complexity. The model takes 2.1 s for training one epoch, and the recognition rate of defect detection is 98.9% after 100 epochs. Meanwhile, the model successfully demonstrates the learning ability for low-resolution samples (100 × 75 pixels), which can identify six types of TENG defects, such as edge fracture, adhesion, and abnormal vibration, with a high recognition rate of 93.6%. This work provides a new strategy for the fault diagnosis and intelligent application of TENGs.

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion

The increasing population of the elderly and motion-impaired people brings a huge challenge to our social system. However, the walking stick as their essential tool has rarely been investigated into its potential capabilities beyond basic physical support, such as activity monitoring, tracing, and accident alert. Here, we report a walking stick powered by ultra-low-frequency human motion and equipped with deep-learning-enabled advanced sensing features to provide a healthcare-monitoring platform for motion-impaired users. A linear-to-rotary structure is designed to achieve highly efficient energy harvesting from the linear motion of a walking stick with ultralow frequency. Besides, two kinds of self-powered triboelectric sensors are proposed and integrated to extract the motion features of the walking stick. Augmented sensing functionalities with high accuracies have been enabled by deep-learning-based data analysis, including identity recognition, disability evaluation, and motion status distinguishing. Furthermore, a self-sustainable Internet of Things (IoT) system with global positioning system tracing and environmental temperature and humidity amenity sensing functions is obtained. Combined with the aforementioned functionalities, this walking stick is demonstrated in various usage scenarios as a caregiver for real-time well-being status and activity monitoring. The caregiving walking stick shows the potential of being an intelligent aid for motion-impaired users to help them live life with adequate autonomy and safety.

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 flexible virtual sensor array based on laser-induced graphene and MXene for detecting volatile organic compounds in human breath

Detecting volatile organic compounds (VOCs) in human breath is critical for the early diagnosis of diseases. Good selectivity of VOC sensors is crucial for the accurate analysis of VOC biomarkers in human breath, which consists of more than 200 types of VOCs. In this paper, a flexible virtual sensor array (FVSA) was proposed based on a sensing layer of MXene and laser-induced graphene interdigital electrodes (LIG-IDEs) for detecting VOCs in exhaled human breath. The fabrication of LIG-IDEs avoids the costly and complicated procedures required for the preparation of traditional IDEs. The FVSA's responses of multiple parameters help build a unique fingerprint for each VOC, without a need for changing the temperature of the sensing element, which is commonly used in the VSA of semiconductor VOC sensors. Based on machine learning algorithms, we have achieved highly precise recognition of different VOCs and mixtures and accurate prediction (accuracy of 89.1%) of the objective VOC's concentration in variable backgrounds using this proposed FVSA. Moreover, a blind analysis validates the capacity of the FVSA to identify alcohol content in human breath with an accuracy of 88.9% using breath samples from volunteers before and after alcohol consumption. These results show that the proposed FVSA is promising for the detection of VOC biomarkers in human exhaled breath and early diagnosis of diseases.