Progress in micro/nano sensors and nanoenergy for future AIoT-based smart home applications

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.

Formation of sub-100-nm suspended nanowires with various materials using thermally adjusted electrospun nanofibers as templates

The air suspension and location specification properties of nanowires are crucial factors for optimizing nanowires in electronic devices and suppressing undesirable interactions with substrates. Although various strategies have been proposed to fabricate suspended nanowires, placing a nanowire in desired microstructures without material constraints or high-temperature processes remains a challenge. In this study, suspended nanowires were formed using a thermally aggregated electrospun polymer as a template. An elaborately designed microstructure enables an electrospun fiber template to be formed at the desired location during thermal treatment. Moreover, the desired thickness of the nanowires is easily controlled with the electrospun fiber templates, resulting in the parallel formation of suspended nanowires that are less than 100 nm thick. Furthermore, this approach facilitates the formation of suspended nanowires with various materials. This is accomplished by evaporating various materials onto the electrospun fiber template and by removing the template. Palladium, copper, tungsten oxide (WO₃), and tin oxide nanowires are formed as examples to demonstrate the advantage of this approach in terms of nanowire material selection. Hydrogen (H₂) and nitrogen dioxide (NO₂) gas sensors comprising palladium and tungsten oxide, respectively, are demonstrated as exemplary devices of the proposed method.

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.

Deep Learning Methods for Space Situational Awareness in Mega-Constellations Satellite-Based Internet of Things Networks

Artificial Intelligence of things (AIoT) is the combination of Artificial Intelligence (AI) technologies and the Internet of Things (IoT) infrastructure. AI deals with the devices' learning process to acquire knowledge from data and experience, while IoT concerns devices interacting with each other using the Internet. AIoT has been proven to be a very effective paradigm for several existing applications as well as for new areas, especially in the field of satellite communication systems with mega-constellations. When AIoT meets space communications efficiently, we have interesting uses of AI for Satellite IoT (SIoT). In fact, the number of space debris is continuously increasing as well as the risk of space collisions, and this poses a significant threat to the sustainability and safety of space operations that must be carefully and efficiently addressed to avoid critical damage to the SIoT networks. This paper aims to provide a systematic survey of the state of the art, challenges, and perspectives on the use of deep learning methods for space situational awareness (SSA) object detection and classification. The contributions of this paper can be summarized as follows: (i) we outline using AI algorithms, and in particular, deep learning (DL) methods, the possibility of identifying the nature/type of spatial objects by processing signals from radars; (ii) we present a comprehensive taxonomy of DL-based methods applied to SSA object detection and classification, as well as their characteristics, and implementation issues.

Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

Letter handwriting, especially stroke correction, is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public. In this study, a biodegradable and conductive carboxymethyl chitosan-silk fibroin (CSF) film is prepared to design wearable triboelectric nanogenerator (denoted as CSF-TENG), which outputs of V_oc ≈ 165 V, I_sc ≈ 1.4 μA, and Q_sc ≈ 72 mW cm^-2. Further, in vitro biodegradation of CSF film is performed through trypsin and lysozyme. The results show that trypsin and lysozyme have stable and favorable biodegradation properties, removing 63.1% of CSF film after degrading for 11 days. Further, the CSF-TENG-based human-machine interface (HMI) is designed to promptly track writing steps and access the accuracy of letters, resulting in a straightforward communication media of human and machine. The CSF-TENG-based HMI can automatically recognize and correct three representative letters (F, H, and K), which is benefited by HMI system for data processing and analysis. The CSF-TENG-based HMI can make decisions for the next stroke, highlighting the stroke in advance by replacing it with red, which can be a candidate for calligraphy practice and correction. Finally, various demonstrations are done in real-time to achieve virtual and real-world controls including writing, vehicle movements, and healthcare.

Mechanically Ultra-Robust, Elastic, Conductive, and Multifunctional Hybrid Hydrogel for a Triboelectric Nanogenerator and Flexible/Wearable Sensor

Flexibility/wearable electronics such as strain/pressure sensors in human-machine interactions (HMI) are highly developed nowadays. However, challenges remain because of the lack of flexibility, fatigue resistance, and versatility, leading to mechanical damage to device materials during practical applications. In this work, a triple-network conductive hydrogel is fabricated by combining 2D Ti₃ C₂ T_x nanosheets with two kinds of 1D polymer chains, polyacrylamide, and polyvinyl alcohol. The Ti₃ C₂ T_x nanosheets act as the crosslinkers, which combine the two polymer chains of PAM and PVA via hydrogen bonds. Such a unique structure endows the hydrogel (MPP-hydrogel) with merits such as mechanical ultra-robust, super-elasticity, and excellent fatigue resistance. More importantly, the introduced Ti₃ C₂ T_x nanosheets not only enhance the hydrogel's conductivity but help form double electric layers (DELs) between the MXene nanosheets and the free water molecules inside the MPP-hydrogel. When the MPP-hydrogel is used as the electrode of the triboelectric nanogenerator (MPP-TENG), due to the dynamic balance of the DELs under the initial potential difference generated from the contact electrification as the driving force, an enhanced electrical output of the TENG is generated. Moreover, flexible strain/pressure sensors for tiny and low-frequency human motion detection are achieved. This work demonstrates a promising flexible electronic material for e-skin and HMI.

Flexible multifunctional platform based on piezoelectric acoustics for human-machine interaction and environmental perception

Flexible human-machine interfaces show broad prospects for next-generation flexible or wearable electronics compared with their currently available bulky and rigid counterparts. However, compared to their rigid counterparts, most reported flexible devices (e.g., flexible loudspeakers and microphones) show inferior performance, mainly due to the nature of their flexibility. Therefore, it is of great significance to improve their performance by developing and optimizing new materials, structures and design methodologies. In this paper, a flexible acoustic platform based on a zinc oxide (ZnO) thin film on an aluminum foil substrate is developed and optimized; this platform can be applied as a loudspeaker, a microphone, or an ambient sensor depending on the selection of its excitation frequencies. When used as a speaker, the proposed structure shows a high sound pressure level (SPL) of ~90 dB (with a standard deviation of ~3.6 dB), a low total harmonic distortion of ~1.41%, and a uniform directivity (with a standard deviation of ~4 dB). Its normalized SPL is higher than those of similar devices reported in the recent literature. When used as a microphone, the proposed device shows a precision of 98% for speech recognition, and the measured audio signals show a strong similarity to the original audio signals, demonstrating its equivalent performance compared to a rigid commercial microphone. As a flexible sensor, this device shows a high temperature coefficient of frequency of -289 ppm/K and good performance for respiratory monitoring.

Recent Progress on Triboelectric Nanogenerators for Vibration Energy Harvesting and Vibration Sensing

The triboelectric nanogenerator (TENG) is a recent technology that reforms kinetic energy generation and motion sensing. A TENG comes with variety of structures and mechanisms that make it suitable for wide range of applications and working conditions. Since mechanical vibrations are abundant source of energy in the surrounding environment, the development of a TENG for vibration energy harvesting and vibration measurements has attracted a huge attention and great research interest through the past two decades. Due to the high output voltage and high-power density of a TENG, it can be used as a sustainable power supply for small electronics, smart devices, and wireless sensors. In addition, it can work as a vibration sensor with high sensitivity. This article reviews the recent progress in the development of a TENG for vibration energy harvesting and vibration measurements. Systems of only a TENG or a hybrid TENG with other transduction technologies, such as piezoelectric and electromagnetic, can be utilized for vibrations scavenging. Vibration measurement can be done by measuring either vibration displacement or vibration acceleration. Each can provide full information about the vibration amplitude and frequency. Some TENG vibration-sensing architectures may also be used for energy harvesting due to their large output power. Numerous applications can rely on TENG vibration sensors such as machine condition monitoring, structure health monitoring, and the Internet of things (IoT).

Textile-type triboelectric nanogenerator using Teflon wrapping wires as wearable power source

Wearable electronic devices such as mobile communication devices, portable computers, and various sensors are the latest significant innovations in technology which use the Internet of Things (IoT) to track personal data. Wearable energy harvesters are required to supply electricity to such devices for the convenience of users. In this study, a textile-type triboelectric nanogenerator (T-TENG), produced using commercial electrode fibers, was fabricated to generate electrical energy using external mechanical stimulation. The commercial fiber was an electrode coated with Teflon on a copper wire with a diameter of ~ 320 μm. Using this commercial fiber, a T-TENG was easily fabricated by knitting and weaving. The performance of the T-TENG was analyzed to understand the effect of force and frequency. It was observed that the performance of the T-TENG did not degrade even under harsh conditions and treatment. The textile-type TENG possessed an energy harvesting capability with an output power density of ~ 0.36 W/m2 and could operate electronic devices by charging a capacitor.

Virtual Sensor Array Based on Piezoelectric Cantilever Resonator for Identification of Volatile Organic Compounds

Piezoelectric cantilever resonator is one of the most promising platforms for real-time sensing of volatile organic compounds (VOCs). However, it has been a great challenge to eliminate the cross-sensitivity of various VOCs for these cantilever-based VOC sensors. Herein, a virtual sensor array (VSA) is proposed on the basis of a sensing layer of GO film deposited onto an AlN piezoelectric cantilever with five groups of top electrodes for identification of various VOCs. Different groups of top electrodes are applied to obtain high amplitudes of multiple resonance peaks for the cantilever, thus achieving low limits of detection (LODs) to VOCs. Frequency shifts of multiple resonant modes and changes of impedance values are taken as the responses of the proposed VSA to VOCs, and these multidimensional responses generate a unique fingerprint for each VOC. On the basis of machine learning algorithms, the proposed VSA can accurately identify different types of VOCs and mixtures with accuracies of 95.8 and 87.5%, respectively. Furthermore, the VSA has successfully been applied to identify the emissions from healthy plants and "plants with late blight" with an accuracy of 89%. The high levels of identifications show great potentials of the VSA for diagnosis of infectious plant diseases by detecting VOC biomarkers.

Advanced Implantable Biomedical Devices Enabled by Triboelectric Nanogenerators

Implantable biomedical devices (IMDs) play essential roles in healthcare. Subject to the limited battery life, IMDs cannot achieve long-term in situ monitoring, diagnosis, and treatment. The proposal and rapid development of triboelectric nanogenerators free IMDs from the shackles of batteries and spawn a self-powered healthcare system. This review aims to overview the development of IMDs based on triboelectric nanogenerators, divided into self-powered biosensors, in vivo energy harvesting devices, and direct electrical stimulation therapy devices. Meanwhile, future challenges and opportunities are discussed according to the development requirements of current-level self-powered IMDs to enhance output performance, develop advanced triboelectric nanogenerators with multifunctional materials, and self-driven close-looped diagnosis and treatment systems.

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.

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.

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.