Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

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.

Thin-Film Piezoelectric Micromachined Ultrasound Transducers in Biomedical Applications: A Review.

Thin-film piezoelectric micromachined ultrasound transducers (PMUTs) are an increasingly relevant and well-researched field, and their biomedical importance has been growing as the technology continues to mature. This review paper briefly discusses their history in biomedical use, provides a simple explanation of their principles for newer readers, and sheds light on the materials selection for these devices. Primarily, it discusses the significant applications of PMUTs in the biomedical industry and showcases recent progress that has been made in each application. The biomedical applications covered include common historical uses of ultrasound such as ultrasound imaging, ultrasound therapy, and fluid sensing, but additionally new and upcoming applications such as drug delivery, photoacoustic imaging, thermoacoustic imaging, biometrics, and intrabody communication. By including a device comparison chart for different applications, this review aims to assist MEMS designers that work with PMUTs by providing a benchmark for recent research works. Furthermore, it puts forth a discussion on the current challenges being faced by PMUTs in the biomedical field, current and likely future research trends, and opportunities for PMUT development areas, as well as sharing the opinions and predictions of the authors on the state of this technology as a whole. The review aims to be a comprehensive introduction to these topics without diving excessively deep into existing literature.

Ultra-compact and high-performance suspended aluminum scandium nitride Lamb wave humidity sensor with a graphene oxide layer.

The utilization of Microelectromechanical Systems (MEMS) technology holds great significance for developing compact and high-performance humidity sensors in human healthcare, and the Internet of Things. However, several drawbacks of the current MEMS humidity sensors limit their applications, including their long response time, low sensitivity, relatively large sensing area, and incompatibility with a complementary metal-oxide-semiconductor (CMOS) process. To address these problems, a suspended aluminum scandium nitride (AlScN) Lamb wave humidity sensor utilizing a graphene oxide (GO) layer is firstly designed and fabricated. The theoretical and experimental results both show that the AlScN Lamb wave humidity sensor exhibits high sensing performance. The mass loading sensitivity of the sensor is one order higher than that of the normal surface acoustic wave (SAW) humidity sensor based on an aluminum nitride (AlN) film; thus the AlScN Lamb wave humidity sensor achieves high sensitivity (∼41.2 ppm per % RH) with only an 80 nm-thick GO film. In particular, the as-prepared suspended AlScN Lamb wave sensors are able to respond to the wide relative humidity (0-80% RH) change in 2 s, and the device size is ultra-compact (260 µm × 72 µm). Moreover, the sensor has an excellent linear response in the 0-80% RH range, great repeatability and long-term stability. Therefore, this work brings opportunities for the development of ultra-compact and high-performance humidity sensors.

Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator.

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves (GFWs) generated by a membrane acoustic waveguide actuator (MAWA). By harnessing the potential of GFWs, cavity-agnostic advanced particle manipulation functions are achieved, unlocking new avenues for microfluidic systems and lab-on-a-chip development. The localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising characteristics. Unlike traditional acoustofluidic technologies, the GFWs propagating on the MAWA's membrane waveguide allow for cavity-agnostic particle manipulation, irrespective of the resonant properties of the fluidic chamber. Moreover, the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane waveguide. Experimental demonstrations using two types of particles include in-sessile-droplet particle transport, mixing, and spatial separation based on particle diameter, along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS channels. These experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA's compatibility with existing microfluidic systems.

Adjustment methods of Schottky barrier height in one- and two-dimensional semiconductor devices.

The Schottky contact which is a crucial interface between semiconductors and metals is becoming increasingly significant in nano-semiconductor devices. A Schottky barrier, also known as the energy barrier, controls the depletion width and carrier transport across the metal-semiconductor interface. Controlling or adjusting Schottky barrier height (SBH) has always been a vital issue in the successful operation of any semiconductor device. This review provides a comprehensive overview of the static and dynamic adjustment methods of SBH, with a particular focus on the recent advancements in nano-semiconductor devices. These methods encompass the work function of the metals, interface gap states, surface modification, image-lowering effect, external electric field, light illumination, and piezotronic effect. We also discuss strategies to overcome the Fermi-level pinning effect caused by interface gap states, including van der Waals contact and 1D edge metal contact. Finally, this review concludes with future perspectives in this field.

Triboelectric Nanogenerator-Enabled Digital Twins in Civil Engineering Infrastructure 4.0: A Comprehensive Review.

The emergence of digital twins has ushered in a new era in civil engineering with a focus on achieving sustainable energy supply, real-time sensing, and rapid warning systems. These key development goals mean the arrival of Civil Engineering 4.0.The advent of triboelectric nanogenerators (TENGs) demonstrates the feasibility of energy harvesting and self-powered sensing. This review aims to provide a comprehensive analysis of the fundamental elements comprising civil infrastructure, encompassing various structures such as buildings, pavements, rail tracks, bridges, tunnels, and ports. First, an elaboration is provided on smart engineering structures with digital twins. Following that, the paper examines the impact of using TENG-enabled strategies on smart civil infrastructure through the integration of materials and structures. The various infrastructures provided by TENGs have been analyzed to identify the key research interest. These areas encompass a wide range of civil infrastructure characteristics, including safety, efficiency, energy conservation, and other related themes. The challenges and future perspectives of TENG-enabled smart civil infrastructure are briefly discussed in the final section. In conclusion, it is conceivable that in the near future, there will be a proliferation of smart civil infrastructure accompanied by sustainable and comprehensive smart services.

Multimodal Sensors Enabled Autonomous Soft Robotic System with Self-Adaptive Manipulation.

Human hands are amazingly skilled at recognizing and handling objects of different sizes and shapes. To date, soft robots rarely demonstrate autonomy equivalent to that of humans for fine perception and dexterous operation. Here, an intelligent soft robotic system with autonomous operation and multimodal perception ability is developed by integrating capacitive sensors with triboelectric sensor. With distributed multiple sensors, our robot system can not only sense and memorize multimodal information but also enable an adaptive grasping method for robotic positioning and grasp control, during which the multimodal sensory information can be captured sensitively and fused at feature level for crossmodally recognizing objects, leading to a highly enhanced recognition capability. The proposed system, combining the performance and physical intelligence of biological systems (i.e., self-adaptive behavior and multimodal perception), will greatly advance the integration of soft actuators and robotics in many fields.

Advances and Applications of Metal-Organic Frameworks (MOFs) in Emerging Technologies: A Comprehensive Review.

Metal-organic frameworks (MOFs) that are the wonder material of the 21st century consist of metal ions/clusters coordinated to organic ligands to form one- or more-dimensional porous structures with unprecedented chemical and structural tunability, exceptional thermal stability, ultrahigh porosity, and a large surface area, making them an ideal candidate for numerous potential applications. In this work, the recent progress in the design and synthetic approaches of MOFs and explore their potential applications in the fields of gas storage and separation, catalysis, magnetism, drug delivery, chemical/biosensing, supercapacitors, rechargeable batteries and self-powered wearable sensors based on piezoelectric and triboelectric nanogenerators are summarized. Lastly, this work identifies present challenges and outlines future opportunities in this field, which can provide valuable references.

All-organic transparent plant e-skin for noninvasive phenotyping.

Real-time in situ monitoring of plant physiology is essential for establishing a phenotyping platform for precision agriculture. A key enabler for this monitoring is a device that can be noninvasively attached to plants and transduce their physiological status into digital data. Here, we report an all-organic transparent plant e-skin by micropatterning poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on polydimethylsiloxane (PDMS) substrate. This plant e-skin is optically and mechanically invisible to plants with no observable adverse effects to plant health. We demonstrate the capabilities of our plant e-skins as strain and temperature sensors, with the application to Brassica rapa leaves for collecting corresponding parameters under normal and abiotic stress conditions. Strains imposed on the leaf surface during growth as well as diurnal fluctuation of surface temperature were captured. We further present a digital-twin interface to visualize real-time plant surface environment, providing an intuitive and vivid platform for plant phenotyping.

Fiber-Based Noncontact Sensor with Stretchability for Underwater Wearable Sensing and VR Applications.

The rapid development of artificial intelligent wearable devices has led to an increasing need for seamless information exchange between humans, machines, and virtual spaces, often relying on touch sensors as the primary interaction medium. Additionally, the demand for underwater detection technologies is on the rise owing to the prevalent wet and submerged environment. Here, a fiber-based capacitive sensor with superior stretchability and hydrophobicity is proposed, designed to cater to noncontact and underwater applications. The sensor is constructed using bacterial cellulose (BC)@BC/carbon nanotubes (CNTs) (BBT) helical fiber as the matrix and methyltrimethoxysilane (MTMS) as the hydrophobic modified agent, forming a hydrophobic silylated BC@BC/CNT (SBBT) helical fiber by the chemical vapor deposition (CVD) technique. These fibers exhibit an impressive contact angle of 132.8°. The SBBT helicalfiber-based capacitive sensor presents capabilities for both noncontact and underwater sensing, which exhibits a significant capacitance change of -0.27 (at a distance of 0.5 cm). We have achieved interactive control between real space and virtual space through intelligent data analysis technology with minimal interference from the presence of water. This work has laid a solid foundation of noncontact sensing with attributes such as degradability, stretchability, and hydrophobicity. Moreover, it offers promising solutions for barrier-free communication in virtual reality (VR) and underwater applications, providing avenues for smart human-machine interfaces for submerged use.

Dynamic construction of refractive index-dependent vibrations using surface plasmon-phonon polaritons.

One of the fundamental hurdles in infrared spectroscopy is the failure of molecular identification when their infrared vibrational fingerprints overlap. Refractive index (RI) is another intrinsic property of molecules associated with electronic polarizability, but with limited contribution to molecular identification in mixed environments currently. Here, we investigate the coupling mode of localized surface plasmon and surface phonon polaritons for vibrational de-overlapping. The coupling mode is sensitive to the molecular refractive index, attributed to the RI-induced vibrational variations of surface phonon polaritons (SPhP) within the Reststrahlen band, referred to as RI-dependent SPhP vibrations. The RI-dependent SPhP vibrations are linked to molecular RI features. According to the deep-learning-augmented demonstration of bond-breaking-bond-making dynamic profiling in biological reaction, we substantiate that the RI-dependent SPhP vibrations effectively disentangle overlapping vibrational modes, achieving a 92% identification accuracy even for the strongly overlapping vibrational modes in the reaction. Our findings offer insights into the realm of light-matter interaction and provide a valuable toolkit for biomedicine applications.

A Natural Gradient Biological-Enabled Multimodal Triboelectric Nanogenerator for Driving Safety Monitoring.

A key element to ensuring driving safety is to provide a sufficient braking distance. Inspired by the nature triply periodic minimal surface (TPMS), a gradient and multimodal triboelectric nanogenerator (GM-TENG) is proposed with high sensitivity and excellent multimodal monitoring. The gradient TPMS structure exhibits the multi-stage stress-strain properties of typical porous metamaterials. Significantly, the multimodal monitoring capability depends on the implicit function of the defined level constant c, which directly contributes to the multimodal driving safety monitoring. The mechanical and electrical responsive behavior of the GM-TENG is analyzed to identify the applied speed, load, and working mode. In addition, optimized peak open-circuit voltage (Voc) is demonstrated for self-awareness of the braking condition. The braking distance factor (L) is conceived to construct the self-aware equation of the friction coefficient based on the integration of Voc with respect to time. Importantly, R-squared up to 94.29 % can be obtained, which improves self-aware accuracy and real-time capabilities. This natural structure and self-aware device provide an effective strategy to improve driving safety, which contributes to the improvement of road safety and presents self-powered sensing with potential applications in an intelligent transportation system.

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.

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.

Evolution of Thermoelectric Generators: From Application to Hybridization.

Considerable thermal energy is emitted into the environment from human activities and equipment operation in the course of daily production. Accordingly, the use of thermoelectric generators (TEGs) can attract wide interest, and it shows high potential in reducing energy waste and increasing energy recovery rates. Notably, TEGs have aroused rising attention and been significantly boosted over the past few years, as the energy crisis has worsened. The reason for their progress is that thermoelectric generators can be easily attached to the surface of a heat source, converting heat energy directly into electricity in a stable and continuous manner. In this review, applications in wearable devices, and everyday life are reviewed according to the type of structure of TEGs. Meanwhile, the latest progress of TEGs' hybridization with triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), and photovoltaic effect is introduced. Moreover, prospects and suggestions for subsequent research work are proposed. This review suggests that hybridization of energy harvesting, and flexible high-temperature thermoelectric generators are the future trends.

Intelligent soft robotic fingers with multi-modality perception ability.

In the context of industry 4.0, automatic sorting is becoming prevalent in production lines. Herein, we developed a bionic sensing system to achieve real-time object recognition. The system consists of 9 single-layer triboelectric nanogenerators (SL-TENGs) as touch sensors and 3 comb-shaped TENGs (CS-TENGs) as bending sensors, with a sensitivity of 110 V/kPa and stable output after 20,000 press cycles. These sensors were attached to a manipulator composed of three soft actuators, serving as soft robotic fingers. An enhanced electrical output of these sensors was achieved successfully, demonstrating their feasibility in detecting grasping location, contact pressure, and bending curvature. A one-dimensional convolutional neural network (1D-CNN) with 98.96% accuracy extracted information from the sensors, enabling the manipulator to serve as an intelligent sensing system with multi-modality perception ability. This robotic manipulator successfully integrated TENG-based self-powered sensors, soft actuators, and artificial intelligence, demonstrating the potential for future digital twin applications, particularly in automatic component sorting.

Thin-film PMUTs: a review of over 40 years of research.

Thin-film PMUTs have been important research topics among microultrasound experts, and a concise review on their research progress is reported herein. Through rigorous surveying, scrutinization, and perception, it has been determined that the work in this field began nearly 44 years ago with the primitive development of functional piezoelectric thin-film materials. To date, there are three major companies commercializing thin-film PMUTs on a bulk scale. This commercialization illustrates the extensive contributions made by more than 70 different centers, research institutes, and agencies across 4 different continents regarding the vast development of these devices' design, manufacturing, and function. This review covers these important contributions in a short yet comprehensive manner; in particular, this paper educates readers about the global PMUT outlook, their governing design principles, their manufacturing methods, nonconventional yet useful PMUT designs, and category-wise applications. Crucial comparison charts of thin-film piezoelectric material used in PMUTs, and their categorically targeted applications are depicted and discussed to enlighten any MEMS designer who plans to work with PMUTs. Moreover, each relevant section features clear future predictions based on the author's past knowledge and expertise in this field of research and on the findings of a careful literature survey. In short, this review is a one-stop time-efficient guide for anyone interested in learning about these small devices.

Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism.

Circular dichroism (CD) spectroscopy has been widely demonstrated for detecting chiral molecules. However, the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging task. To solve this problem, we report an enhanced vibrational circular dichroism (VCD) sensing platform based on plasmonic chiral metamaterials, which presents a 6-magnitude signal enhancement with a selectivity of chiral molecules. Guided by coupled-mode theory, we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process, which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss, resulting in improved chiral light-matter interaction and enhanced molecular VCD signals. Besides, we demonstrate the thin-film sensing process of BSA and ß-lactoglobulin proteins, which contain secondary structures α-helix and ß-sheet and achieve a limit of detection down to zeptomole level. Furthermore, we also, for the first time, explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures, where the mixing ratio can be successfully differentiated with our proposed chiral metamaterials. Our findings improve the sensing signal of molecules and expand the extractable information, paving the way toward label-free, compact, small-volume chiral molecule detection for stereochemical and clinical diagnosis applications.

Ultrasensitive Molecular Fingerprint Retrieval Using Strongly Detuned Overcoupled Plasmonic Nanoantennas.

Tailoring light-matter interactions via plasmonic nanoantennas (PNAs) has emerged as a breakthrough technology for spectroscopic applications. The detuning between molecular vibrations and plasmonic resonances, as a fundamental and inevitable optical phenomenon in light-matter interactions, reduces the interaction efficiency, resulting in a weak molecule sensing signal at the strong detuning state. Here, it is demonstrated that the low interaction efficiency from detuning can be tackled by overcoupled PNAs (OC-PNAs) with a high ratio of the radiative to intrinsic loss rates, which can be used for ultrasensitive spectroscopy at strong plasmonic-molecular detuning. In OC-PNAs, the ultrasensitive molecule signals are achieved within a wavelength detuning range of 248 cm-1 , which is 173 cm-1 wider than previous works. Meanwhile, the OC-PNAs are immune to the distortion of molecular signals and maintain a lineshape consistent with the molecular signature fingerprint. This strategy allows a single device to enhance and capture the full and complex fingerprint vibrations in the mid-infrared range. In the proof-of-concept demonstration, 13 kinds of molecules with some vibration fingerprints strongly detuning by the OC-PNAs are identified with 100% accuracy with the assistance of machine-learning algorithms. This work gains new insights into detuning-state nanophotonics for potential applications including spectroscopy and sensors.