Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600 µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80 °C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Development of Photonic In-Sensor Computing Based on a Mid-Infrared Silicon Waveguide Platform.

Neuromorphic in-sensor computing has provided an energy-efficient solution to smart sensor design and on-chip data processing. In recent years, various free-space-configured optoelectronic chips have been demonstrated for on-chip neuromorphic vision processing. However, on-chip waveguide-based in-sensor computing with different data modalities is still lacking. Here, by integrating a responsivity-tunable graphene photodetector onto the silicon waveguide, an on-chip waveguide-based in-sensor processing unit is realized in the mid-infrared wavelength range. The weighting operation is achieved by dynamically tuning the bias of the photodetector, which could reach 4 bit weighting precision. Three different neural network tasks are performed to demonstrate the capabilities of our device. First, image preprocessing is performed for handwritten digits and fashion product classification as a general task. Next, resistive-type glove sensor signals are reversed and applied to the photodetector as an input for gesture recognition. Finally, spectroscopic data processing for binary gas mixture classification is demonstrated by utilizing the broadband performance of the device from 3.65 to 3.8 µm. By extending the wavelength from near-infrared to mid-infrared, our work shows the capability of a waveguide-integrated tunable graphene photodetector as a viable weighting solution for photonic in-sensor computing. Furthermore, such a solution could be used for large-scale neuromorphic in-sensor computing in photonic integrated circuits at the edge.

Bionic e-skin with precise multi-directional droplet sliding sensing for enhanced robotic perception.

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.

AI-Assisted Self-Powered Vehicle-Road Integrated Electronics for Intelligent Transportation Collaborative Perception.

Collaborative perception between a vehicle and the road has the potential to enhance the limited perception capability of autonomous driving technologies. With this background, self-powered vehicle-road integrated electronics (SVRIE) with a multilevel fractal structure is designed to play a dual role, including a SVRIE device integrated into vehicle tires and a SVRIE array embedded into a road surface. The pressure sensing capability and anti-crosstalk performance of the SVRIE array are characterized separately to validate the feasibility of applying the SVRIE in a cooperative vehicle-infrastructure system. It is demonstrated that the SVRIE based on the multi-layered fractal structure exhibits maximum performance in collaborative sensing and interaction between vehicles and road information, such as vehicle motion, road surface condition, and tire life cycle health monitoring. Traditional data analysis methods are often of questionable accuracy. Therefore, a convolutional neural network is used to classify the vehicle and road conditions with accuracy of at least 88.3%. The transfer learning model is constructed to enhance the road surface identification capabilities with 100% accuracy. The accuracies of the vehicle tire motion recognition and tire health monitoring are 97% and 99%, respectively. This work provides new ideas for collaborative perception between vehicles and roadsides.

Surface plasmons-phonons for mid-infrared hyperspectral imaging.

Surface plasmons have proven their ability to boost the sensitivity of mid-infrared hyperspectral imaging by enhancing light-matter interactions. Surface phonons, a counterpart technology to plasmons, present unclear contributions to hyperspectral imaging. Here, we investigate this by developing a plasmon-phonon hyperspectral imaging system that uses asymmetric cross-shaped nanoantennas composed of stacked plasmon-phonon materials. The phonon modes within this system, controlled by light polarization, capture molecular refractive index intensity and lineshape features, distinct from those observed with plasmons, enabling more precise and sensitive molecule identification. In a deep learning-assisted imaging demonstration of severe acute respiratory syndrome coronavirus (SARS-CoV), phonons exhibit enhanced identification capabilities (230,400 spectra/s), facilitating the de-overlapping and observation of the spatial distribution of two mixed SARS-CoV spike proteins. In addition, the plasmon-phonon system demonstrates increased identification accuracy (93%), heightened sensitivity, and enhanced detection limits (down to molecule monolayers). These findings extend phonon polaritonics to hyperspectral imaging, promising applications in imaging-guided molecule screening and pharmaceutical analysis.

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 article 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 microelectromechanical systems (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.

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.

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.

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.