4D Printed Soft Microactuator for Particle Manipulation via Surrounding Medium Variation.

Soft actuators have assumed vital roles in a diverse number of research and application fields, driving innovation and transformative advancements. Using 3D molding of smart materials and combining these materials through structural design strategies, a single soft actuator can achieve multiple functions. However, it is still challenging to realize soft actuators that possess high environmental adaptability while capable of different tasks. Here, the response threshold of a soft actuator is modulated by precisely tuning the ratio of stimulus-responsive groups in hydrogels. By combining a heterogeneous bilayer membrane structure and in situ multimaterial printing, the obtained soft actuator deformed in response to changes in the surrounding medium. The response medium is suitable for both biotic and abiotic environments, and the response rate is fast. By changing the surrounding medium, the precise capture, manipulation, and release of micron-sized particles of different diameters in 3D are realized. In addition, static capture of a single red blood cell is realized using biologically responsive medium changes. Finally, the experimental results are well predicted using finite element analysis. It is believed that with further optimization of the structure size and autonomous navigation platform, the proposed soft microactuator has significant potential to function as an easy-to-manipulate multifunctional robot.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction.

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

AI-enhanced biomedical micro/nanorobots in microfluidics.

Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems.

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

Three dimensional architected thermoelectric devices with high toughness and power conversion efficiency.

For decades, the widespread application of thermoelectric generators has been plagued by two major limitations: heat stagnation in its legs, which limits power conversion efficiency, and inherent brittleness of its constituents, which accelerates thermoelectric generator failure. While notable progress has been made to overcome these quintessential flaws, the state-of-the-art suffers from an apparent mismatch between thermoelectric performance and mechanical toughness. Here, we demonstrate an approach to potentially enhance the power conversion efficiency while suppressing the brittle failure in thermoelectric materials. By harnessing the enhanced thermal impedance induced by the cellular architecture of microlattices with the exceptional strength and ductility (>50% compressive strain) derived from partial carbonization, we fabricate three-dimensional (3D) architected thermoelectric generators that exhibit a specific energy absorption of ~30 J g-1 and power conversion efficiency of ~10%. We hope our work will improve future thermoelectric generator fabrication design through additive manufacturing with excellent thermoelectric properties and mechanical robustness.

Correlation of Local Isomerization Induced Lateral and Terminal Torsions with Performance and Stability of Organic Photovoltaics.

Organic photovoltaics (OPVs) have achieved great progress in recent years due to delicately designed non-fullerene acceptors (NFAs). Compared with tailoring of the aromatic heterocycles on the NFA backbone, the incorporation of conjugated side-groups is a cost-effective way to improve the photoelectrical properties of NFAs. However, the modifications of side-groups also need to consider their effects on device stability since the molecular planarity changes induced by side-groups are related to the NFA aggregation and the evolution of the blend morphology under stresses. Herein, a new class of NFAs with local-isomerized conjugated side-groups are developed and the impact of local isomerization on their geometries and device performance/stability are systematically investigated. The device based on one of the isomers with balanced side- and terminal-group torsion angles can deliver an impressive power conversion efficiency (PCE) of 18.5%, with a low energy loss (0.528 V) and an excellent photo- and thermal stability. A similar approach can also be applied to another polymer donor to achieve an even higher PCE of 18.8%, which is among the highest efficiencies obtained for binary OPVs. This work demonstrates the effectiveness of applying local isomerization to fine-tune the side-group steric effect and non-covalent interactions between side-group and backbone, therefore improving both photovoltaic performance and stability of fused ring NFA-based OPVs.

Manipulating Crystallographic Orientation via Cross-Linkable Ligand for Efficient and Stable Perovskite Solar Cells.

The crystallographic orientation of polycrystalline perovskites is found to be strongly correlated with their intrinsic properties; therefore, it can be used to effectively enhance the performance of perovskite-based devices. Here, a facile way of manipulating the facet orientation of polycrystalline perovskite films in a controllable manner is reported. By incorporating a cross-linkable organic ligand into the perovskite precursor solution, the crystal orientation disorder can be reduced in the resultant perovskite films to exhibit the prominent (001) orientation with a preferred stacking mode. Moreover, the as-formed low-dimensional perovskites (LDPs) between the organic ligand and the excess lead iodide can passivate the defects around the grain boundaries. Consequently, highly efficient p-i-n structured perovskite solar cells (PSCs) can be made in both rigid and flexible forms from modified perovskites to show high power conversion efficiencies (PCE) of 24.12% and 23.23%, respectively. The devices also exhibit superior long-term stability in a humid environment (with T90  > 1000 h) and under thermal stress (retaining 87% of its initial PCE after 1000 h). More importantly, the ligand enables the derived LDPs to be crosslinked (under 254 nm UV illumination) to demonstrate excellent mechanical bending durability in flexible devices.

Development of the high angular resolution 360° LiDAR based on scanning MEMS mirror.

Light detection and ranging (LiDAR) using various operational principles has been applied in many fields, e.g., robotics navigation, autonomous vehicles, unmanned aerial flyers, land surveying, etc. The multichannel LiDAR system is of great importance in the field of autonomous driving due to its larger field of view (FoV). However, the number of transceivers limits the vertical angular resolution of multichannel LiDAR systems and makes them costly. On the other hand, the emergence of microelectromechanical systems (MEMS) mirrors may provide a highly promising solution to a low-cost, high angular resolution LiDAR system. We have demonstrated a MEMS mirror-based 360° LiDAR system with high angular resolution and will present the detailed design process and obtained experimental results in this paper. With the combination of the MEMS mirror and a rotation platform for the LiDAR system, a 360° × 8.6° (horizontal × vertical) FoV was achieved. Compared with existing commercial multichannel 360° LiDAR systems, our system has 13.8 times better angular resolution than the Velodyne HDL-64 LiDAR sensor. The experimental results verified an excellent performance of 0.07° × 0.027° (horizontal × vertical) angular resolution, which enhances the panoramic scanning and imaging capability of the LiDAR system, potentially providing more accurate 3D scanning applications in areas such as autonomous vehicles, indoor surveying, indoor robotics navigation, etc.

Online estimating weight of white Pekin duck carcass by computer vision.

The increasing consumption of ducks and chickens in China demands characterizing carcasses of domestic birds efficiently. Most existing methods, however, were developed for characterizing carcasses of pigs or cattle. Here, we developed a noncontact and automated weighing method for duck carcasses hanging on a production line. A 2D camera with its facilitating parts recorded the moving duck carcasses on the production line. To estimate the weight of carcasses, the images in the acquired dataset were modeled by a convolution neuron network (CNN). This model was trained and evaluated using 10-fold cross-validation. The model estimated the weight of duck carcasses precisely with a mean abstract deviation (MAD) of 58.8 grams and a mean relative error (MRE) of 2.15% in the testing dataset. Compared with 2 widely used methods, pixel area linear regression and the artificial neural network (ANN) model, our model decreases the estimation error MAD by 64.7 grams (52.4%) and 48.2 grams (45.0%). We release the dataset and code at https://github.com/RuoyuChen10/Image_weighing.

Self-charging electrostatic face masks leveraging triboelectrification for prolonged air filtration.

Electrostatic adsorption is an important complement to the mechanical filtration for high-efficiency air filtering. However, the electrostatic charge decays with time, especially in humid conditions. In this work, a self-charging air filter is presented to capture airborne particles in an efficient and long-lasting manner without the need of external power sources. Leveraging the triboelectric effect between the electrospun poly(vinylidene fluoride) nanofiber film and nylon fabric, the self-charging air filter-based mask excited by breathing can continuously replenish electrostatic charges. As a result, its effective lifespan is up to 60 hours (including 30 hours of wearing), with a minimum filtration efficiency of 95.8% for 0.3-µm particles. The filtration efficiency and lifespan are significantly higher than those of a commercial surgical mask. Furthermore, we uncover the quantitative relation between filtration efficiency and surface electrostatic potential. This work provides an effective strategy to significantly prolong the electrostatic adsorption efficacy for high-performance air-filtering masks.

Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters.

Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra-curved auricle. Here, a 3D graphene-based ear-conformable sensing device with embedded and distributed 3D electrodes for full-auricle physiological monitoring is reported. As a proof-of-concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject-specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region-specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning-based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR-based biometrical findings show promising biomedical applications.

Glycated Hemoglobin Electrochemical Immunosensor Based on Screen-Printed Electrode.

An electrochemical HbA1c sensor with high sensitivity and good specificity is proposed based on the electrochemical immune principle. The reproducibility and conductivity of the electrode are improved by depositing gold nanoparticles (AuNPs) on the surface of the screen-printed electrode (SPE). The HbA1c antibodies are immobilized on the surface of the modified electrode by adsorption to capture the HbA1c in the sample. The hindering effect of HbA1c on the electrode transfer reaction was exploited as the HbA1c detection mechanism. The electrode's properties were characterized by electrochemical impedance spectroscopy (EIS), and the measurement properties of the electrode were analyzed using differential pulse voltammetry (DPV) and cyclic voltammetry (CV). The experimental results show that the peak current signal of the electrochemical immunosensor produced a linear response to HbA1c in the concentration range of 20-200 µg/mL, a linear relationship coefficient of 0.9812, a detection limit of 15.5 µg/mL, and a sensitivity of 0.0938 µA/µg·mL-1. The sensor delivered satisfactory repeatability, stability, and anti-interference performance. Due to its small size, high sensitivity, and wide linear detection range, it is expected to play a significant role in managing diabetes at home.

Importance of structural hinderance in performance-stability equilibrium of organic photovoltaics.

Power conversion efficiency and long-term stability are two critical metrics for evaluating the commercial potential of organic photovoltaics. Although the field has witnessed a rapid progress of efficiency towards 19%, the intrinsic trade-off between efficiency and stability is still a challenging issue for bulk-heterojunction cells due to the very delicate crystallization dynamics of organic species. Herein, we developed a class of non-fullerene acceptors with varied side groups as an alternative to aliphatic chains. Among them, the acceptors with conjugated side groups show larger side-group torsion and more twisted backbone, however, they can deliver an efficiency as high as 18.3% in xylene-processed cells, which is among the highest values reported for non-halogenated solvent processed cells. Meanwhile, decent thermal/photo stability is realized for these acceptors containing conjugated side groups. Through the investigation of the geometry-performance-stability relationship, we highlight the importance of side-group steric hinderance of acceptors in achieving combined high-performance, stable, and eco-friendly organic photovoltaics.

A Review on Rail Defect Detection Systems Based on Wireless Sensors.

Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.

Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities.

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

A Review of Electrochemical Sensors for the Detection of Glycated Hemoglobin.

Glycated hemoglobin (HbA1c) is the gold standard for measuring glucose levels in the diagnosis of diabetes due to the excellent stability and reliability of this biomarker. HbA1c is a stable glycated protein formed by the reaction of glucose with hemoglobin (Hb) in red blood cells, which reflects average glucose levels over a period of two to three months without suffering from the disturbance of the outside environment. A number of simple, high-efficiency, and sensitive electrochemical sensors have been developed for the detection of HbA1c. This review aims to highlight current methods and trends in electrochemistry for HbA1c monitoring. The target analytes of electrochemical HbA1c sensors are usually HbA1c or fructosyl valine/fructosyl valine histidine (FV/FVH, the hydrolyzed product of HbA1c). When HbA1c is the target analyte, a sensor works to selectively bind to specific HbA1c regions and then determines the concentration of HbA1c through the quantitative transformation of weak electrical signals such as current, potential, and impedance. When FV/FVH is the target analyte, a sensor is used to indirectly determine HbA1c by detecting FV/FVH when it is hydrolyzed by fructosyl amino acid oxidase (FAO), fructosyl peptide oxidase (FPOX), or a molecularly imprinted catalyst (MIC). Then, a current proportional to the concentration of HbA1c can be produced. In this paper, we review a variety of representative electrochemical HbA1c sensors developed in recent years and elaborate on their operational principles, performance, and promising future clinical applications.

Stretchable Sweat-Activated Battery in Skin-Integrated Electronics for Continuous Wireless Sweat Monitoring.

Wearable electronics have attracted extensive attentions over the past few years for their potential applications in health monitoring based on continuous data collection and real-time wireless transmission, which highlights the importance of portable powering technologies. Batteries are the most used power source for wearable electronics, but unfortunately, they consist of hazardous materials and are bulky, which limit their incorporation into the state-of-art skin-integrated electronics. Sweat-activated biocompatible batteries offer a new powering strategy for skin-like electronics. However, the capacity of the reported sweat-activated batteries (SABs) cannot support real-time data collection and wireless transmission. Focused on this issue, soft, biocompatible, SABs are developed that can be directly integrated on skin with a record high capacity of 42.5 mAh and power density of 7.46 mW cm-2 among the wearable sweat and body fluids activated batteries. The high performance SABs enable powering electronic devices for a long-term duration, for instance, continuously lighting 120 lighting emitting diodes (LEDs) for over 5 h, and also offers the capability of powering Bluetooth wireless operation for real-time recording of physiological signals for over 6 h. Demonstrations of the SABs for powering microfluidic system based sweat sensors are realized in this work, allowing real-time monitoring of pH, glucose, and Na+ in sweat.

Characterization of interconnectivity of gelatin methacrylate hydrogels using photoacoustic imaging.

Hydrogels can provide a three-dimensional microenvironment for cells and thus serve as an extracellular matrix in a biofabrication process. The properties of hydrogels, such as their porosity and mechanical properties, significantly influence the cell growth. However, there is still a lack of effective methods for characterizing the hydrogel structure noninvasively. Herein, a photoacoustic (PA) imaging-based method is proposed for the characterization of gelatin methacrylate (GelMA) hydrogels. Owing to their high PA contrast, red blood cells (RBCs) are included as mediators in the GelMA hydrogel to analyze its pore distribution. The interconnectivity of the pores is further analyzed through the lysis of RBCs. The diffusion of the RBC lysis buffer in the GelMA is consistent with the trend observed in simulations. The analyzed vitality of HEK293 cells in different GelMA hydrogels reveals that understanding the diffusion of solutes (i.e., nutrients) is a potential strategy to optimize the hydrogel parameters during biofabrication.

Physical Cytometry: Detecting Mass-Related Properties of Single Cells.

The physical properties of a single cell, such as mass, volume, and density, are important indications of the cell's metabolic characteristics and homeostasis. Precise measurement of a single cell's mass has long been a challenge due to its minute size. It is only in the past 10 years that a variety of instruments for measuring living cellular mass have emerged with the development of MEMS, microfluidics, and optics technologies. In this review, we discuss the current developments of physical cytometry for quantifying mass-related physical properties of single cells, highlighting the working principle, applications, and unique merits. The review mainly covers these measurement methods: single-cell mass cytometry, levitation image cytometry, suspended microchannel resonator, phase-shifting interferometry, and opto-electrokinetics cell manipulation. Comparisons are made between these methods in terms of throughput, content, invasiveness, compatibility, and precision. Some typical applications of these methods in pathological diagnosis, drug efficacy evaluation, disease treatment, and other related fields are also discussed in this work.

Measurement methods of single cell drug response.

In the last decades, a wide multitude of research activity has been focused on the development of new drugs, and devoted to overcome the challenges of high cost and low efficiency in drug evaluation. The measurement of drug response at the single cell level is a quicker, more direct and more accurate way to reflect drug efficacy, which can shorten the drug development period and reduce research costs. Therefore, the single cell drug response (SCDR) measurement technology has aroused extensive attention from researchers, and has become a hot topic in the fields of drug research and cell biology. Recent years have seen the emergence of various SCDR measurement technologies that feature different working principles and different levels of measurement performance. To better examine, compare and summarize the characteristics and functions of these technologies, we select signal-to-noise ratio, throughput, content, invasion, and device complexity as the criteria to evaluate them from the drug efficacy perspective. This review aims to highlight sixteen kinds of SCDR measurement technologies, including patch-clamp technique, live-cell interferometry, capillary electrophoresis, secondary ion mass spectrometry, and more, and report widespread representative examples of SCDR measurement the recent approaches for over the past forty years. Based on their reaction principles, these technologies are classified into four categories: electrical, optical, electrochemical, and mass spectrometry, and a detailed comparison is made between them. After in-depth understanding of these technologies, it is expected to improve or integrate these technologies to propose better SCDR measurement strategies, and explore methods in new drug development and screening, as well as disease diagnosis and treatment.