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.

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.

Light-sheet microscopy in the near-infrared II window.

Non-invasive deep-tissue three-dimensional optical imaging of live mammals with high spatiotemporal resolution is challenging owing to light scattering. We developed near-infrared II (1,000-1,700 nm) light-sheet microscopy with excitation and emission of up to approximately 1,320 nm and 1,700 nm, respectively, for optical sectioning at a penetration depth of approximately 750 µm through live tissues without invasive surgery and at a depth of approximately 2 mm in glycerol-cleared brain tissues. Near-infrared II light-sheet microscopy in normal and oblique configurations enabled in vivo imaging of live mice through intact tissue, revealing abnormal blood flow and T-cell motion in tumor microcirculation and mapping out programmed-death ligand 1 and programmed cell death protein 1 in tumors with cellular resolution. Three-dimensional imaging through the intact mouse head resolved vascular channels between the skull and brain cortex, and allowed monitoring of recruitment of macrophages and microglia to the traumatic brain injury site.

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.

Scanning Super-Resolution Imaging in Enclosed Environment by Laser Tweezer Controlled Superlens.

Super-resolution imaging using microspheres has attracted tremendous scientific attention recently because it has managed to overcome the diffraction limit and allowed direct optical imaging of structures below 100 nm without the aid of fluorescent microscopy. To allow imaging of specific areas on the surface of samples, the migration of the microspheres to specific locations on two-dimensional planes should be controlled to be as precise as possible. The common approach involves the attachment of microspheres on the tip of a probe. However, this technology requires additional space for the probe and could not work in an enclosed environment, e.g., in a microfluidic enclosure, thereby reducing the range of potential applications for microlens-based super-resolution imaging. Herein, we explore the use of laser trapping to manipulate microspheres to achieve super-resolution imaging in an enclosed microfluidic environment. We have demonstrated that polystyrene microsphere lenses could be manipulated to move along designated routes to image features that are smaller than the optical diffraction limit. For example, a silver nanowire with a diameter of 90 nm could be identified and imaged. In addition, a mosaic image could be constructed by fusing a sequence of images of a sample in an enclosed environment. Moreover, we have shown that it is possible to image Escherichia coli bacteria attached on the surface of an enclosed microfluidic device with this method. This technology is expected to provide additional super-resolution imaging opportunities in enclosed environments, including microfluidic, lab-on-a-chip, and organ-on-a-chip devices.

Determination of Cell Membrane Capacitance and Conductance via Optically Induced Electrokinetics.

Cell membrane capacitance and conductance are key pieces of intrinsic information correlated with the cellular dielectric parameters and morphology of the plasma membrane; these parameters have been used as electrophysiological biomarkers to characterize cellular phenotype and state, and they have many associated clinical applications. Here, we present our work on the non-invasive determination of cell membrane capacitance and conductance by an optically activated microfluidics chip. The model for determining the cell membrane capacitance and conductance was established by a single layer of the shell-core polarization model. Three-dimensional finite-element analyses of the positive and negative optically induced dielectrophoresis forces generated by the projected light arrays of spots were performed, thus providing a theoretical validation of the feasibility of this approach. Then, the crossover frequency spectra for four typical types of cells (Raji cells, MCF-7 cells, HEK293 cells, and K562 cells) were experimentally investigated by using a micro-vision based motion-tracking technique. The different responses of these cells to the positive and negative ODEP forces were studied under four different liquid conductivities by automatic observation and tracking of the cellular trajectory and texture during the cells' translation. The cell membrane capacitance and conductance were determined from the curve-fitted spectra, which were 11.1 ± 0.9 mF/m2 and 782 ± 32 S/m2, respectively, for Raji cells, 11.5 ± 0.8 mF/m2 and 114 ± 28 S/m2 for MCF-7 cells, 9.0 ± 0.9 mF/m2 and 187 ± 22 S/m2 for HEK293 cells, and 10.2 ± 0.7 mF/m2 and 879 ± 24 S/m2 for K562 cells. Furthermore, as an application of this technique, the membrane capacitances of MCF-7 cells treated with four different concentrations of drugs were acquired. This technique introduces a determination of cell membrane capacitance and conductance that yields statistically significant data while allowing information from individual cells to be obtained in a non-invasive manner.

Super-resolution endoscopy for real-time wide-field imaging.

Resolving subcellular structures in vitro beyond optical diffraction barrier by a light microscope has achieved significant development since the advancement of super-resolution fluorescence microscopes, such as stimulated emission depletion (STED) microscopy, stochastic optical reconstruction microscopy (STORM) and photoactivated localization microscopy (PALM). However, the resolution of observation in deep and dense in vivo tissues is still confined to cellular level presently, and hence, exploring image details at subcellular level or even beyond organelle level in vivo has continued to attract much research attention. Currently, endoscopy provides an effective way to achieve in vivo observations and is compatible with mature optical microscopy technologies, but its resolution is usually confined to ~1 µm. Here we report a new endoscopy method by functionalizing graded-index (GRIN) lens with microspheres for real-time white-light or fluorescent super-resolution imaging. The capability of resolving objects with feature size of ~λ/5, which breaks the diffraction barrier of traditional GRIN lens based endoscopes by a factor of two, has been demonstrated by using this super-resolution endoscopy method. Further development of such a super-resolution endoscopy technique may provide new opportunities for in vivo life sciences studies.

Improving atomic force microscopy imaging by a direct inverse asymmetric PI hysteresis model.

A modified Prandtl-Ishlinskii (PI) model, referred to as a direct inverse asymmetric PI (DIAPI) model in this paper, was implemented to reduce the displacement error between a predicted model and the actual trajectory of a piezoelectric actuator which is commonly found in AFM systems. Due to the nonlinearity of the piezoelectric actuator, the standard symmetric PI model cannot precisely describe the asymmetric motion of the actuator. In order to improve the accuracy of AFM scans, two series of slope parameters were introduced in the PI model to describe both the voltage-increase-loop (trace) and voltage-decrease-loop (retrace). A feedforward controller based on the DIAPI model was implemented to compensate hysteresis. Performance of the DIAPI model and the feedforward controller were validated by scanning micro-lenses and standard silicon grating using a custom-built AFM.

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.

Deep Learning-Based Microscopic Cell Detection using Inverse Distance Transform and Auxiliary Counting.

Microscopic cell detection is a challenging task due to significant inter-cell occlusions in dense clusters and diverse cell morphologies. This paper introduces a novel framework designed to enhance automated cell detection. The proposed approach integrates a deep learning model that produces an inverse distance transform-based detection map from the given image, accompanied by a secondary network designed to regress a cell density map from the same input. The inverse distance transform-based map effectively highlights each cell instance in the densely populated areas, while the density map accurately estimates the total cell count in the image. Then, a custom counting-aided cell center extraction strategy leverages the cell count obtained by integrating over the density map to refine the detection process, significantly reducing false responses and thereby boosting overall accuracy. The proposed framework demonstrated superior performance with F-scores of 96.93%, 91.21%, and 92.00% on the VGG, MBM, and ADI datasets, respectively, surpassing existing state-of-the-art methods. It also achieved the lowest distance error, further validating the effectiveness of the proposed approach. These results demonstrate significant potential for automated cell analysis in biomedical applications.

Biometric-Tuned E-Skin Sensor with Real Fingerprints Provides Insights on Tactile Perception: Rosa Parks Had Better Surface Vibrational Sensation than Richard Nixon.

The dense mechanoreceptors in human fingertips enable texture discrimination. Recent advances in flexible electronics have created tactile sensors that effectively replicate slowly adapting (SA) and rapidly adapting (RA) mechanoreceptors. However, the influence of dermatoglyphic structures on tactile signal transmission, such as the effect of fingerprint ridge filtering on friction-induced vibration frequencies, remains unexplored. A novel multi-layer flexible sensor with an artificially synthesized skin surface capable of replicating arbitrary fingerprints is developed. This sensor simultaneously detects pressure (SA response) and vibration (RA response), enabling texture recognition. Fingerprint ridge patterns from notable historical figures - Rosa Parks, Richard Nixon, Martin Luther King Jr., and Ronald Reagan - are fabricated on the sensor surface. Vibration frequency responses to assorted fabric textures are measured and compared between fingerprint replicas. Results demonstrate that fingerprint topography substantially impacts skin-surface vibrational transmission. Specifically, Parks' fingerprint structure conveyed higher frequencies more clearly than those of Nixon, King, or Reagan. This work suggests individual fingerprint ridge morphological variation influences tactile perception and can confer adaptive advantages for fine texture discrimination. The flexible bioinspired sensor provides new insights into human vibrotactile processing by modeling fingerprint-filtered mechanical signals at the finger-object interface.

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.

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.

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.

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.

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.

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.

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.