Microsystems & Nanoengineering最新文献

In robotic-assisted surgery (RAS), traditional surgical instruments without sensing capability cannot perceive accurate operational forces during the task, and such drawbacks can be largely intensified when sophisticated tasks involving flexible and slender arms with small end-effectors, such as in gastrointestinal endoscopic surgery (GES). In this study, we propose a microelectromechanical system (MEMS) piezoresistive 3-axial tactile sensor for GES forceps, which can intuitively provide surgeons with online force feedback during robotic surgery. The MEMS fabrication process facilitates sensor chips with miniaturized dimensions. The fully encapsulated tactile sensors can be effortlessly integrated into miniature GES forceps, which feature a slender diameter of just 3.5 mm and undergo meticulous calibration procedures via the least squares method. Through experiments, the sensor's ability to accurately measure directional forces up to 1.2 N in the Z axis was validated, demonstrating an average relative error of only 1.18% compared with the full-scale output. The results indicate that this tactile sensor can provide effective 3-axial force sensing during surgical operations, such as grasping and pulling, and in ex vivo testing with a porcine stomach. The compact size, high precision, and integrability of the sensor establish solid foundations for clinical application in the operating theater.

Current potentiometric sensing methods are limited to detecting nitrate at parts-per-billion (sub-micromolar) concentrations, and there are no existing potentiometric chemical sensors with ultralow detection limits below the parts-per-trillion (picomolar) level. To address these challenges, we integrate interdigital graphene ion-sensitive field-effect transistors (ISFETs) with a nitrate ion-sensitive membrane (ISM). The work aims to maximize nitrate ion transport through the nitrate ISM, while achieving high device transconductance by evaluating graphene layer thickness, optimizing channel width-to-length ratio (R_WL), and enlarging total sensing area. The captured nitrate ions by the nitrate ISM induce surface potential changes that are transduced into electrical signals by graphene, manifested as the Dirac point shifts. The device exhibits Nernst response behavior under ultralow concentrations, achieving a sensitivity of 28 mV/decade and establishing a record low limit of detection of 0.041 ppt (4.8 × 10^-13 M). Additionally, the sensor showed a wide linear detection range from 0.1 ppt (1.2 × 10^-12 M) to 100 ppm (1.2 × 10^-3 M). Furthermore, successful detection of nitrate in tap and snow water was demonstrated with high accuracy, indicating promising applications to drinking water safety and environmental water quality control.

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high level of variability in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems that are informed by the transient state of droplets and their interactions. However, most related studies focus on droplet localization and shape recognition. In this study, we develop the AI-assisted DMF framework μDropAI for multistate droplet control on the basis of droplet morphology. The semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%; it enables control of droplets without user intervention. The coefficient of variation (CV) of the volumes of split droplets can be limited to 2.74%, which is lower than the CV of traditional dispensed droplets, contributing to an improvement in the precision of volume control for droplet splitting. The proposed system inspires the development of semantic-driven DMF systems that can interface with multimodal large language models (MLLMs) for fully automatic control.

A high quality (Q) factor is essential for enhancing the performance of resonant nanoelectromechanical systems (NEMS). NEMS resonators based on two-dimensional (2D) materials such as molybdenum disulfide (MoS₂) have high frequency tunability, large dynamic range, and high sensitivity, yet room-temperature Q factors are typically less than 1000. Here, we systematically investigate the effects of device size and surface nonidealities on Q factor by measuring 52 dry-transferred fully clamped circular MoS₂ NEMS resonators with diameters ranging from 1 μm to 8 μm, and optimize the Q factor by combining these effects with the strain-modulated dissipation model. We find that Q factor first increases and then decreases with diameter, with an optimized room-temperature Q factor up to 3315 ± 115 for a 2-μm-diameter device. Through extensive characterization and analysis using Raman spectroscopy, atomic force microscopy, and scanning electron microscopy, we demonstrate that surface nonidealities such as wrinkles, residues, and bubbles are especially significant for decreasing Q factor, especially for larger suspended membranes, while resonators with flat and smooth surfaces typically have larger Q factors. To further optimize Q factors, we measure and model Q factor dependence on the gate voltage, showing that smaller DC and radio-frequency (RF) driving voltages always lead to a higher Q factor, consistent with the strain-modulated dissipation model. This optimization of the Q factor delineates a straightforward and promising pathway for designing high-Q 2D NEMS resonators for ultrasensitive transducers, efficient RF communications, and low-power memory and computing.

An active-matrix electrowetting-on-dielectric (AM-EWOD) system integrates hundreds of thousands of active electrodes for sample droplet manipulation, which can enable simultaneous, automatic, and parallel on-chip biochemical reactions. A smart detection system is essential for ensuring a fully automatic workflow and online programming for the subsequent experimental steps. In this work, we demonstrated an artificial intelligence (AI)-enabled multipurpose smart detection method in an AM-EWOD system for different tasks. We employed the U-Net model to quantitatively evaluate the uniformity of the applied droplet-splitting methods. We used the YOLOv8 model to monitor the droplet-splitting process online. A 97.76% splitting success rate was observed with 18 different AM-EWOD chips. A 99.982% model precision rate and a 99.980% model recall rate were manually verified. We employed an improved YOLOv8 model to detect single-cell samples in nanolitre droplets. Compared with manual verification, the model achieved 99.260% and 99.193% precision and recall rates, respectively. In addition, single-cell droplet sorting and routing experiments were demonstrated. With an AI-based smart detection system, AM-EWOD has shown great potential for use as a ubiquitous platform for implementing true lab-on-a-chip applications.

The thermal expansion of gas and the air permeability of polydimethylsiloxane (PDMS) were previously thought to be the main causes of bubbles and water loss during polymerase chain reaction (PCR), resulting in a very complex chip design and operation. Here, by calculating and characterizing bubble formation, we discovered that water vapor is the main cause of bubbling. During PCR, heat increases the volume of the bubble by a factor of only ~0.2 in the absence of water vapor but by a factor of ~6.4 in the presence of water vapor. In addition, the phenomenon of "respiration" due to the repeated evaporation and condensation of water vapor accelerates the expansion of bubbles and the loss of water. A water seal above 109 kPa can effectively prevent bubbles in a bare PDMS chip with a simple structure, which is significant for the wide application of PDMS chips.

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS₂ and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 10³, 100 times larger than that of devices prepared with undoped WS₂. The Nb atoms in WS₂ play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS₂ synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study's contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.

Due to the excellent mechanical, chemical, and electrical properties of third-generation semiconductor silicon carbide (SiC), pressure sensors utilizing this material might be able to operate in extreme environments with temperatures exceeding 300 °C. However, the significant output drift at elevated temperatures challenges the precision and stability of measurements. Real-time in situ temperature monitoring of the pressure sensor chip is highly important for the accurate compensation of the pressure sensor. In this study, we fabricate platinum (Pt) thin-film resistance temperature detectors (RTDs) on a SiC substrate by incorporating aluminum oxide (Al₂O₃) as the transition layer and utilizing aluminum nitride (AlN) grooves for alignment through microfabrication techniques. The composite layers strongly adhere to the substrate at temperatures reaching 950 °C, and the interface of the Al₂O₃/Pt bilayer remains stable at elevated temperatures of approximately 950 °C. This stability contributes to the excellent high-temperature electrical performance of the Pt RTD, enabling it to endure temperatures exceeding 850 °C with good linearity. These characteristics establish a basis for the future integration of Pt RTD in SiC pressure sensors. Furthermore, tests and analyses are conducted on the interfacial diffusion, surface morphological, microstructural, and electrical properties of the Pt films at various annealing temperatures. It can be inferred that the tensile stress and self-diffusion of Pt films lead to the formation of hillocks, ultimately reducing the electrical performance of the Pt thin-film RTD. To increase the upper temperature threshold, steps should be taken to prevent the agglomeration of Pt films.

Controllable droplet propulsion on solid surfaces plays a crucial role in various technologies. Many actuating methods have been developed; however, there are still some limitations in terms of the introduction of additives, the versatilities of solid surfaces, and the speed of transportation. Herein, we have demonstrated a universal droplet propulsion method based on dynamic surface-charge wetting by depositing oscillating and opposite surface charges on dielectric films with unmodified surfaces. Dynamic surface-charge wetting propels droplets by continuously inducing smaller front contact angles than rear contact angles. This innovative imbalance is built by alternately storing and spreading opposite charges on dielectric films, which results in remarkable electrostatic forces under large gradients and electric fields. The method exhibits excellent droplet manipulation performance characteristics, including high speed (~130 mm/s), high adaptability of droplet volume (1 μL-1 mL), strong handling ability on non-slippery surfaces with large contact angle hysteresis (CAH) (maximum angle of 35°), significant programmability and reconfigurability, and low mass loss. The great application potential of this method has been effectively demonstrated in programmable microreactions, defogging without gravity assistance, and surface cleaning of photovoltaic panels using condensed droplets.

Research on outdoor, mobile, and self-powered temperature-control devices has always been highly regarded. These devices can reduce energy consumption for cooling and heating, and they have broad market prospects. On this basis, a rotary disc-shaped triboelectric nanogenerator (TENG) with a maximum open-circuit voltage of 6913 V, a maximum short-circuit current of 85 μA, and a maximum transferred charge of 1.3 μC was prepared. We synthesized a ferroelectric ceramic composed of 0.15PbTiO₃-0.85PbSc_0.5Ta_0.5O₃ (0.15PT-0.85PST), which exhibited excellent electrothermal effects at room temperature. By quenching, the electrothermal effect ( $\Delta$ T_max) and energy harvesting properties of the device were 1.574 K and 0.542 J/cm³, respectively. Then, for the first time, we proposed a self-powered temperature quantification control system with a rotary disc-shaped TENG. This device effectively harnessed wind and water energy, in addition to other types of energy. The system consisted of energy collecting cups, a rotating disc-shaped FEP-rabbit fur TENG, a circuit management module, and a ferroelectric ceramic chip array. Through the circuit management module, the system converted external wind energy into a high-voltage electric field at the two ends of the 0.15PT-0.85PST ceramic chip to fully stimulate the electrothermal effect. At a speed of 200 rpm, the temperature change in the insulated cup within 276 s was 0.49 K, and the volume of the insulated cup was 300 times greater than that of the 0.15PT-0.85PST ceramic chip. Compared with the results reported in previous work, the cooling and heating times were both reduced by 31%, and the temperature changes for both cooling and heating increased by 81%. Moreover, the heating and cooling temperatures of the device optimized on this basis were increased to 1.19 K and 0.93 K, respectively. The great improvement in the temperature variation performance confirmed the great potential of the device for commercialization. This research could serve as a reference for reducing energy consumption for cooling and heating, and it meets the international energy policies of carbon dioxide emission peaking and carbon neutrality.