Microsystems & Nanoengineering最新文献

Microparticle manipulation is a critical concern across various fields including microfabrication, flexible electronics and tissue engineering. Acoustic-activated sharp structures have been designed as simple and flexible tools to manipulate microparticles with their good compatibility, fast response, and broad tunability. However, there still lacks rational acoustic-structure design for effective energy concentration at the acoustic-activated sharp structures for microparticle manipulation. Here, we present the acoustic black hole (ABH) effect as enhancement for the acoustic micro-manipulator. It provides great reliability, simplicity and ease of use, supporting custom design of high-throughput patterning modes. Moreover, compared to commonly used configurations, such as cylindrical or conical microneedles, those microneedles with ABH profile exhibit superior acoustic energy focusing at the tip and induce stronger acoustofluidic effects. The average acoustic flow velocity induced by the ABH microneedle is 154 times greater than that of the conical one and 45 times greater than that of the cylindrical microneedle. Besides, the average acoustic radiation force (ARF) produced by the ABH microneedle against acrylic microparticles is about 319 times greater than that of the cylindrical one and 16 times greater than that of the conical one. These results indicate that ABH design significantly enhances microparticle manipulation. We demonstrate this concept with ABH effect enhanced microparticle manipulation and study the parameters influencing its performance including operating frequency, operating voltage and particle diameter. Furthermore, considering the flexibility of this system, we employ it for various patterning and high-throughput microparticle manipulation. This work paves the way for controllable microparticle manipulation, holding great potential for applications in microfabrication and biomedicine.

The high-frequency and high-quality factor characteristics of bulk acoustic wave (BAW) resonators have significantly advanced their application in sensing technologies. In this work, a fluidic sensor based on a BAW resonator structure is fabricated and investigated. Embedded microchannels are formed beneath the active area of the BAW device without the need for external processes. As liquid flows through the microchannel, pressure is exerted on the upper wall (piezoelectric film) of the microchannel, which causes a shift in the resonant frequency. Using density functional theory, we revealed the intrinsic mechanism by which piezoelectric film deformation influences BAW resonator performance. Theoretically, the upwardly convex piezoelectric film caused by liquid flow can increase the resonant frequency. The experimental results obtained with ethanol solutions of different concentrations reveal that the sensor, which operates at a high resonant frequency of 2.225 GHz, achieves a remarkable sensitivity of 5.1 MHz/% (221 ppm/%), with an ultrahigh linearity of 0.995. This study reveals the intrinsic mechanism of liquid sensing based on BAW resonators, highlights the potential of AlN/Al_0.8Sc_0.2N composite film BAW resonators in liquid sensing applications and offers insights for future research and development in this field.

Understanding complex regulatory networks in plant systems requires elucidating the roles of various gene regulators under a spatial landscape. MicroRNA are key regulators that impart high information value through their tissue specificity and stability when using expression patterns for evaluating network outcomes. However, current techniques that utilize spatial multiplexing and quantitation of microRNA are limited to primarily mammalian systems. Here, we present a method to spatially resolve and quantify multiple endogenous microRNA in situ using ethanol fixed, paraffin embedded model plant species. This method utilizes target-specific microRNA capture along with universal ligating and labelling, all within functionalized hydrogel posts containing DNA probes in nanoliter well arrays. We demonstrate the platform's multiplexing capabilities through analyzing three endogenous microRNA in Arabidopsis thaliana rosettes which provide useful answers to fundamental plant growth and development from the unique expression patterns. The spatial tissue technique is also validated using non-spatial small RNA assays to demonstrate the versatility of the well array platform. Our new platform expands the toolkit of spatial omics technologies for plants.

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.