Microsystems & Nanoengineering最新文献

The introduction of flows within sessile droplets is highly effective for many lab-on-a-chip chemical and biomedical applications. However, generating such flows is difficult due to the typically small droplet volumes. Here, we present a simple, non-contact strategy to generate internal flows in sessile droplets for enhancing mixing and mass transport. The flows are driven by actuating a rigid substrate into oscillation with certain amplitude distributions without relying on the resonance of the droplet itself. Substrate oscillation characteristics and corresponding flow patterns are documented herein. Mixing indices and mass transfer coefficients of sessile droplets on the substrate surface are measured using optical and electrochemical methods. They demonstrate complete mixing within the droplets in 1.35 s and increases in mass transfer rates of more than seven times static values. Proof of concept was conducted with experiments of silver nanoparticle synthesis and with heavy metal ion sensing employing the sessile droplet as a microreactor for synthesis and an electrochemical cell for sensing. The degrees of enhancement of synthesis efficiency and detection sensitivity attributed to the internal flows are experimentally documented.

Reservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.

Photodetectors (PDs) play a crucial role in imaging, sensing, communication systems, etc. Graphene (Gr), a leading two-dimensional material, has demonstrated significant potential for photodetection in recent years. However, its relatively weak interaction with light poses challenges for practical applications. The integration of silicon (Si) and perovskite quantum dots (PQDs) has opened new avenues for Gr in the realm of next-generation optoelectronics. This review provides a comprehensive investigation of Gr/Si Schottky junction PDs and Gr/PQD hybrid PDs as well as their heterostructures. The operating principles, design, fabrication, optimization strategies, and typical applications of these devices are studied and summarized. Through these discussions, we aim to illuminate the current challenges and offer insights into future directions in this rapidly evolving field.

Assays mimicking in vitro the concentration gradients triggering biological responses like those involved in fighting infections and blood clotting are essential for biomedical research. Microfluidic assays prove especially attractive as they allow precise control of gradient shape allied to a reduction in scale. Conventional microfluidic devices are fabricated using solid plastics that prevent direct access to responding cells. Fluid-walled microfluidics allows the manufacture of circuits on standard Petri dishes in seconds, coupled to simple operating methods; cell-culture medium sitting in a standard dish is confined to circuits by fluid walls made of an immiscible fluorocarbon. We develop and experimentally validate an analytical model of diffusion between two or more aqueous streams flowing at different rates into a fluid-walled conduit with the cross-section of a circular segment. Unlike solid walls, fluid walls morph during flows as pressures fall, with wall shape changing down the conduit. The model is validated experimentally for Fourier numbers < 0.1 using fluorescein diffusing between laminar streams. It enables a priori prediction of concentration gradients throughout a conduit, so allowing rapid circuit design as well as providing bio-scientists with an accurate way of predicting local concentrations of bioactive molecules around responsive and non-responsive cells.

With the rapid development of the Internet of Things (IoTs), wearable sensors are playing an increasingly important role in daily monitoring of personal health and wellness. The signal-to-noise-ratio has become the most critical performance factor to consider. To enhance it, on the one hand, good sensing materials/devices have been employed; on the other hand, signal amplification and noise reduction circuits have been used. However, most of these devices and circuits work in an active sampling mode, requiring frequent data acquisition and hence, entailing high-power consumption. In this scenario, a flexible and wearable event-triggered sensor with embedded signal amplification without an external power supply is of great interest. Here, we report a flexible two-terminal piezotronic n-p-n bipolar junction transistor (PBJT) that acts as an autonomous and highly sensitive, current- and/or voltage-mediated pressure sensor. The PBJT is formed by two back-to-back piezotronic diodes which are defined as emitter-base and collector-base diodes. Upon force exertion on the emitter side, as a result of the piezoelectric effect, the emitter-base diode is forward biased while the collector-base diode is reverse biased. Due to the inherent BJT amplification effect, the PBJT achieves record-high sensitivities of 139.7 kPa^-1 (current-based) and 88.66 kPa^-1 (voltage-based) in sensing mode. The PBJT also has a fast response time of <110 ms under exertion of dynamic stimuli ranging from a flying butterfly to a gentle finger touch. Therefore, the PBJT advances the state of the art not only in terms of sensitivity but also in regard to being self-driven and autonomous, making it promising for pressure sensing and other IoT applications.

With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human-computer interaction. Due to the high sensitivity (460.1 kPa^-1), high linearity (R ² > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual's blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

Silicon interposers embedded with ultra-deep through-silicon vias (TSVs) are in great demand for the heterogeneous integration and packaging of opto-electronic chiplets and microelectromechanical systems (MEMS) devices. Considering the cost-effective and reliable manufacturing of ultra-deep TSVs, the formation of continuous barrier and seed layers remains a crucial challenge to solve. Herein, we present a novel dual catalysis-based electroless plating (ELP) technique by tailoring polyimide (PI) liner surfaces to fabricate dense combined Ni barrier/seed layers in ultra-deep TSVs. In additional to the conventional acid catalysis procedure, a prior catalytic step in an alkaline environment is proposed to hydrolyze the PI surface into a polyamide acid (PAA) interfacial layer, resulting in additional catalysts and the formation of a dense Ni layer that can function as both a barrier layer and a seed layer, particularly at the bottom of the deep TSV. TSVs with depths larger than 500 μm and no voids are successfully fabricated in this study. The fabrication process involves low costs and temperatures. For a fabricated 530-μm-deep TSV with a diameter of 70 μm, the measured depletion capacitance and leakage current are approximately 1.3 pF and 1.7 pA at 20 V, respectively, indicating good electrical properties. The proposed fabrication strategy can provide a cost-effective and feasible solution to the challenge of manufacturing ultra-deep TSVs for modern 3D heterogeneous integration and packaging applications.

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm³) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.

Low-thermal-budget, electrically active, and thick polysilicon films are necessary for building a microelectromechanical system (MEMS) on top of a complementary metal oxide semiconductor (CMOS). However, the formation of these polysilicon films is a challenge in this field. Herein, for the first time, the development of in situ phosphorus-doped silicon films deposited under ultrahigh-vacuum conditions (~10^-9 Torr) using electron-beam evaporation (UHVEE) is reported. This process results in electrically active, fully crystallized, low-stress, smooth, and thick polysilicon films with low thermal budgets. The crystallographic, mechanical, and electrical properties of phosphorus-doped UHVEE polysilicon films are studied. These films are compared with intrinsic and boron-doped UHVEE silicon films. Raman spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM) and atomic force microscopy (AFM) are used for crystallographic and surface morphological investigations. Wafer curvature, cantilever deflection profile and resonance frequency measurements are employed to study the mechanical properties of the specimens. Moreover, resistivity measurements are conducted to investigate the electrical properties of the films. Highly vertical, high-aspect-ratio micromachining of UHVEE polysilicon has been developed. A comb-drive structure is designed, simulated, fabricated, and characterized as an actuator and inertial sensor comprising 20-μm-thick in situ phosphorus-doped UHVEE films at a temperature less than 500 °C. The results demonstrate for the first time that UHVEE polysilicon uniquely allows the realization of mechanically and electrically functional MEMS devices with low thermal budgets.

[This corrects the article DOI: 10.1038/s41378-024-00656-x.].