Microsystems & Nanoengineering最新文献

[This corrects the article DOI: 10.1038/s41378-022-00478-9.].

Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0-220 mN for normal force and 0-15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm², while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

Separating plasma from whole blood is an important sample processing technique required for fundamental biomedical research, medical diagnostics, and therapeutic applications. Traditional protocols for plasma isolation require multiple centrifugation steps or multiunit microfluidic processing to sequentially remove large red blood cells (RBCs) and white blood cells (WBCs), followed by the removal of small platelets. Here, we present an acoustofluidic platform capable of efficiently removing RBCs, WBCs, and platelets from whole blood in a single step. By leveraging differences in the acoustic impedances of fluids, our device generates significantly greater forces on suspended particles than conventional microfluidic approaches, enabling the removal of both large blood cells and smaller platelets in a single unit. As a result, undiluted human whole blood can be processed by our device to remove both blood cells and platelets (>90%) at low voltages (25 Vpp). The ability to successfully remove blood cells and platelets from plasma without altering the properties of the proteins and antibodies present creates numerous potential applications for our platform in biomedical research, as well as plasma-based diagnostics and therapeutics. Furthermore, the microfluidic nature of our device offers advantages such as portability, cost efficiency, and the ability to process small-volume samples.

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.