Microsystems & Nanoengineering最新文献

Neuroinflammation is characterized by the elevation of cytokines and adenosine triphosphate (ATP), which in turn activates microglia. These immunoregulatory molecules typically form gradients in vivo, which significantly influence microglial behaviors such as increasing calcium signaling, migration, phagocytosis, and cytokine secretion. Quantifying microglial calcium signaling in the context of inflammation holds the potential for developing precise therapeutic strategies for neurological diseases. However, the current calcium imaging systems are technically challenging to operate, necessitate large volumes of expensive reagents and cells, and model immunoregulatory molecules as uniform concentrations, failing to accurately replicate the in vivo microenvironment. In this study, we introduce a novel calcium monitoring micro-total analysis system (CAM-μTAS) designed to quantify calcium dynamics in microglia (BV2 cells) within defined cytokine gradients. Leveraging programmable pneumatically actuated lifting gate microvalve arrays and a Quake valve, CAM-μTAS delivers cytokine gradients to microglia, mimicking neuroinflammation. Our device automates sample handling and cell culture, enabling rapid media changes in just 1.5 s, thus streamlining the experimental workflow. By analyzing BV2 calcium transient latency to peak, we demonstrate location-dependent microglial activation patterns based on cytokine and ATP gradients, offering insights contrasting those of non-gradient-based perfusion systems. By harnessing advancements in microsystem technology to quantify calcium dynamics, we can construct simplified human models of neurological disorders, unravel the intricate mechanisms of cell-cell signaling, and conduct robust evaluations of novel therapeutics.

Hypersonic vehicles and aircraft engine blades face complex and harsh environments such as high heat flow density and high temperature, and they are generally narrow curved spaces, making it impossible to actually install them for testing. Thin-film heat flux sensors (HFSs) have the advantages of small size, fast response, and in-situ fabrication, but they are prone to reach thermal equilibrium and thus fail during testing. In our manuscript, an ITO-In₂O₃ thick film heat flux sensor (HFS) is designed, and a high-temperature heat flux test system is built to simulate the working condition of a blade subjected to heat flow impact. The simulation and test results show that the test performance of the thick-film HFS is improved by optimizing the structure and parameters. Under the condition of no water cooling, the designed HFS can realize short-time heat flux monitoring at 1450 °C and long-term stable monitoring at 1300 °C and below. With a maximum output thermopotential of 17.8 mV and an average test sensitivity of 0.035 mV/(kW/m²), the designed HFS has superior high-temperature resistance that cannot be achieved by other existing thin (thick) film HFSs. Therefore, the designed HFS has great potential for application in harsh environments such as aerospace, weaponry, and industrial metallurgy.

Hydraulic technology with smaller sizes and higher reliability trends, including fault prediction and intelligent control, requires high-performance temperature and pressure-integrated sensors. Current designs rely on planar wafer- or chip-level integration, which is limited by pressure range, chip size, and low reliability. We propose a small-size temperature/high-pressure integrated sensor via the flip-chip technique. The pressure and temperature units are arranged vertically, and the sensing signals of the two units are integrated into one plane through silicon vias and gold–gold bonding, reducing the lateral size and improving the efficiency of signal transmission. The flip-chip technique ensures a reliable electrical connection. A square diaphragm with rounded corners is designed and optimised with simulation to sense high pressure based on the piezoresistive effect. The temperature sensing unit with a thin-film platinum resistor measures temperature and provides back-end high-precision compensation, which will improve the precision of the pressure unit. The integrated chip is fabricated by MEMS technology and packaged to fabricate the extremely small integrated sensor. The integrated sensor is characterised, and the pressure sensor exhibits a sensitivity and sensitivity drift of 7.97 mV/MPa and −0.19% FS in the range of 0–20 MPa and −40 to 120 °C. The linearity, hysteresis, repeatability, accuracy, basic error, and zero-time drift are 0.16% FS, 0.04% FS, 0.06% FS, 0.18% FS, ±0.23% FS and 0.04% FS, respectively. The measurement error of the temperature sensor and temperature coefficient of resistance is less than ±1 °C and 3142.997 ppm/°C, respectively. The integrated sensor has broad applicability in fault diagnosis and safety monitoring of high-end equipment such as automobile detection, industrial equipment, and oil drilling platforms.

Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation.

With the rapid development of various fields, including aerospace, industrial measurement and control, and medical monitoring, the need to quantify flow velocity measurements is increasing. It is difficult for traditional flow velocity sensors to fulfill accuracy requirements for velocity measurements due to their small ranges, susceptibility to environmental impacts, and instability. Herein, to optimize sensor performance, a flexible microelectromechanical system (MEMS) thermal flow sensor is proposed that combines the working principles of thermal loss and thermal temperature difference and utilizes a flexible cavity substrate made of a low-thermal-conductivity polyimide/SiO₂ (PI/SiO₂) composite porous film to broaden the measurement range and improve the sensitivity. The measurement results show that the maximum measurable flow velocity can reach 30 m/s with a resolution of 5.4 mm/s. The average sensitivities of the sensor are 59.49 mV/(m s⁻¹) in the medium-to-low wind velocity range of 0–2 m/s and 467.31 mV/(m s⁻¹) in the wind velocity range of 2–30 m/s. The sensor proposed in this work can enable new applications of flexible flow sensors and wearable devices.

The emergence of biomagnetism imaging has led to the development of ultrasensitive and compact spin-exchange relaxation-free (SERF) atomic magnetometers that promise high-resolution magnetocardiography (MCG) and magnetoencephalography (MEG). However, conventional optical components are not compatible with nanofabrication processes that enable the integration of atomic magnetometers on chips, especially for elliptically polarized laser-pumped SERF magnetometers with bulky optical systems. In this study, an elliptical-polarization pumping beam (at 795 nm) is achieved through a single-piece metasurface, which results in an SERF magnetometer with a high sensitivity reaching 10.61 fT/Hz^1/2 by utilizing a ⁸⁷Rb vapor cell with a 3 mm inner diameter. To achieve the optimum theoretical polarization, our design combines a computer-assisted optimization algorithm with an emerging metasurface design process. The metasurface is fabricated with 550 nm thick silicon-rich silicon nitride on a 2 × 2 cm ² SiO₂ substrate and features a 22.17° ellipticity angle (a deviation from the target polarization of less than 2%) and more than 80% transmittance. This study provides a feasible approach for on-chip polarization control of future all-integrated atomic magnetometers, which will further pave the way for high-resolution biomagnetism imaging and portable atomic sensing applications.

This work presents the development and design of aptasensor employing porous silicon (PSi) Fabry‒Pérot thin films that are suitable for use as optical transducers for the detection of lactoferrin (LF), which is a protein biomarker secreted at elevated levels during gastrointestinal (GI) inflammatory disorders such as inflammatory bowel disease and chronic pancreatitis. To overcome the primary limitation associated with PSi biosensors-namely, their relatively poor sensitivity due to issues related to complex mass transfer phenomena and reaction kinetics-we employed two strategic approaches: First, we sought to optimize the porous nanostructure with respect to factors including layer thickness, pore diameter, and capture probe density. Second, we leveraged convection properties by integrating the resulting biosensor into a 3D-printed microfluidic system that also had one of two different micromixer architectures (i.e., staggered herringbone micromixers or microimpellers) embedded. We demonstrated that tailoring the PSi aptasensor significantly improved its performance, achieving a limit of detection (LOD) of 50 nM-which is >1 order of magnitude lower than that achieved using previously-developed biosensors of this type. Moreover, integration into microfluidic systems that incorporated passive and active micromixers further enhanced the aptasensor's sensitivity, achieving an additional reduction in the LOD by yet another order of magnitude. These advancements demonstrate the potential of combining PSi-based optical transducers with microfluidic technology to create sensitive label-free biosensing platforms for the detection of GI inflammatory biomarkers.

Supramolecules are considered as promising materials for volatile organic compounds (VOCs) sensing applications. The proper understanding of the sorption process taking place in host-guest interactions is critical in improving the pattern recognition of supramolecules-based sensing arrays. Here, we report a novel approach to investigate the dynamic host-guest recognition process by employing a bulk acoustic wave (BAW) resonator capable of producing multiple oscillation amplitudes and simultaneously recording multiple responses to VOCs. Self-assembled monolayers (SAMs) of β-cyclodextrin (β-CD) were modified on four BAW sensors to demonstrate the gas-surface interactions regarding oscillation amplitude and SAM length. Based on the method, a virtual sensor array (VSA) type electronic nose (e-nose) can be realized by pattern recognition of multiple responses at different oscillation amplitudes of a single sensor. VOCs analysis was realized respectively by using principal component analysis (PCA) for individual VOC identification and linear discriminant analysis (LDA) for VOCs mixtures classification.

Flexible sensors are essential components in emerging fields such as epidermal electronics, biomedicine, and human-computer interactions, and creating high-performance sensors through simple structural design for practical applications is increasingly needed. Presently, challenges still exist in establishing efficient models of flexible piezoresistive pressure sensors to predict the design required for achieving target performance. This work establishes a theoretical model of a flexible pressure sensor with a simple laminated and enclosed structure. In the modeling, the electrical constriction effect is innovatively introduced to explain the sensitization mechanism of the laminated structure to a broad range of pressures and to predict the sensor performance. The experimental results confirmed the effectiveness of the theoretical model. The sensor exhibited excellent stability for up to three million cycles and superior durability when exposed to salt solution owing to its simple laminated and enclosed structural design. Finally, a wearable sensing system for real-time collection and analysis of plantar pressure is constructed for exercise and rehabilitation monitoring applications. This work aims to provide theoretical guidance for the rapid design and construction of flexible pressure sensors with target performance for practical applications.

Exosomes derived from mesenchymal stem cells (MSCs) have been confirmed to enhance cell proliferation and improve tissue repair. Exosomes release their contents into the cytoplasmic solution of the recipient cell to mediate cell expression, which is the main pathway through which exosomes exert therapeutic effects. The corresponding process of exosome internalization mainly occurs in the early stage of treatment. However, the therapeutic effect of exosomes in the early stage remains to be further studied. We report that the three-dimensional cell traction force can intuitively reflect the ability of exosomes to enhance the cytoskeleton and cell contractility of recipient cells, serving as an effective method to characterize the therapeutic effect of exosomes. Compared with traditional biochemical methods, we can visualize the early therapeutic effect of exosomes in real time without damage by quantifying the cell traction force. Through quantitative analysis of traction forces, we found that endometrial stromal cells exhibit short-term cell roundness accompanied by greater traction force during the early stage of exosome therapy. Further experiments revealed that exosomes enhance the traction force and cytoskeleton by regulating the Rac1/RhoA signaling pathway, thereby promoting cell proliferation. This work provides an effective method for rapidly quantifying the therapeutic effects of exosomes and studying the underlying mechanisms involved.