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A miniaturized MEMS accelerometer with anti-spring mechanism for enhancing sensitivity

Anti-spring mechanisms are widely used for improving the noise performance of MEMS accelerometers due to their stiffness softening effect. However, the existing mechanisms typically require large bias force and displacement for achieving stiffness softening, leading to large device dimensions. Here, we propose a novel anti-spring mechanism composed of two pre-shaped curved beams connected in a parallel configuration, which can achieve stiffness softening without requiring large bias force and displacement. The stiffness softening effect of the mechanism is verified through theoretical modeling and finite element method (FEM) simulation. After that, the mechanism is implemented in a 4.2 mm × 4.9 mm MEMS capacitive accelerometer prototype. The experimental results reveal that the sensitivity of the accelerometer increases by 10.4% compared to the initial sensitivity; at the same time, the noise floor and bias instability decrease by 10.5% and 4.2%. The sensitivity, nonlinearity, bias instability, and noise floor after biasing are 51.1 mV/g, 0.99%, 0.24 mg, and 21.3 ({rm{mu }}{rm{g}}/sqrt{{rm{Hz}}}), respectively. Thus, the proposed mechanism can enhance the performance of the accelerometer. This work provides an innovative approach for improving the performance of MEMS accelerometers while enabling miniaturization.

Vision-based tactile sensor design using physically based rendering

High-resolution tactile sensors are very helpful to robots for fine-grained perception and manipulation tasks, but designing those sensors is challenging. This is because the designs are based on the compact integration of multiple optical elements, and it is difficult to understand the correlation between the element arrangements and the sensor accuracy by trial and error. In this work, we introduce the digital design of vision-based tactile sensors using a physically accurate light simulator. The framework modularizes the design process, parameterizes the sensor components, and contains an evaluation metric to quantify a sensor’s performance. We quantify the effects of sensor shape, illumination setting, and sensing surface material on tactile sensor performance using our evaluation metric. The proposed optical simulation framework can replicate the tactile image of the real vision-based tactile sensor prototype without any prior sensor-specific data. Using our approach we can substantially improve the design of a fingertip GelSight sensor. This improved design performs approximately 5 times better than previous state-of-the-art human-expert design at real-world robotic tactile embossed text detection. Our simulation approach can be used with any vision-based tactile sensor to produce a physically accurate tactile image. Overall, our approach enables the automatic design of sensorized soft robots and opens the door for closed-loop co-optimization of controllers and sensors for dexterous manipulation.

Electric-field manipulation of magnetization in an insulating dilute ferromagnet through piezoelectromagnetic coupling

The electric field control of magnetization is of significant interest in materials science due to potential applications in many devices such as sensors, actuators, and magnetic memories. Here, we report magnetization changes generated by an electric field in ferromagnetic Ga1−xMnxN grown by molecular beam epitaxy. Two classes of phenomena have been revealed. First, over a wide range of magnetic fields, the magnetoelectric signal is odd in the electric field and reversible. Employing a macroscopic spin model and atomistic Landau-Lifshitz-Gilbert theory with Langevin dynamics, we demonstrate that the magnetoelectric response results from the inverse piezoelectric effect that changes the trigonal single-ion magnetocrystalline anisotropy. Second, in the metastable regime of ferromagnetic hystereses, the magnetoelectric effect becomes non-linear and irreversible in response to a time-dependent electric field, which can reorient the magnetization direction. Interestingly, our observations are similar to those reported for another dilute ferromagnetic semiconductor Crx(Bi1−ySby)1−xTe3, in which magnetization was monitored as a function of the gate electric field. Those results constitute experimental support for theories describing the effects of time-dependent perturbation upon glasses far from thermal equilibrium in terms of an enhanced effective temperature.

SETD1B-mediated broad H3K4me3 controls proper temporal patterns of gene expression critical for spermatid development

Epigenetic programming governs cell fate determination during development through intricately controlling sequential gene activation and repression. Although H3K4me3 is widely recognized as a hallmark of gene activation, its role in modulating transcription output and timing within a continuously developing system remains poorly understood. In this study, we provide a detailed characterization of the epigenomic landscapes in developing male germ cells. We identified thousands of spermatid-specific broad H3K4me3 domains regulated by the SETD1B-RFX2 axis, representing a previously underappreciated form of H3K4me3. These domains, overlapping with H3K27ac-marked enhancers and promoters, play critical roles in orchestrating robust transcription and accurate temporal control of gene expression. Mechanistically, these broad H3K4me3 compete effectively with regular H3K4me3 for transcriptional machinery, thereby ensuring robust levels and precise timing of master gene expression in mouse spermiogenesis. Disruption of this mechanism compromises the accuracy of transcription dosage and timing, ultimately impairing spermiogenesis. Additionally, we unveil remarkable changes in the distribution of heterochromatin marks, including H3K27me3 and H3K9me2, during the mitosis-to-meiosis transition and completion of meiotic recombination, which closely correlates with gene silencing. This work underscores the highly orchestrated epigenetic regulation in spermatogenesis, highlighting the previously unrecognized role of Setd1b in the formation of broad H3K4me3 domains and transcriptional control, and provides an invaluable resource for future studies toward the elucidation of spermatogenesis.

First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes

MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.

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