Silicon wet bulk micromachining using alkaline etchants such as KOH, TMAH, EDP, NaOH, and CsOH is a widely used technique for fabricating a variety of microstructures, including microcantilever beams, diaphragms, grooves, trenches, and microchannels. It is extensively employed in both research laboratories and the industrial manufacturing of microelectromechanical systems (MEMS). This technique offers several advantages, including low processing cost, crystallographic orientation-dependent etching, tunable etch rates through additives, the ability to fabricate three-dimensional structures with slanted sidewalls, controlled undercutting for suspended structures, and high selectivity toward masking materials. Among alkaline etchants, NaOH-based solutions have attracted considerable attention for silicon micromachining. The etching process involves the chemical dissolution of silicon, with the etch rate strongly influenced by parameters such as NaOH concentration, solution temperature, and the crystallographic orientation of the silicon substrate. Recent studies have focused on enhancing the etch rate and undercutting behavior through the incorporation of suitable additives. In this review, we comprehensively present the etching characteristics of NaOH solutions, including etch rate, undercutting, and etched surface morphology, both in the absence and presence of additives. Particular emphasis is placed on the influence of NH₂OH as an additive, which significantly modifies the etch rate and undercutting behavior. The etching performance of NH₂OH-modified NaOH solutions for Si100, Si110, and Si111 wafers is systematically analyzed. This paper aims to serve as a consolidated reference for NaOH-based wet bulk micromachining in the fabrication of various MEMS components.
Transport of small molecules in biological media is governed by microstructure, yet the quantitative link between geometry and effective diffusion remains poorly resolved, particularly for brain-on-a-chip systems. Here, we present a minimal model of brain parenchyma relevant to microfluidic systems, in which the extracellular space is treated as a porous medium with spherical cellular inclusions at prescribed volume fractions. Using finite-element simulations of steady-state Fickian transport, we have studied diffusion and tortuosity on extracellular space (ECS). We have observed essential dependence of the diffusion on the particle packing even at small volume fraction (or large ECS fraction) corresponding to those in microfluidic systems brain-on-a-chip. The results establish a direct structure-transport relationship relevant for optimizing engineering and experiments with various microfluidic physiological systems tissue-on-a-chip.
This review presents a comprehensive overview of methodologies employed to measure and quantify the biomechanical properties of single cells, focusing on techniques that provide insights into cellular deformation and force response. Understanding cell mechanics is crucial for advancing our knowledge of cellular behavior, disease progression, and therapeutic innovation in contexts such as cancer metastasis, tissue engineering, and regenerative medicine. We critically examine five primary experimental platforms: atomic force microscopy (AFM), dielectrophoresis (DEP), optical tweezers, micropipette aspiration, and hydrodynamic-induced deformation. For each method, we detail the underlying theoretical frameworks, experimental configurations, advantages, limitations, and application scope. Contact-based techniques (AFM, micropipette aspiration) provide high-resolution measurements of localized stiffness and viscoelasticity, while non-contact approaches (DEP, hydrodynamic stretching, optical tweezers) enable high-throughput analysis with minimal physical disruption. Furthermore, we evaluate the mechanical models used to extract biomechanical parameters and highlight modern data extraction processes, including automated image analysis. Crucially, this review addresses the significant discrepancies in measured properties that arise across different methods due to varying loading rates, boundary conditions, and model assumptions. By synthesizing these experimental and theoretical considerations, this review aims to guide the standardization and refinement of single-cell biomechanics for both fundamental research and translational biomedical applications.
This study reports a self-powered sensor that simultaneously measures wind speed and relative humidity using two triboelectric nanogenerators. The output of conventional triboelectric nanogenerators decreases with increasing ambient humidity. In previous wind sensors, this dependence has been treated as an error to be eliminated. Herein, we used the humidity dependence as a measurement signal. One device used a spin-coated polyvinyl alcohol–lithium chloride film, whose output proved far less sensitive to humidity; this behavior is consistent with reported spectroscopic evidence that lithium chloride retains the absorbed water within the film matrix rather than as a free surface layer. The other used a commercial nylon film, whose output steadily decreases with increasing humidity. Each film forms the fixed bottom layer of a device, with an identical polyethylene terephthalate film fluttering above it in the wind, making the bottom material the only material difference between the two devices. Therefore, both outputs vary with wind speed but exhibit markedly different responses to humidity, enabling wind speed and relative humidity to be determined from the paired signals over 3–5 m/s and 40–70
The demand for lightweight energy-absorbing structures has rapidly increased. Auxetic structures, such as re-entrant honeycombs, exhibit negative Poisson’s ratio behavior and high impact stability; however, their compressive response is highly nonlinear and sensitive to geometric parameters, making them computationally expensive and design inefficient. In this study, we present an end-to-end machine learning design acceleration framework for a quasi-2D re-entrant honeycomb that integrates PyAnsys Geometry and PyMechanical. The proposed framework employs automated geometry generation and finite element analysis (FEA) to build a dataset (n = 40) for interpretable surrogate modeling and covariance matrix adaptation evolution strategy optimization. Experimental validation was conducted using printed digital light processing specimens. An ensemble surrogate model (combining polynomial regression and gradient boosting regression) achieved the best 5-fold cross-validated performance (R² = 0.729). Surrogate-based optimization yielded an optimal design that increased energy absorption density (EAD) in refined-mesh FEA by 63.6
