The accurate measurement of nanoscale mechanical characteristics is crucial in the emerging field of soft condensed matter for industrial applications. An atomic force microscope (AFM) can be used to conduct nanoscale evaluation of the Young’s modulus on the target surface based on site-specific force spectroscopy. However, there is still a lack of well-organized study about the nanomechanical interpretation model dependence along with cantilever stiffness and radius of the tip apex for the Young’s modulus measurement on the soft materials. Here, we present the fast and accurate measurement of the Young’s modulus of a sample’s entire scan surface using the AFM in a newly developed PinPoint^TM nanomechanical mode. This approach enables simultaneous measurements of topographical data and force–distance data at each pixel within the scan area, from which quantitative visualization of the pixel-by-pixel topographical height and Young’s modulus of the entire scan surface was realized. We examined several models of contact mechanics and showed that cantilevers with proper mechanical characteristics such as stiffness and tip radius can be used with the PinPoint^TM mode to accurately evaluate the Young’s modulus depending on the sample type.

When ejaculated sperm travels through the vagina to the uterus, mucus secreted by the cervical canal generally filters out sperm having low motility and poor morphology. To investigate this selection principle in vivo, we developed a microfluidic sperm-sorting chip with a viscous medium (polyvinylpyrrolidone: PVP) to imitate the biophysical environment mimic system of the human cervical canal. The material property of the PVP solution was tuned to the range of viscosities of cervical mucus using micro-viscometry. The selection of high-quality human sperm was experimentally evaluated in vitro and theoretically analyzed by the convection-diffusion mechanism. The convection flow is shown to be dominant at low viscosity of the medium used in the sperm-sorting chip when seeded with raw semen; hence, the raw semen containing sperm and debris convectively flow together with suppressed relative dispersions. Also, it was observed that the sperm selected via the chip not only had high motilities but also normal morphologies and high DNA integrity. Therefore, the biomimetic sperm-sorting chip with PVP medium is expected to improve male fertility by enabling the selection of high-quality sperm as well as uncovering pathways and regulatory mechanisms involved in sperm transport through the female reproductive tract for egg fertilization.

The use of scientific instruments generally requires prior knowledge and skill on the part of operators, and thus, the obtained results often vary with different operators. The autonomous operation of instruments producing reproducible and reliable results with little or no operator-to-operator variation could be of considerable benefit. Here, we demonstrate the autonomous operation of an atomic force microscope using a machine learning-based object detection technique. The developed atomic force microscope was able to autonomously perform instrument initialization, surface imaging, and image analysis. Two cameras were employed, and a machine-learning algorithm of region-based convolutional neural networks was implemented, to detect and recognize objects of interest and to perform self-calibration, alignment, and operation of each part of the instrument, as well as the analysis of obtained images. Our machine learning-based approach could be generalized to apply to various types of scanning probe microscopes and other scientific instruments.

A quartz tuning fork and its qPlus configuration show different characteristics in their dynamic features, including peak amplitude, resonance frequency, and quality factor. Here, we present an electromechanical model that comprehensively describes the dynamic responses of an electrically driven tuning fork and its qPlus configuration. Based on the model, we theoretically derive and experimentally validate how the peak amplitude, resonance frequency, quality factor, and normalized capacitance are changed when transforming a tuning fork to its qPlus configuration. Furthermore, we introduce two experimentally measurable parameters that are intrinsic for a given tuning fork and not changed by the qPlus configuration. The present model and analysis allow quantitative prediction of the dynamic characteristics in tuning fork and qPlus, and thus could be useful to optimize the sensors’ performance.

Mechanical sensors provide core keys for high-end research in quantitative understanding of fundamental phenomena and practical applications such as the force or pressure sensor, accelerometer and gyroscope. In particular, in situ sensitive and reliable detection is essential for measurements of the mechanical vibration and displacement forces in inertial sensors or seismometers. However, enhancing sensitivity, reducing response time and equipping sensors with a measurement capability of bidirectional mechanical perturbations remains challenging. Here, we demonstrate the buckling cantilever-based non-linear dynamic mechanical sensor which addresses intrinsic limitations associated with high sensitivity, reliability and durability. The cantilever is attached on to a high-Q tuning fork and initially buckled by being pressed against a solid surface while a flexural stress is applied. Then, buckling instability occurs near the bifurcation region due to lateral movement, which allows high-sensitive detection of the lateral and perpendicular surface acoustic waves with bandwidth-limited temporal response of less than 1 ms.

We report a comprehensive study on depth filter functionality by using a slurry, colloidal silica, used for chemical mechanical polishing process in semiconductor manufacturing. We show that the depth filters with nominal pore size ranging from 0.1 μm to 0.5 μm selectively reduces the concentration of ‘large’ particles up to 80%, and maintains other critical properties of slurry including solid content ratio, mean diameter of particles, pH, zeta potential, and conductivity. Furthermore, we find the particle size-dependent filter efficiency, and the stable and allowable pressure drop across the filters. The depth filtration efficiently reduces the large particle concentration in slurry, and thus could be useful for suppressing the associated manufacturing defects such as micro-scratches and pits on the polished wafers.