Flexural-Mode Piezoelectric Resonators: Structure, Performance, and Emerging Applications in Physical Sensing Technology, Micropower Systems, and Biomedicine.

Piezoelectric material-based devices have garnered considerable attention from scientists and engineers due to their unique physical characteristics, resulting in numerous intriguing and practical applications. Among these, flexural-mode piezoelectric resonators (FMPRs) are progressively gaining prominence due to their compact, precise, and efficient performance in diverse applications. FMPRs, resonators that utilize one- or two-dimensional piezoelectric materials as their resonant structure, vibrate in a flexural mode. The resonant properties of the resonator directly influence its performance, making in-depth research into the resonant characteristics of FMPRs practically significant for optimizing their design and enhancing their performance. With the swift advancement of micro-nano electronic technology, the application range of FMPRs continues to broaden. These resonators, representing a domain of piezoelectric material application in micro-nanoelectromechanical systems, have found extensive use in the field of physical sensing and are starting to be used in micropower systems and biomedicine. This paper reviews the structure, working principle, resonance characteristics, applications, and future prospects of FMPRs.

Leveraging Ferroelectret Nanogenerators for Acoustic Applications.

Ferroelectret nanogenerator (FENG), renowned for its remarkable electromechanical conversion efficiency and low Young's modulus, has gained significant attention in various acoustic applications. The increasing interest is attributed to the crucial role acoustic devices play in our daily lives. This paper provides a comprehensive review of the advancements made in using FENG for acoustic applications. It elaborates on the operational mechanism of FENG in acoustics, with a special focus on comparing the influence of different fabrication materials and techniques on its properties. This review categorizes acoustic applications of FENG into three primary areas: acoustic sensing, acoustic actuation, and acoustic energy harvesting. The detailed descriptions of FENG's implementations in these areas are provided, and potential directions and challenges for further development are outlined. By demonstrating the wide range of potential applications for FENG, it is shown that FENG can be adapted to meet different individual needs.

A Piezoelectrically Excited ZnO Nanowire Mass Sensor with Closed-Loop Detection at Room Temperature.

One-dimensional nanobeam mass sensors offer an unprecedented ability to measure tiny masses or even the mass of individual molecules or atoms, enabling many interesting applications in the fields of mass spectrometry and atomic physics. However, current nano-beam mass sensors suffer from poor real-time test performance and high environment requirements. This paper proposes a piezoelectrically excited ZnO nanowire (NW) mass sensor with closed-loop detection at room temperature to break this limitation. It is detected that the designed piezo-excited ZnO NW could operate at room temperature with a resonant frequency of 417.35 MHz, a quality factor of 3010, a mass sensitivity of -8.1 Hz/zg, and a resolution of 192 zg. The multi-field coupling dynamic model of ZnO NW mass sensor under piezoelectric excitation was established and solved. The nonlinear amplitude-frequency characteristic formula, frequency formula, modal function, sensitivity curve, and linear operating interval were obtained. The ZnO NW mass sensor was fabricated by a top-down method and its response to ethanol gas molecules was tested at room temperature. Experiments show that the sensor has high sensitivity, good closed-loop tracking performance, and high linearity, which provides great potential for the detection of biochemical reaction process of biological particles based on mechanics.

A Precise Closed-Loop Controlled ZnO Nanowire Resonator Operating at Room Temperature.

To realize the real-time measurement of masses of nanoparticles, virus molecules, organic macromolecules, and gas molecules, and to analyze their physical and chemical properties, a ZnO nanowire (NW) resonator operating at room temperature with an ultrahigh resonant frequency, real-time detection, and high precision was designed and developed in this study. The machining method is simple and easy to integrate into an integrated circuit. A closed-loop detection system based on a phase-locked loop (PLL) and frequency modulation technology (FM) was used to perform closed-loop testing of electromagnetically excited ZnO NW. The first-order resonance frequency of the resonator was 10.358 MHz, the quality factor Q value was about 600, the frequency fluctuation value fRMS was about 300 Hz, and the FM range could reach 200 kHz. The equivalent circuit model of the resonator was established, the parasitic parameters during the test were obtained, and the frequency accuracy and phase noise of the resonator were analyzed and tested. The experimental results show that the closed-loop system can automatically control the resonator in a wide range of frequency bands, with good tracking performance of the resonant frequency, small frequency fluctuation, and low phase noise level.

[A classification method of gene expression profile based on a locally linear embedding algorism with improved distance].

With its high dimensionalities, small samples and great noise, feature reduction of gene expression profile becomes quite necessary. The most common form of gene expression profile is nonlinear, and traditional dimensionality reduction methods can not project high dimensional data, whose initial dimensionalities are low, into low dimensional space. In this work, an improved distance locally linear embedding (LLE ) algorism was proposed to reduce the dimensionalities. LLE method is very sensitive to the closely-neighboring parameters. In order to enhance the robustness to the number of neighbors, in the paper we presented a novel distance to measure the distance between the samples for the purpose of reducing-the influence of distribution of samples. Experimental results demonstrated that the improved distance LLE can effectively extract information of classification features and greatly reduce the dimensionalities of data while maintaining a higher classification accuracy.

Local and global preserving semisupervised dimensionality reduction based on random subspace for cancer classification.

Precise cancer classification is essential to the successful diagnosis and treatment of cancers. Although semisupervised dimensionality reduction approaches perform very well on clean datasets, the topology of the neighborhood constructed with most existing approaches is unstable in the presence of high-dimensional data with noise. In order to solve this problem, a novel local and global preserving semisupervised dimensionality reduction based on random subspace algorithm marked as RSLGSSDR, which utilizes random subspace for semisupervised dimensionality reduction, is proposed. The algorithm first designs multiple diverse graphs on different random subspace of datasets and then fuses these graphs into a mixture graph on which dimensionality reduction is performed. As themixture graph is constructed in lower dimensionality, it can ease the issues on graph construction on highdimensional samples such that it can hold complicated geometric distribution of datasets as the diversity of random subspaces. Experimental results on public gene expression datasets demonstrate that the proposed RSLGSSDR not only has superior recognition performance to competitive methods, but also is robust against a wide range of values of input parameters.