Micromachines最新文献

This study presents a theoretical investigation into the voltammetric behavior of bipolar interfacial nanopores due to the effect of potential scan rate (1-1000 V/s). Finite element method (FEM) is utilized to explore the current-voltage (I-V) properties of bipolar interfacial nanopores at different bulk salt concentrations. The results demonstrate a strong impact of the scan rate on the I-V response of bipolar interfacial nanopores, particularly at relatively low concentrations. Hysteresis loops are observed in bipolar interfacial nanopores under specific scan rates and potential ranges and divided by a cross-point potential that remains unaffected by the scan rate employed. This indicates that the current in bipolar interfacial nanopores is not just reliant on the bias potential that is imposed but also on the previous conditions within the nanopore, exhibiting history-dependent or memory effects. This scan-rate-dependent current-voltage response is found to be significantly influenced by the length of the nanopore (membrane thickness). Thicker membranes exhibit a more pronounced scan-rate-dependent phenomenon, as the mass transfer of ionic species is slower relative to the potential scan rate. Additionally, unlike conventional bipolar nanopores, the ion current passing through bipolar interfacial nanopores is minimally affected by the membrane thickness, making it easier to detect.

The substitutional doping of nitrogen is an efficient way to modulate the electronic properties of graphene and carbon nanotubes (CNTs). Therefore, it could enhance their physical and chemical properties as well as offer potential applications. This paper provides an overview of the experimental and theoretical investigations regarding nitrogen-doped graphene and CNTs. The formation of various nitrogen defects in nitrogen-doped graphene and CNTs, which are identified by several observations, is reviewed. The electronic properties and transport characteristics for nitrogen-doped graphene and CNTs are also reviewed for the development of high-performance electronic device applications.

A single-domain nanomagnet, shaped like a thin elliptical disk with small eccentricity, has a double-well potential profile with two degenerate energy minima separated by a small barrier of a few kT (k = Boltzmann constant and T = absolute temperature). The two minima correspond to the magnetization pointing along the two mutually anti-parallel directions along the major axis of the ellipse. At room temperature, the magnetization fluctuates randomly between the two minima, mimicking telegraph noise. This makes the nanomagnet act as a "binary" stochastic neuron (BSN) with the neuronal state encoded in the magnetization orientation. If the nanomagnet is magnetostrictive, then the barrier can be depressed further by applying (electrically generated) uniaxial stress along the ellipse's major axis, thereby gradually eroding the double-well shape. When the barrier almost vanishes, the magnetization begins to randomly assume any arbitrary orientation (not just along the major axis), making the nanomagnet act as an "analog" stochastic neuron (ASN). The magnetization fluctuation then begins to increasingly resemble white noise. The full width at half maximum (FWHM) of the noise auto-correlation function decreases with increasing stress, as the fluctuation gradually transforms from telegraph noise to white noise. Consistent with this trend, the noise spectral density exhibits a 1/f^β spectrum (at high frequencies) with β decreasing from 2.00 to 1.88 with increasing stress. Stress can thus not only reconfigure a BSN to an ASN, which has its own applications, but it can also perform "noise engineering", i.e., tune the auto-correlation function and power spectral density, having applications in signal processing.

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized for the same comb finger area. With the fingers' length, width, and depth at 120 μm, 4 μm, and 45 μm, respectively, the optimized finger gap ratio is 2.5. The area of the proof mass is 750 μm × 560 μm, which leads to a theoretical thermomechanical noise of 9 μg/√Hz. The accelerometer has been fabricated using a modified silicon-on-glass (SOG) process, in which a groove is pre-etched into the glass to hold the metal electrode. This SOG process greatly improves the silicon-to-glass bonding yield. The measurement results show that the resonant frequency of the accelerometer is about 2.05 kHz, the noise floor is 28 μg/√Hz, and the nonlinearity is less than 0.5%.

Micro-nano-scale mechanical properties are vital for engineering and biological materials. The elastic modulus is generally measured by processing the force-indentation curves obtained by atomic force microscopy (AFM). However, the measurement precision is largely affected by tip shape, tip wear, sample morphology, and the contact model. In such research, it has been found that the radius of the sharp tip increases due to wear during contact scanning, affecting elastic modulus calculations. For flat-ended tips, it is difficult to identify the contact condition, leading to inaccurate results. Our research team has invented a nano-spherical tip, obtained by implanting focused helium ions into a silicon microcantilever, causing it to expand into a silicon nanosphere. This nano-spherical tip has the advantages of sub-micro size and a smooth spherical surface. Comparative tests of the elastic modulus measurement were conducted on polytetrafluoroethylene (PTFE) and polypropylene (PP) using these three tips. Overall, the experimental results show that our nano-spherical tip with a consistent tip radius, symmetrical geometric shape, and resistance to wear and contamination can improve precision in elastic modulus measurements of polymer materials.

The inertial measurement method of pipelines utilizes a Micro-Electro-Mechanical Systems Inertial Measurement Unit (MIMU) to get the three-dimensional trajectory of underground pipelines. The initial attitude is significant for the inertial measurement method of pipelines. The traditional method to obtain the initial attitude uses three-axis magnetometers to measure the Earth's magnetic field. However, the magnetic field in urban underground pipelines is intricate, which leads to the initial attitude being inaccurate. To overcome this challenge, a novel multi-position initial alignment method based on data backtracking for a single-axis FOG and a three-axis Micro-Electro-Mechanical Inertial Measurement Unit (MIMU) is proposed. Firstly, the configuration of the sensors is determined. Secondly, according to the three-point support structure of the pipeline measuring instrument, a three-position alignment scheme is designed. Additionally, an initial alignment algorithm using the data backtracking method is developed. In this algorithm, a rough initial alignment is conducted by the data from single-axis FOG, and a fine initial alignment is conducted by the data from FOG/MIMU. Finally, an experiment was conducted to validate this method. The experiment results indicate that the pitch and roll angle errors are less than 0.05°, and the azimuth angle errors are less than 0.2°. This improved the precision of the 3-D trajectory of underground pipelines.

Nanogenerators have garnered significant interest as environmentally friendly and potential energy-harvesting systems. Nanogenerators can be broadly classified into piezo-, tribo-, and hybrid nanogenerators. The hybrid nanogenerator used in this experiment is a nanogenerator that uses both piezo and tribo effects. These hybrid nanogenerators have the potential to be used in wearable electronics, health monitoring, IoT devices, and more. In addition, the versatility of the material application in electrospinning makes it an ideal complement to hybrid nanogenerators. However, despite their potential, several experimental variables, biocompatibility, and harvesting efficiency require improvement in the research field. In particular, maximizing the output voltage of the fibers is a significant challenge. Based on this premise, this study aims to characterize hybrid nanogenerators (HNGs) with varied structures and material combinations, with a focus on identifying HNGs that exhibit superior piezoelectric- and triboelectric-induced voltage. In this study, several HNGs based on coaxial structures were fabricated via electrospinning. PVDF-HFP and PAN, known for their remarkable electrospinning properties, were used as the primary materials. Six combinations of these two materials were fabricated and categorized into homo and hetero groups based on their composition. The output voltage of the hetero group surpassed that of the homo group, primarily because of the triboelectric-induced voltage. Specifically, the overall output voltage of the hetero group was higher. In addition, the combination group with the most favorable voltage characteristics combined PVDF-HFP@PAN(BTO) and PAN hollow, boasting an output voltage of approximately 3.5 V.

Piezoelectric ultrasonic motors (USMs) are actuators that use ultrasonic frequency piezoelectric vibration-generated waves to transform electrical energy into rotary or translating motion. USMs receive more attention because they offer distinct qualities over traditional magnet-coil-based motors, such as miniaturization, great accuracy, speed, non-magnetic nature, silent operation, straightforward construction, broad temperature operations, and adaptability. This review study focuses on the principle of USMs and their classifications, characterization, fabrication methods, applications, and future challenges. Firstly, the classifications of USMs, especially, standing-wave, traveling-wave, hybrid-mode, and multi-degree-of-freedom USMs, are summarized, and their respective functioning principles are explained. Secondly, finite element modeling analysis for design and performance predictions, conventional and nano/micro-fabrication methods, and various characterization methods are presented. Thirdly, their advantages, such as high accuracy, small size, and silent operation, and their benefits over conventional motors for the different specific applications are examined. Fourthly, the advantages and disadvantages of USMs are highlighted. In addition, their substantial contributions to a variety of technical fields like surgical robots and industrial, aerospace, and biomedical applications are introduced. Finally, their future prospects and challenges, as well as research directions in USM development, are outlined, with an emphasis on downsizing, increasing efficiency, and new materials.

This study developed a new metallography-property relationship neural network (MPR-Net) to predict the relationship between the microstructure and mechanical properties of 316L stainless steel built by laser powder bed fusion (LPBF). The accuracy R² of MPR-Net was 0.96 and 0.91 for tensile strength and Vickers hardness predictions, respectively, based on optical metallurgy images. Feature visualisation methods, such as gradient-weighted class activation mapping (Grad-CAM) and clustering, were employed to interpret the abstract features within the MPR-Net, providing insights into the molten pool morphology and grain formation mechanisms during the LPBF process. Experimental results showed that the optimal process parameters-190 W laser power and 700 mm/s scanning speed-yielded a maximum tensile strength of 762.83 MPa and a Vickers hardness of 253.07 HV_0.2 with nearly full densification (99.97%). The study marks the first application of a convolutional neural network (MPR-Net) to predict the mechanical properties of 316L stainless steel samples manufactured through laser powder bed fusion (LPBF) based on metallography. It innovatively employs techniques such as gradient-weighted class activation mapping (Grad-CAM), spatial coherence testing, and clustering to provide deeper insights into the workings of the machine learning model, enhancing the interpretability of complex neural network decisions in material science.

Light field cameras are unsuitable for further acquisition of high-quality images due to their small depth of field, insufficient spatial resolution, and poor imaging quality. To address these issues, we proposed a novel four-focal-square microlens and light field system. A square aspheric microlens array with four orthogonal focal lengths was designed, in which the aperture of a single lens was 100 μm. The square arrangement improves pixel utilization, the four focal lengths increase the depth of field, and the aspheric improves image quality. The simulations demonstrate pixel utilization rates exceeding 90%, depth-of-field ranges 6.57 times that of a single focal length, and image quality is significantly improved. We have provided a potential solution for improving the depth of field and image quality of the light field imaging system.