Chao Jiang

Electrothermal micromirrors are attractive for optical imaging and sensing due to their large scan range, large displacement, and low-voltage operation. However, simultaneously achieving a large reflection area and a wide scan angle remains challenging because of inherent structural trade-offs. In this work, an electrothermal micromirror with a footprint of 4.5 mm × 4.5 mm was designed, simulated, fabricated, and experimentally characterized. Multiple complete bimorph actuators were monolithically integrated into each driving arm to enhance the actuation moment while optimizing thermal capacitance and heat conduction. The fabricated device achieved a mirror plate of 2.5 mm × 2.5 mm with an optical scan angle of ±20.5° at only 5 VDC, corresponding to a fill factor of 30.9% and a field of view of 41°. A maximum vertical displacement of 270 μm was obtained at 4 VDC, and the resonant frequency was 248 Hz with rise and fall times of 13.9 ms and 14.0 ms, respectively. Comparative analyses based on micro plate dimension, field of view, resonant frequency, and static and dynamic figures of merit demonstrate competitive performance compared to previously reported counterparts, indicating strong potential for optical imaging and sensing applications.

Estimating the parameters of sinusoidal signals is a fundamental problem in signal processing and in time-series analysis. Although various genetic algorithms and their hybrids have been introduced to the field, the problems pertaining to complex implementation, premature convergence, and accuracy are still unsolved. To overcome these drawbacks, an enhanced genetic algorithm (EGA) based on biological evolutionary and mathematical ecological theory is originally proposed in this study; wherein a prejudice-free selection mechanism, a two-step crossover (TSC), and an adaptive mutation strategy are designed to preserve population diversity and to maintain a synergy between convergence and search ability. In order to validate the performance, benchmark function-based studies are conducted, and the results are compared with that of the standard genetic algorithm (SGA), the particle swarm optimization (PSO), the cuckoo search (CS), and the cloud model-based genetic algorithm (CMGA). The results reveal that the proposed method outperforms the others in terms of accuracy, convergence speed, and robustness against noise. Finally, parameter estimations of real-life sinusoidal signals are performed, validating the superiority and effectiveness of the proposed method.

The electrical property of gas diffusion layers (GDLs) plays a significant role in influencing the overall performance of polymer electrolyte membrane fuel cells (PEMFCs). The electrical degradation performance of GDLs has not been reported sufficiently. Understanding the electrical degradation characteristics of GDLs is vital to better fuel cell performance, higher efficiency, and longer service time. This paper investigated the effective in-plane electrical resistivity of a commercial GDL by considering environmental and assembly conditions similar to those in use for the operation of PEMFCs. The effective in-plane electrical resistivity of the GDL, subjected to a series of freeze–thaw thermal cycles, was characterized to study its progressive electrical degradation with thermal cycles. Experimental results indicated that, under low compressive loads, the effective in-plane electrical resistivity of the commercial GDL showed weak anisotropy, and was greatly influenced by the transformation of carbon fiber connection in the porous layer. In particular, the thermal aging treatment on the GDL through the first 100 freeze–thaw cycles contributed a lot to its in-plane electrical degradation performance.

In this paper, a three-dimensional finite element analysis (3D-FEA) model for shear horizontal surface acoustic wave (SH-SAW) torque sensors is presented. Torque sensors play a significant role in various fields to ensure a reliable torque transmission in drivelines. Featured with the advantages of high propagation velocity, large Q-value, and good power capacity, SH-SAW based torque sensors are promising but very few studies have been carried out. In order to develop a successful sensor, understanding the characteristics of SH-SAWs produced on piezoelectric substrates and torque sensing modes is indispensable. Therefore, in this study, we first investigated the effect on the generation of waves when different Y-cut quartz substrates are engaged. Thereafter, analyses and comparisons, regarding the effect on the polarized displacement, wave guidance, and wave mode, were conducted for different configurations of wave-guide layer thickness to wavelength ratios (h_layer/λ) and materials. Results show that Y-cut quartz at an angle close to 36° with a gold (Au) layer varying from h_Au/λ = 0.02 to 0.03 thickness could be the most effective configuration for the excitation of SH-SAWs, compared to other combinations using platinum (Pt), titanium (Ti) and silicon dioxide (SiO₂). Finally, based on the FEA SH-SAW torque sensor model configuring with a Y + 36° quartz substrate and 0.025 λ-thick gold layer, the relationship between the applied torque and sensed voltage was examined, which shows a perfect linearity demonstrating the performance of the sensors.

In this article, a novel genetic algorithm (NGA) for estimating parameters of sinusoidal signals is presented. Estimation of the relative parameters from periodic time series is a classical research topic in engineering and applications. To get accurate results, high-efficient techniques are increasingly demanded. For this, we propose several enhancements for traditional genetic algorithm (GA) in this research to improve the performance. Comprehensively considering the advantages and disadvantages of different fitness individuals, a new selection mechanism is posted to preserve population diversity. For improving the information exchange between individuals, a two-step crossover operator is designed. In addition, we establish an adaptive mutation to avoid premature convergence. In order to examine the performance of the proposed algorithm, benchmark function based numerical experiments and parameter estimation of sinusoidal signal are carried out. As a result, the NGA outperforms GA and particle swarm optimization (PSO) in terms of accuracy and robustness.

In this paper, the mechanical degradation of a commercial gas diffusion layer subjected to repeated freeze–thaw thermal cycles is studied. In a fuel cell, the mechanical assembly state directly affects the performance of polymer electrolyte membrane fuel cells. Particularly, the gas diffusion layer repeatedly withstands the complex heat and humidity environmental conditions in which the temperature and humidity are always greatly changed. Studying the three-dimensional mechanical degradation of gas diffusion layers due to orthotropic properties is very useful in extending the lifetime and durability of fuel cells. To investigate this, we first established the standard freeze–thaw thermal cycle and studied the gas diffusion layer’s mechanical degradation performance with up to 400 repeated freeze–thaw thermal cycles. Furthermore, different types of failure in the gas diffusion layer caused by the repeated thermal aging treatment were observed using a scanning electron microscope, to explain the change in the mechanical deterioration. As a result, the different thermal failure plays different roles in the explanation of the gas diffusion layer’s mechanical degradation under different thermal cycles. In particular, the thermal failure that resulted from the first 100 thermal cycles has the greatest effect on the compressive and tensile performance, compared to the shear behavior.

This paper presents a novel genetic algorithm (GA) for the parameter estimation of surface acoustic wave (SAW) sensor signal. Differently from conventional GA, a two-step crossover operation is proposed for better interchange between variables and individuals. Besides, to resolve the problem of premature convergence and to increase the population diversity, an adaptive mutation mechanism is integrated as well. In order to verify the performance of the proposed method, benchmark function based studies using traditional GA, particle swarm optimization (PSO), and our proposed algorithm are implemented. At the end, parameter estimations for simulated signal are carried out. All the results highlight the superiority of our algorithm.

For the design of magnetostrictive MEMS devices, finite-element modeling (FEM), the determination of the magnetostrictive coefficient, and application examples were examined in this paper. The magnetostrictive coefficients are usually calculated by measuring the bending deflection of the cantilever on which magnetostrictive materials are deposited. In most of the research on the magnetostrictive deposited substrate for the derivation of structural formulas, it is commonly assumed that the flexural rigidities of magnetostrictive materials are negligible because magnetostrictive films are much thinner than substrate materials. In this paper, the effect of magnetostrictive thin film's elastic energy on the magnetostrictive coefficient is investigated. A self-coded and developed finite-element program for magnetostrictive actuators is formulated, where magnetic properties are considered as nonlinear, and elastic properties are assumed as linear. As a result, it is potentially shown that magnetostrictive thin-film micropatterned actuators would be accommodated for targeted actuating behavior through an FEM capability for evaluation and selective micropatterning technique for fabrication.