Search Results (7)

Fast, spark-free detection of hydrogen leaks is indispensable for large-scale hydrogen deployment, yet electronic sensors remain power-intensive and prone to cross-talk. Optical schemes based on surface plasmons enable remote read-out, but single-metal devices offer either weak H2 affinity or poor plasmonic quality. Here we employ full-wave finite-difference time-domain (FDTD) simulations to map the hydrogen response of nanohole arrays (NAs) that can be mass-produced by colloidal lithography. Square lattices of 200 nm holes etched into 100 nm films of Pd, Mg, Ti, V, or Zr expose an intrinsic trade-off: Pd maintains sharp extraordinary optical transmission modes but shifts by only 28 nm upon hydriding, whereas Mg undergoes a large dielectric transition that extinguishes its resonance. Vertical pairing of a hydride-forming layer with a noble metal plasmonic cap overcomes this limitation. A Mg/Pd bilayer preserves all modes and red-shifts by 94 nm, while the predicted optimum Ag (60 nm)/Mg (40 nm) stack delivers a 163 nm shift with an 83 nm linewidth, yielding a figure of merit of 1.96—surpassing the best plasmonic hydrogen sensors reported to date. Continuous-film geometry suppresses mechanical degradation, and the design rules—noble-metal plasmon generator, buried hydride layer, and thickness tuning—are general. This study charts a scalable route to remote, sub-ppm, optical hydrogen sensors compatible with a carbon-neutral energy infrastructure. Full article

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization. Full article

In order to improve the thickness uniformity of the electroformed metal layer and components, a new electroforming technique is proposed—wire-anode scanning electroforming (WAS-EF). WAS-EF uses an ultrafine inert anode so that the interelectrode voltage/current is superimposed upon a very narrow ribbon-shaped area at the cathode, thus ensuring better localization of the electric field. The anode of WAS-EF is in constant motion, which reduces the effect of the current edge effect. The stirring paddle of WAS-EF can affect the fluid flow in the microstructure, and improve the mass transfer effect inside the structure. The simulation results show that, when the depth-to-width ratio decreases from 1 to 0.23, the depth of fluid flow in the microstructure can increase from 30% to 100%. Experimental results show that. Compared with the traditional electroforming method, the single metal feature and arrayed metal components prepared by WAS-EF are respectively improved by 15.5% and 11.4%. Full article

Micro-milling tools are usually used for the 3D precision processing of micro metal parts under ultra-high speed. However, due to the structural characteristics of small scale, variable cross-section, and weak stiffness, the vibration of micro-milling tools is weak and easily mutates, which can potential cause great harm to the stability and machining accuracy of machine tools. To reveal the transfer law of micro-milling tool vibration, guiding the method selection of tool vibration measurement and providing new means for mechanical model verification; firstly, the vibration mechanics model and vibration transfer matrix of the micro-milling tool were established. The vibration propagation characteristics of the micro-milling tool were analysed in contrast with the time domain and frequency domain, taking two representative micro-milling tools, Tool A and Tool B, as examples which with different cross-sections and structural parameters. Secondly, a micro-milling tool vibration measurement experimental system was set up and a sensor array with four optical fibre displacement sensors was used to obtain the vibration displacements at different positions of the tool under pulse and start-stop excitation. Finally, the results show the following: for Tool A, the max vibration displacement of the measurement of point 1 is about 3.5 times of measurement point 2 but near 18 times the measurement of point 3; meanwhile, compared with measurement point 1, the 16.8 kHz signal disappeared in measurement point 2, measurement point 3 and measurement point 4. However, for Tool B, the max vibration displacement of measurement point 1 is about 11.24 times the measurement of point 2; in contrast, the signal strength of the measurement of point 3 and point 4 is too weak to compare and analyse, although there are three resonant frequencies (10.2 kHz, 17.6 kHz, and 26.7 Hz) of Tool B based on the signal of measurement point 1, the 26.7 kHz signal disappeared in measurement point 2. The vibration amplitude of the tool tip decreases rapidly in the process of tool transfer, a bigger ratio cross-section with bigger attenuation of vibration amplitude and smaller size will aggravate this process. This study provides a reference for the selection of measuring points of micro-milling tool vibration displacement. Full article

The classification and use of robust methodologies in sensor array applications of electronic noses (ENs) remain an open problem. Among the several steps used in the developed methodologies, data preprocessing improves the classification accuracy of this type of sensor. Data preprocessing methods, such as data transformation and data reduction, enable the treatment of data with anomalies, such as outliers and features, that do not provide quality information; in addition, they reduce the dimensionality of the data, thereby facilitating the tasks of a machine learning classifier. To help solve this problem, in this study, a machine learning methodology is introduced to improve signal processing and develop methodologies for classification when an EN is used. The proposed methodology involves a normalization stage to scale the data from the sensors, using both the well-known $min-max$ approach and the more recent mean-centered unitary group scaling (MCUGS). Next, a manifold learning algorithm for data reduction is applied using uniform manifold approximation and projection (UMAP). The dimensionality of the data at the input of the classification machine is reduced, and an extreme learning machine (ELM) is used as a machine learning classifier algorithm. To validate the EN classification methodology, three datasets of ENs were used. The first dataset was composed of 3600 measurements of 6 volatile organic compounds performed by employing 16 metal-oxide gas sensors. The second dataset was composed of 235 measurements of 3 different qualities of wine, namely, high, average, and low, as evaluated by using an EN sensor array composed of 6 different sensors. The third dataset was composed of 309 measurements of 3 different gases obtained by using an EN sensor array of 2 sensors. A 5-fold cross-validation approach was used to evaluate the proposed methodology. A test set consisting of 25% of the data was used to validate the methodology with unseen data. The results showed a fully correct average classification accuracy of 1 when the MCUGS, UMAP, and ELM methods were used. Finally, the effect of changing the number of target dimensions on the reduction of the number of data was determined based on the highest average classification accuracy. Full article

Resistive random access memories (RRAMs) are a type of resistive memory with two metal electrodes and a semi-insulating switching material in-between. As the persistent technology node downscaling continues in transistor technologies, RRAM designers also face similar device scaling challenges in simple cross-point arrays. For this reason, a cost-effective 3D vertical RRAM (VRRAM) structure which requires a single pivotal lithography step is attracting significant attention from both the scientific community and the industry. Integrating an extremely thin plane electrode to such a structure is a difficult but necessary step to enable high memory density. In addition, experimentally verifying and modeling such devices is an important step to designing RRAM arrays with a high noise margin, low resistive-capacitive (RC) delays, and stable switching characteristics. In this work, we conducted an electromagnetic analysis on a 3D vertical RRAM with atomically thin graphene electrodes and compared it with the conventional metal electrode. Based on the experimental device measurement results, we derived a theoretical basis and models for each VRRAM design that can be further utilized in the estimation of graphene-based 3D memory at the circuit and architecture levels. We concluded that a 71% increase in electromagnetic field strength was observed in a 0.3 nm thick graphene electrode when compared to a 5 nm thick metal electrode. Such an increase in the field led to much lower energy consumption and fluctuation range during RRAM switching. Due to unique graphene properties resulting in improved programming behavior, the graphene-based VRRAM can be a strong candidate for stacked storage devices in new memory computing platforms. Full article

In this work, we focus on investigating electrothermal flow in rotating electric fields (ROT-ETF), with primary attention paid to the horizontal traveling-wave electrothermal (TWET) vortex induced at the center of the electric field. The frequency-dependent flow profiles in the microdevice are analyzed using different heat transfer models. Accordingly, we address in particular the importance of electrode cooling in ROT-ETF as metal electrodes of high thermal conductivity, while substrate material of low heat dissipation capability is employed to develop such microfluidic chips. Under this circumstance, cooling of electrode array due to external natural convection on millimeter-scale electrode pads for external wire connection occurs and makes the internal temperature maxima shift from the electrode plane to a bit of distance right above the cross-shaped interelectrode gaps, giving rise to reversal of flow rotation from a typical repulsion-type to attraction-type induction vortex, which is in good accordance with our experimental observations of co-field TWET streaming at frequencies in the order of reciprocal charge relaxation time of the bulk fluid. These results point out a way to make a correct interpretation of out-of-phase electrothermal streaming behavior, which holds great potential for handing high-conductivity analytes in modern microfluidic systems. Full article