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There is a currently a lack of large-area plasma sterilization devices that can intelligently identify the shape of a wound for automatic sterilization. For this reason, in this work, a plasma sterilization device with wound-edge recognition was developed using a field-programmable gate array (FPGA) and a high-performance image-processing platform to realize intelligent and precise sterilization of wounds. SOLIDWORKS was used to design the mechanical structure of the device, and it was manufactured using 3D printing. The device used an improvement of the traditional Sobel detection algorithm, which extends the detection of edges in only the x and y directions to eight directions (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°), completing the wound-edge detection by adaptive thresholding. The device can be controlled according to different shapes of sterilization area to adjust the positioning of a single plasma-jet tube in the horizontal plane for two-dimensional movement; the distance between the plasma-jet tube and the surface of the object to be sterilized can be also adjusted in the vertical direction. In this way, motors are used to move the plasma jet and achieve automatic, efficient, and accurate plasma sterilization. It was found that a good sterilization effect could be achieved at both the culture-medium level and the biological-tissue level. The ideal sterilization parameters at the culture-medium level were a speed of 2 mm/s and a flow rate of 0.6 slm, while at the biological-tissue level, these values were 1 mm/s and 0.6 slm, respectively. ARTICLE HIGHLIGHTS: • Image processing combining eight-direction Sobel edge detection and a median-filtering adaptive threshold was used for wound-edge recognition. • A device with x, y, and z degrees of freedom was designed and 3D printed. • An FPGA-based sterilization platform combining edge-recognition detection, a three-degree-of-freedom kinematic device, and a plasma injector was built to precisely sterilize wounds. [ABSTRACT FROM AUTHOR]

The dissolution process of CO2 bubbles adhered to a flat plate in a rectangular channel at different flow velocities is studied experimentally and theoretically. In the experiments, the CO2 bubble is manually introduced by means of a needle connected to a micro-syringe in the rectangular channel filled with ultra-pure degassed water. The rectangular channel comprises a transparent 3D printed cavity and a replaceable plate. The water flow velocity in the rectangular channel can be precisely controlled using a liquid flow controller. The CO2 bubble is adhered to the flat plate, which is replaceable and has different wetting properties. The dissolution process of the bubble is visualized using a high-speed camera at different flow velocities. The changing bubble radius over time is calculated using an image processing program. A mathematical dissolution model is developed to estimate the dissolution durations of the CO2 bubble adhered to the flat plate, which includes the effect of contact angle and water flow velocity by using Sherwood number. With appropriate constants, the dissolution model exhibits good agreement with the experimental results. It is found that both the contact angle and water flow velocity play important roles in the bubble dissolution rate. An increase in water flow velocity can help improve the bubble dissolution rate. With a decrease in contact angle, the bubble dissolution rate becomes faster. The dissolution model is used to predict the maximum allowable bubble size at different scanning speeds in immersion lithography. [ABSTRACT FROM AUTHOR]

Acoustic measurements using sine sweeps are prone to background noise and non-stationary disturbances. Repeated measurements can be averaged to improve the resulting signal-to-noise ratio. However, averaging leads to poor rejection of non-stationary high-energy disturbances and, in the case of a time-variant environment, causes attenuation at high frequencies. This paper proposes a robust method to combine repeated sweep measurements using across-measurement median filtering in the time-frequency domain. The method, called Mosaic, successfully rejects non-stationary noise, suppresses background noise, and is more robust toward time variation than averaging. The proposed method allows high-quality measurement of impulse responses in a noisy environment. [ABSTRACT FROM AUTHOR]

This research proposes a novel dynamic image watermarking technique with features inspired by quantum computing principles. This method encodes binary values into qubits and embeds a watermark into an original image. The watermarking process is achieved by utilizing quantum circuits to manipulate the qubits representing the pixel values of the original and watermark images. To extract the watermark, encode each pixel value into a qubit, combine them using quantum operations, and then measure the resultant quantum state. This technique ensures the integrity and authenticity of the image by embedding a watermark that can be extracted with high fidelity. Simulation results show that our technique successfully embeds watermarks while maintaining picture quality. Moreover, this method exhibits robustness against common image processing attacks, highlighting its potential for secure image verification applications. [ABSTRACT FROM AUTHOR]

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research. [ABSTRACT FROM AUTHOR]

Establishing quantitative parameters for differentiating between healthy and diseased cartilage tissues by examining collagen fibril degradation patterns facilitates the understanding of tissue characteristics during disease progression. These findings could also complement existing clinical methods used to diagnose cartilage-related diseases. In this study, cartilage samples from normal, osteoarthritis (OA), and rheumatoid arthritis (RA) tissues were prepared and analyzed using polarization-resolved second harmonic generation (P-SHG) imaging and quantitative image texture analysis. The enhanced molecular contrast obtained from this approach is expected to aid in distinguishing between healthy and diseased cartilage tissues. P-SHG image analysis revealed distinct parameters in the cartilage samples, reflecting variations in collagen fibril arrangement and organization across different pathological states. Normal tissues exhibited distinct χ33/χ31 values compared with those of OA and RA, indicating collagen type transition and cartilage erosion with chondrocyte swelling, respectively. Compared with those of normal tissues, OA samples demonstrated a higher degree of linear polarization, suggesting increased tissue birefringence due to the deposition of type-I collagen in the extracellular matrix. The distribution of the planar orientation of collagen fibrils revealed a more directional orientation in the OA samples, associated with increased type-I collagen, while the RA samples exhibited a heterogeneous molecular orientation. This study revealed that the imaging technique, the quantitative analysis of the images, and the derived parameters presented in this study could be used as a reference for disease diagnostics, providing a clear understanding of collagen fibril degradation in cartilage. [ABSTRACT FROM AUTHOR]

A field-programmable photonic gate array is an integrated optical chip that combines electrical control and optical processing, enabling real-time reconfiguration of the optical path through software programming. While most current optical processors rely on Mach–Zehnder interferometer (MZI)-based architectures, those based on micro-disk resonators (MDRs) offer unique characteristics, including high integration and wavelength correlation, providing new ideas for programmable photonic chip architectures. In this paper, a scalable asymmetric MZI-assisted field-programmable micro-ring array (AMZI-FPRA) processor is proposed with a cell area of only 85 × 42 µm2. This design not only has high wavelength selectivity but also possesses dual adjustable wavelengths and coupling coefficients compared with traditional MDRs. By extending the cell into a 2 × 2 AMZI-FPRA using a two-dimensional square mesh approach, it is experimentally demonstrated that different optical path topologies can be realized with a compact footprint, including bandpass bandstop filtering, optical temporal differentiation, microwave delay, wavelength-division multiplexing/demultiplexing, and optical add-drop multiplexing. Increasing the array scale will enable more versatile and high-performance microwave photonic signal processing tasks. The scheme will be a promising candidate at the present time for reconfigurable programmable photonic signal processors due to its wide reconfigurability, on-chip integration, complementary metal–oxide–semiconductor-compatibility, and low power consumption. [ABSTRACT FROM AUTHOR]

Digital Signal Processing (DSP) finds a wide range of applications in various fields, including telecommunications, audio and video processing, biomedical engineering, radar systems, and image processing. Previous DSP designs faced limitations in available processing power and computational resources. Insufficient processing power could result in slower execution times, an inability to handle complex algorithms, or limited capacity to process high-speed or large-scale signals. As the demand for minimal power consumption in DSP circuits continues to grow, reversible logic and quantum-dot cellular automata (QCA) have emerged as promising technologies due to their inherent ability to reduce energy loss. Within this landscape, the arithmetic and logic unit (ALU) plays a vital role in complex circuitry, serving as a key component in digital signal processing applications. However, challenges persist, including high quantum cost and the need to limit the number of cells in the ALU design. To address these challenges, our research aims to develop an efficient ALU by integrating reversible logic and QCA technology. Our focus will be on generating essential components, such as Feynman gates, Fredkin gates, and full adder circuits, which serve as foundational building blocks for reversible logic and QCA designs. These components will be combined to construct a comprehensive ALU capable of performing 20 different operations. Our implementation efforts will be centered around QCADesigner, with a specific emphasis on digital signal processing systems that prioritize energy efficiency and optimal utilization of occupied areas. [ABSTRACT FROM AUTHOR]

Detecting diseases is a vital and crucial step in maintaining healthy, high-yielding plants. The challenge of manually identifying infections is arduous as well. The proposed work is to diagnose plant leaf diseases and discuss their origins and remedies. Image processing is used to discover the infected leaf and provide remedial measures through a mobile robot application. The use of machine learning techniques allows for the detection of leaf diseases using the support vector machine model, the K nearest neighbor model, and the Naïve Bayes classification to categorize the sample leaves. In this paper, the Momordica charantia leaf and the common four diseases dataset are developed, and a classification model is developed to identify and categorize leaf curl, downy mildew, powdery mildew, and angular leaf spot. Based on the disease classification, appropriate chemical pesticides are sprayed by controlling the servo actuated valve in the proposed agriculture robot, which is controlled and validated. The result reveals that the proposed approach has an average accuracy of 82% in identifying the disease type that remains more prevalent in Momordica charantia leaves than other compared classification algorithms. [ABSTRACT FROM AUTHOR]

Denoising algorithms for obtaining the effective data of depth images affected by random noise mainly focus on the processing of gray images. These algorithms are not distinct from traditional image-processing methods, and there is no way to evaluate the effectiveness of denoising after the point cloud transformation of denoised depth images. In this paper, the principle of fractional-order integral denoising is studied in detail and inverse distance weighted interpolation is introduced into a denoising model, which is based on the G–L (Grünwald–Letnikov) fractional-order integral to construct a fractional-order integral with an inverse distance weighted denoising model. The model is used to solve the blurring problem caused by sharp changes at the edge and achieves an excellent denoising effect. By using the optimized fractional-order integral denoising operator to construct a denoising model for depth images, the results of the experiments demonstrate that the fractional-order integral of the best denoising effect achieved by the model is −0.6 ≤ ν ≤ −0.4, and the peak signal-to-noise ratio is improved from +6 to +13 dB. In the same condition, median denoising has a distortion of −30 to −15 dB. The depth image that has been denoised is converted into an image of point clouds, and subjective evaluation indicates that the noise is effectively removed. On the whole, the results demonstrate that the fractional-order integral denoising operator with inverse distance weight shows the high efficiency and the outstanding effect in removing noise from depth images while maintaining the image related to the edge and texture information. [ABSTRACT FROM AUTHOR]

In conditions of low visibility, the scattering effect of atmospheric particles can result in images captured by cameras exhibiting a number of problems, including reduced contrast, color distortion, and blurred details. These issues can have a significant impact on the clarity and recognition of the images. In response to these issues, this study combines atmospheric scattering models with wavelet transform, introduces non-blind deconvolution algorithms and, finally, designs a special gamma correction function to ultimately develop a new foggy image restoration model. The relevant outcomes showed that when the dehazing model processed most image samples, its edge preservation was mainly concentrated in a relatively high range, namely [0.80, 0.96]. From the perspective of the structural similarity index, the discrete wavelet transform achieved the highest value of 0.8264. In addition, the peak signal-to-noise ratio of the improved gamma correction function was 18.346. The proposed algorithm for restoring foggy images has the advantages of high clarity, strong color restoration ability, and fast restoration speed, providing more reliable and efficient image data support for applications in related fields. [ABSTRACT FROM AUTHOR]

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups. [ABSTRACT FROM AUTHOR]

In this paper, an active ultrasound-based touchscreen technology is presented for real-time monitoring of multiple contacts on a glass panel of 20 × 19 cm. Both fundamental Lamb wave modes are generated by sending a linear chirp between 50 and 100 kHz to a single piezoelectric ceramic lead zirconate titanate. Measurement is performed using four piezoelectric ceramic lead zirconate titanate elements, and real-time localization and detection are performed on a system on module i.MX 8 M Nano. Results show that Lamb waves can be used for real-time multi-touch detection and localization on both sides of the panel. Moreover, the prototype developed allows for relative pressure measurement. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI) and computing technologies are currently disrupting the modeling and design paradigms in photonics. In this work, we present our perspective on the utilization of current AI models for photonic device modeling and design. Initially, through the physics-informed neural networks (PINNs) framework, we embark on the task of modal analysis, offering a unique neural networks-based solver and utilizing it to predict propagating modes and their corresponding effective indices for slab waveguides. We compare our model's predictions against theoretical benchmarks and a finite differences solver. Evidently, using 349 analysis points, the PINN approach had a relative percentage error of 0.69272% compared to the finite differences method, which had a percentage error of 1.28142% with respect to the analytical solution, indicating that the PINN approach was more accurate in conducting modal analysis. Our model's continuity over the entire solution domain enhances its performance and flexibility while requiring no training data due to its guidance by Maxwell's equations, setting it apart from most AI approaches. Our model design also flexibly enables simultaneous prediction of multiple modes over any specified intervals of effective indices. In addition, we present a novel reinforcement learning (RL)-based paradigm, employing an actor–critic model for inverse design. We utilize this paradigm to optimize the transmittance of a grating coupler by manipulating the device geometry. Comparing the obtained design to that obtained using the Particle Swarm Optimization (PSO) algorithm, our RL-based approach effectively produced a significant enhancement of 34% in 14 iterations only over the initial design compared to the PSO, which prematurely scored 27% enhancement in 30 iterations, proving that our model navigates the design space more efficiently, achieving a better design than PSO and resulting in a superior design. Based on these approaches, we discuss the future of AI in photonics in forward modeling and inverse design and the untapped potential in bringing these worlds together. [ABSTRACT FROM AUTHOR]

In order to reveal the modulation of particles on turbulent coherent structures and particle concentration variations induced by turbulence, particle image velocimetry experiments were performed in a dilute liquid–solid two-phase turbulent boundary layer. Large-scale coherent structures, including streaks and hairpin vortices, were extracted in the liquid–solid two-phase turbulent boundary layer by using multiple methods, such as proper orthogonal decomposition and finite-time Lyapunov exponent. The results showed that the addition of particles thickened the buffer layer of the average velocity profile, weakened the streamwise turbulence intensity at y+ > 10, strengthened the wall-normal turbulence in the near wall region (y+ < 52), and weakened the Reynolds stress in the logarithmic law region. A more thorough study of the turbulent structure revealed that the particles caused the mean spacing of the near-wall streak structure to increase, hairpin vortex migration to slow down, the uplift of hairpin vortex to be suppressed, and the overall size of the hairpin vortex packet to be compressed. This demonstrated that particles could modulate turbulence by inhibiting the development of hairpin vortices. [ABSTRACT FROM AUTHOR]

Monopole time reversal is an advanced noise source localization technique, but its sidelobe level in localizing sound sources is greatly affected by noise, leading to a decrease in the accuracy of sound source localization under a low signal-to-noise ratio. To address this issue, this paper proposes a time-domain monopole time reversal technique that excludes autocorrelation to reduce the impact of noise on sound source localization results. Numerical simulation and experimental results show that, compared to conventional monopole time reversal techniques, the proposed method can effectively reduce the sidelobes in sound source localization results and achieve accurate sound source localization under −20 dB low signal-to-noise ratio conditions. [ABSTRACT FROM AUTHOR]

Wafer inspection is crucial for semiconductor manufacturing, as it identifies defects in wafers before manufacturing. Wafer defect detection avoids wasting time and production capacity, boosts productivity, and assures production quality. In this paper, we propose an ensemble learning-based method for wafer defect detection that fuses the classification results of four models, namely, ResNet, ResNeSt, ResNeSt + CBAM, and ResNeSt + Self-attention. During the integration phase, we employ a hybrid strategy that combines weighted averaging and voting to determine weight coefficients. Our analysis shows that the model's performance surpasses that of the arithmetic mean model within an interval of 0.8–1, according to our mathematical derivations. Furthermore, results demonstrate and substantiate that optimal performance is attained by setting the weighting value to 1. We experimentally validated the effectiveness of the proposed method on the WM-811k industrial dataset. In the experiments, the ensemble learning based method achieves an accuracy of 99.70%, which outperforms the individual model. Our approach outperforms the traditional arithmetic mean model by combining the strengths of all prediction models to improve prediction accuracy. Experimental results demonstrate that the proposed method has the potential to be an ideal option for wafer defect detection. [ABSTRACT FROM AUTHOR]

Gastrointestinal disorders are a class of prevalent disorders in the world. Capsule endoscopy is considered an effective diagnostic modality for diagnosing such gastrointestinal disorders, especially in small intestinal regions. The aim of this work is to leverage the potential of deep convolutional neural networks for automated classification of gastrointestinal abnormalities from capsule endoscopy images. This method developed a deep learning architecture, GastroNetV1, an automated classifier, to detect abnormalities in capsule endoscopy images. The gastrointestinal abnormalities considered are ulcerative colitis, polyps, and esophagitis. The curated dataset consists of 6000 images with "ground truth" labeling. The input image is automatically classified as ulcerative colitis, a polyp, esophagitis, or a normal condition by a web-based application designed with the trained algorithm. The classifier produced 99.2% validation accuracy, 99.3% specificity, 99.3% sensitivity, and 0.991 AUC. These results exceed that of the state-of-the-art systems. Hence, the GastroNetV1 could be used to identify the different gastrointestinal abnormalities in the capsule endoscopy images, which will, in turn, improve healthcare quality. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of bubble growth and collapse during gas–liquid two-phase transient flow is crucial for minimizing pipeline wear and maintaining the normal operation of the pipeline system. This paper focuses on obtaining the bubble trajectory, movement velocity, and volume change of gas bubbles. The bubble images during the transient flow are first denoised and enhanced, then the moving target detection algorithm and watershed segmentation algorithm are used to segment the bubbles, and the contour reconstruction of the bubbles is performed using fitting to extract the relevant parameters. The movement trajectories of the bubbles are then traced by analyzing the trajectories and velocities of growth and collapse of the first bubbles during the transient flow, specifically when the flow rate of the hydraulic pipeline is 0.75 m/s. Finally, the changes in gas content during the transient flow in the pipeline under different initial flow rates are compared between the algorithm proposed here and the simulation results. [ABSTRACT FROM AUTHOR]

Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain–machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain–machine interfacing. [ABSTRACT FROM AUTHOR]

Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs. [ABSTRACT FROM AUTHOR]

Photonic neuromorphic computing has emerged as a promising avenue toward building a high-speed, low-latency, and energy-efficient non-von-Neumann computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. Linear weighting and nonlinear spiking activation are two fundamental functions of a SNN. However, the nonlinear computation of PSNN remains a significant challenge. Therefore, this perspective focuses on the nonlinear computation of photonic spiking neurons, including numerical simulation, device fabrication, and experimental demonstration. Different photonic spiking neurons are considered, such as vertical-cavity surface-emitting lasers, distributed feedback (DFB) lasers, Fabry–Pérot (FP) lasers, or semiconductor lasers embedded with saturable absorbers (SAs) (e.g., FP-SA and DFB-SA). PSNN architectures, including fully connected and convolutional structures, are developed, and supervised and unsupervised learning algorithms that take into account optical constraints are introduced to accomplish specific applications. This work covers devices, architectures, learning algorithms, and applications for photonic and optoelectronic neuromorphic computing and provides our perspective on the challenges and prospects of photonic neuromorphic computing based on semiconductor lasers. [ABSTRACT FROM AUTHOR]

Parametric x-ray radiation (PXR) is a prospective mechanism for producing directional, tunable, and quasi-coherent x-rays in laboratory-scale dimensions, yet it is limited by heat dissipation and self-absorption. Resolving these limits, we show the PXR source flux is suitable for medical imaging and x-ray spectroscopy. We discuss the experimental feasibility of these findings for a compact commercial PXR source. [ABSTRACT FROM AUTHOR]

Transformation optics applied to low frequency magnetic systems have been recently implemented to design magnetic field concentrators and cloaks with superior performance. Although this achievement has been amply demonstrated theoretically and experimentally in bulk 3D macrostructures, the performance of these devices at low dimensions remains an open question. In this work, we numerically investigate the non-monotonic evolution of the gain of a magnetic metamaterial field concentrator as the axial dimension is progressively shrunk. In particular, we show that in planar structures, the role played by the diamagnetic components becomes negligible, whereas the paramagnetic elements increase their magnetic field channeling efficiency. This is further demonstrated experimentally by tracking the gain of superconductor-ferromagnet concentrators through the superconducting transition. Interestingly, for thicknesses where the diamagnetic petals play an important role in the concentration gain, they also help to reduce the stray field of the concentrator, thus limiting the perturbation of the external field (invisibility). Our findings establish a roadmap and set clear geometrical limits for designing low dimensional magnetic field concentrators. [ABSTRACT FROM AUTHOR]

Material-by-design has been a long-standing aspiration that has recently become a reality. Such designer materials have been repeatedly demonstrated using the top-down approach of mechanical exfoliation and stacking, leading to a variety of artificial 2D heterostructures with new properties that are otherwise unattainable. Consequently, tremendous research frontiers in physics, chemistry, engineering, and life science have been created. While thousands of layered crystals exist in nature, only a few dozen of them with manageable chemical-stability have been made into heterostructures using this method. Moreover, experimental investigations of materials that have received limited exploration in the 2D realm, such as cuprates, halides, and perovskites, along with their heterostructures, have been fundamentally hindered by their rapid chemical degradation. Another critical challenge imposed by exfoliating and stacking 2D layers in ambient environment is the absorption of itinerant gas molecules that further contaminate sensitive 2D interfaces in the heterostructures. Such contamination and compromised material properties significantly hinder surface-sensitive local probes—scanning probe microscopy (SPM)—that often require nanometer to atomic scale surface cleanliness. In this article, we aim to provide a technical review of recent development toward 2D materials and heterostructure fabrication in more controlled environments that are suitable for SPM characterizations. These include the development of more efficient mechanical exfoliation and dry-transfer techniques, as well as the incorporation of 2D material exfoliation and transfer in inert gas, low vacuum, and, eventually, ultra-high vacuum environments. Finally, we provide an outlook on the remaining challenges and opportunities in ultra-clean 2D material fabrication techniques. [ABSTRACT FROM AUTHOR]

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness. [ABSTRACT FROM AUTHOR]

Dynamic range is one of the primary limitations that restricts digital image sensors from acquiring more visual information. Current high dynamic range (HDR) imaging techniques entail a trade-off between dynamic range and visual fidelity. In this work, we propose a HDR imaging method, termed PE-HDR, to achieve both a wide dynamic range and high visual fidelity without additional complex post-processing algorithms. Instead of merging a bracketed exposure sequence, the PE-HDR captures HDR images in a single shot using optical coded pixel-wise exposure control, enabling cost-effective and flexible HDR imaging. By incorporating a differentiable optical encoder and a neural network decoder, we jointly optimize the imaging pipeline from light irradiance to digital image signals, thereby refining the pixel-wise exposure control strategy and improving image fidelity. Both simulations and experiments demonstrate that the proposed method achieves a dynamic range of up to 120 dB and an excellent visual fidelity with spatial resolution of up to 2560 × 1600 pixels. [ABSTRACT FROM AUTHOR]

This research provides a detailed explanation of the design, simulation, and experimental of quad-square metamaterial-based negative-index unit cells for S-band applications. The Computer Simulation Technology 2022 licensee version was utilized to design and obtain numerical results for the unit cell. The proposed unit cell for the metamaterial has dimensions of 5 × 5 × 1.57 mm3. The substrate chosen was FR-4, resulting in a substantial effective medium ratio value of 19.07. A series of systematic parametric studies were conducted to optimize the quad square metamaterial structure. Key parameters, such as substrate types, unit cell arrays, thicknesses of substrate, and split gaps, were varied to determine their impact on the structure. The validated equivalent circuit result was compared to the simulated results, showing a significant agreement. The demonstrated correlation between simulation and experimental data highlights the dependability of the proposed quad-square metamaterial, positioning it as a viable option for a range of electromagnetic applications, such as communication systems, sensors, and imaging devices. [ABSTRACT FROM AUTHOR]

Solar energy is potentially the largest source of renewable energy for providing electrical power for human society. However, significant advances are required to make photovoltaic technologies have a low-carbon footprint in manufacture, be environmentally friendly at the end of their lives through recyclability, and be biodegradable. Here we report dissolvable organic photovoltaic devices based on poly(2-hydroxyethyl methacrylate), which show equal power conversion efficiency to their glass substrate-based counterparts. We use a novel method of including smectic liquid crystal (7-dioctyl[1]benzothieno[3,2- b][1]benzothiophene, C8-BTBT) as a crystal phase regulator in the heterojunction donor:acceptor polymer system to maintain the disposable organic solar cell efficiency without pre- or post-thermal annealing. The results show strong promise not only for more sustainable solar-cell fabrication but also as disposable and biocompatible solar cells for self-powered (energy harvesting) wearable and biomedical devices. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the dynamic characteristics of vacuum metal vapor arcs under rotating and traditional direct contact separation modes. Non-symmetrical pure copper electrodes with an anode radius of 7.5 mm and a cathode radius of 22.5 mm are employed in the experiments. The permanent magnet actuator provides an average opening speed of 1.0 m/s for cathode direct separation and 1–3 deg/ms for rotation. Results show that electrode rotation significantly influences the distribution and motion speed of the arc root on the cathode surface. [ABSTRACT FROM AUTHOR]

In this paper, we propose and demonstrate a high precision three-dimensional (3D) ellipse fitting correction method for optical fiber composite overhead ground wire (OPGW) galloping monitoring, based on weak fiber Bragg grating array. 3D ellipse formed by three outputs of the system is fitted by the coordinate rotation and least square methods, and direct current (DC) components and alternate current (AC) component coefficients of the three outputs are obtained. By differentiating and cross-multiplying the three outputs, those DC components are zeroized, AC component coefficients are normalized, and finally, the interference phase signal is calculated. The experimental results show that the proposed method can demodulate the vibration signals better, and the fitted R2 is above 0.99. The system is implemented on a 220 kV transmission line in the plateau region of Western China, forecasting a galloping period with a wavelength of about 60 m within the span accurately. This method demonstrates suitability for monitoring OPGW transmission line galloping events. [ABSTRACT FROM AUTHOR]

Micro-newton thrusters are widely utilized in the field of astronautics. Typically, the precision of micro-newton thrust measurement fundamentally depends on the background noise level. In this research, we introduce the Multichannel Variational Mode Decomposition Input Deep Residual Shrinkage Network (MV-DRSN) to identify the effective signals merged in the background noise. Experimental studies in vacuum were conducted to investigate the effect of noise reduction on MV-DRSN. It is shown that a steady-state signal with 0.1 μN as the minimum change unit can be recovered from the noises with an amplitude of 0.8 μN with an accuracy of 96.7% using MV-DRSN. In addition, the superiority of MV-DRSN over conventional ResNet has been validated, and its effectiveness in practical scenarios is verified. The proposed method has potential for noise reduction of steady-state sensor signals. [ABSTRACT FROM AUTHOR]

This paper introduces an improved weighted nuclear norm with a total variation model tailored for removing multiplicative noise. The model incorporates a weight matrix to regularize the residual matrix, effectively leveraging image redundancy to differentiate various statistical properties of the noise. Since there is no guarantee of a unique solution, the model is reformulated as a linear equality constraint problem and decomposed into two subproblems. These are addressed by using the alternating direction method of multipliers and the split Bregman method, respectively. In addition, each alternative update step has a closed-form and convergent solution. After obtaining the denoised image in the log-domain, the recovered image is given by using the exponential function and bias correction. Experimental evaluations demonstrate the efficacy of our algorithms in enhancing image restoration quality. [ABSTRACT FROM AUTHOR]

There is a connection that has been established between the virus responsible for monkeypox and the formation of skin lesions. This virus has been detected in Africa for many years. Our research is centered around the detection of skin lesions as potential indicators of monkeypox during a pandemic. Our primary objective is to utilize metaheuristic optimization techniques to improve the performance of feature selection and classification algorithms. In order to accomplish this goal, we make use of deep learning and a transfer learning technique to extract attributes. The GoogleNet network, a deep learning framework, is used to carry out feature extraction. Furthermore, the feature selection process is conducted using a binary version of the dynamic Al-Biruni earth radius optimization (DBER). After that, the convolutional neural network is used to assign labels to the selected features from the collection. To improve the classification accuracy, adjustments are made to the convolutional neural network by utilizing the continuous version of the DBER algorithm. We used a range of metrics to analyze the different assessment methods, including accuracy, sensitivity, specificity, positive predictive value (P-value), negative predictive value (N-value), and F1-score. They were compared to each other. All the metrics, including the F1-score, sensitivity, specificity, P-value, and N-value, achieved high values of 0.992, 0.991, and 0.993, respectively. The outcomes were achieved by combining feature selection with the use of a convolutional neural network. After optimizing the parameters in the convolutional neural network, the proposed method achieved an impressive overall accuracy rate of 0.992. [ABSTRACT FROM AUTHOR]

The compressive strength of concrete with low strength aggregate volume fractions of 10%, 20%, and 30% was calculated using both the graphical analysis method and mesomechanics method. In the calculation method based on graphical analysis, three-dimensional random spherical aggregate models of concrete with different volume contents of low strength aggregate were established and sliced. The results show that the graphical analysis method can effectively calculate the compressive strength of concrete with different volume contents of low strength aggregate. In the graphical analysis method, the relative errors of the calculated compressive strength of concrete with low strength aggregate volume fractions of 10%, 20%, and 30% were 4.84%, 4.84%, and 6.43%, respectively. Three-phase concrete models composed of mortar, aggregate, and interfacial transition zone were analyzed through the method of mesomechanics. The calculation results of the mesomechanics method show that the compressive strength of concrete was controlled by low strength aggregate, and the calculated compressive strength of concrete decreased with the increase in low strength aggregate volume content. In the mesomechanics method, the relative errors of the calculated compressive strength of concrete with low strength aggregate volume fractions of 10%, 20%, and 30% were 1.36%, 1.74%, and 3.7%, respectively. It can be found that the calculation results of the mesomechanics method are closer to the test values and have smaller relative errors, which indicate that the calculation method of mesomechanics theory is superior to the method of graphical analysis. [ABSTRACT FROM AUTHOR]

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model's predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia's complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden. [ABSTRACT FROM AUTHOR]

The development and advancement of spectroscopy techniques for the timely diagnosis of diabetic foot ulcers (DFUs) requires foot-skin equivalent reference objects, such as tissue phantoms. Therefore, herein, we describe the fabrication and characterization of a solid and single-layered tissue-mimicking phantom, which mimics the optical properties of the sole skin of the normal and diabetic foot. We found that 600 µl of 7% w/v titanium dioxide (TiO2) per 100 ml epoxy solution could provide a solid tissue phantom with the optical properties equivalent to those of a healthy foot. On the contrary, adding 10 µl of India ink to the same phantom concentration could provide a phantom with optical properties equivalent to those of diabetic foot sole skin. Monte Carlo simulations have been performed based on the calculated optical properties and geometry of the developed phantom and found that the fluence distribution was higher in the normal foot compared to the diabetic foot. To investigate the feasibility of the developed phantoms in spectroscopy study, diffuse reflectance measurements have been performed. The diffuse reflectance of the normal phantom plate was higher than that of the diabetic phantom plate. Furthermore, phantom inhomogeneity error was measured and found to be less than 2%, indicating that nearly homogeneous phantoms have been developed. The stability study confirmed that the optical properties of the developed phantoms do not change significantly after a six-month period, which potentially makes them a suitable controlled reference object that may be used in spectroscopy studies for DFU diagnosis and assessment of its severity. [ABSTRACT FROM AUTHOR]

Predicting acoustic transmission loss in the SOFAR channel faces challenges, such as excessively complex algorithms and computationally intensive calculations in classical methods. To address these challenges, a deep learning-based underwater acoustic transmission loss prediction method is proposed. By properly training a U-net-type convolutional neural network, the method can provide an accurate mapping between ray trajectories and the transmission loss over the problem domain. Verifications are performed in a SOFAR channel with Munk's sound speed profile. The results suggest that the method has potential to be used as a fast predicting model without sacrificing accuracy. [ABSTRACT FROM AUTHOR]

Three-dimensional reciprocal space mapping (3D-RSM) offers crucial insights into the intricate microstructural properties of materials, including spatial domain distribution, directional long-range ordering, multilayer-substrate mismatch, layer tilting, and defect structure. Traditionally, 3D-RSMs are conducted at synchrotron facilities where instrumental resolution is constrained in all three directions. Lab-based sources have often been considered suboptimal for 3D-RSM measurements due to poor instrumental resolution along the axial direction. However, we demonstrate that, by employing three-dimensional reciprocal space x-ray computed tomography (RS-XCT), the same perceived limitation in resolution can be effectively leveraged to acquire high quality 3D-RSMs. Through a combination of ultrafast reciprocal space mapping and computed tomography reconstruction routines, lab-based 3D-RSMs achieve resolutions comparable to those obtained with synchrotron-based techniques. RS-XCT introduces a practical modality for lab-based x-ray diffractometers, enabling high-resolution 3D-RSM measurements on a variety of materials exhibiting complex three-dimensional scattering landscapes in reciprocal space. [ABSTRACT FROM AUTHOR]

The trade-off relationship between cost and performance is a major challenge in the development of surface-enhanced Raman spectroscopy (SERS) sensors for practical applications. We propose a roll-to-roll system with incorporated vacuum sputtering to manufacture Ag-coated nanodimples (Ag/NDs) on A4-scale films in a single step. The Ag/ND SERS platforms were prepared via O2 ion beam sputtering and Ag sputtering deposition. The concave three-dimensional spaces in the Ag/NDs functioned as hotspots, and their optimal fabrication conditions were investigated with two variables: moving speed and Ag thickness. The entire process was automated, which resulted in highly consistent optical responses (i.e., relative standard deviation of ∼10%). The activation of plasmonic hotspots was demonstrated by electric-field profiles calculated via the finite-difference time-domain method. The wavelength dependency of the Ag/ND platforms was also examined by dark-field microscopy. The results indicate that the developed engineering technique for the large-scale production of Ag/ND plasmonic chips would likely be competitive in the commercial market. [ABSTRACT FROM AUTHOR]

The visual motion blur imaging for the feature recognition process of silicon nitride bearing roller fissures is a pathological problem. This is solved by proposing squeeze-and-excitation asymmetric fusion of multi-scale features with high-frequency loss attention coupled U-Net (MHU-Net). The visual motion blur elimination of fissure features on silicon nitride bearing rollers is achieved. In the deblurring model, the multi-scale feature information on silicon nitride bearing roller fissures is blocked and there is weak correlation between channels. A design for an asymmetric fusion multi-scale feature module under the channel information compression–excitation mode is proposed. It successfully balances the channel information from different scales while integrating multi-scale features in image fusion. The high-frequency region of fissure features on silicon nitride bearing rollers is analyzed. Around the high-frequency feature loss in the multi-frequency domain of images combined with spatial feature loss, a multi-frequency band high-frequency loss attention module is built. Then, the complete structural details of silicon nitride bearing roller fissures are obtained. The proposed algorithm achieves a peak signal-to-noise ratio of 27.58 and a structural similarity of 0.847 on our self-made silicon nitride defect motion dataset. The visual motion blur of fissure features is noticeably eliminated. The restored image exhibits complete details in the feature structures and overall region smoothness. [ABSTRACT FROM AUTHOR]

Early fire identification is critical to saving lives and property. One viable strategy for improving fire detection systems' efficacy and precision is the use of Internet of Things (IoT) technology. This article details research into the conception and development of an IoT, ESP32-based, Arduino MEGA-connected smart pre-alert detection system for fires. Because of its flexible nature, the Arduino microcontroller can easily exchange data with various sensors for real-time monitoring and analysis. This study surveys the existing literature to provide insight into many approaches to early fire detection (forecasting), such as sensor fusion methods and the use of the intelligence concept as a pre-predictor. The research highlights the need to set up sophisticated fire monitoring and alarm systems and to develop predictive models based on sensor data. This study illustrates how embedded systems via IoT technology and the ESP32 architecture may improve the efficiency and dependability of very early fire detection systems. This research adds to the expanding corpus of information that may be used to improve fire safety in contemporary settings. The proposal's sensors include an Arduino MEGA, an ESP32, a MAX6675 module, an MQ-Smoke, and an MQ-Natural Gas for collecting data, which are then processed using Cloud Firebase and a sleek mobile interface that can be reached overseas at any time. [ABSTRACT FROM AUTHOR]

In order to improve the immunity and speed tracking performance of active inverters in resonant overvoltage flexible suppression under load variation, parameter perturbation, and other uncertainties, a current control strategy of a resonant overvoltage flexible grounding device based on a cascading linear-nonlinear active disturbance rejection controller is proposed in this paper. First, the mathematical model of the flexible device considering load disturbance and parameter mismatch is established. Second, a cascade of linear and nonlinear extended state observers is designed to estimate and compensate the uncertain disturbance of the system in real time. The former linear extended state observer ensures the stability of the system under large disturbance, and the latter nonlinear extended state observer further improves the estimation accuracy of the disturbance by using the nonlinear mechanism. Finally, the flexible grounding system based on PI control and cascaded linear-nonlinear active disturbance rejection control (CL-NLADRC) is simulated and compared. Compared with PI control, NLADRC, and CLADRC, the current drop of the proposed CL-NLADRC during loading is 0.03 kA, which is 81.1%, 85.7%, and 73% of the three, respectively, and the regulation time is 13 ms, which is 82%, 75%, and 18.7% shorter than the three, respectively. The speed increase during load shedding is 93.5%, 80.5%, and 74.3% of the three, and the adjustment time is shortened by 80%, 73%, and 28%, respectively. The superiority of the proposed method is verified. [ABSTRACT FROM AUTHOR]

To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls' surface images are 95.00%, 92.50%, and 92.50%, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the safety and energy exchange efficiency of electric thermal pyrotechnic devices, a new energy exchange element technology of high energy metal (W) film/aluminum nitride ceramic was explored by taking advantage of the high thermal conductivity of aluminum nitride ceramic materials. High-temperature co-fired ceramics were used to prepare the new system energy exchangers, and the prototype of the new system energy exchanger of high-energy metal (W) film/aluminum nitride was obtained. Through infrared microscopic test and ignition performance test, the new system energy exchanger of high-energy metal (W) film/aluminum nitride can satisfy the 1A1W5min non-ignition test, and the ignition current of 50% is 2.80 A. The response current of 99.9% is 3.54 A, and the response current of 0.1% is 2.06 A, which provides technical support for the application of a new type of passivated electric thermal pyrogenic product. [ABSTRACT FROM AUTHOR]

Precise density and thermal expansion measurements of refractory materials are extremely challenging due to the chemical reactions and thermal gradients at ultrahigh temperatures. The UV image technique has been successfully applied to the density measurements at high temperatures above 3000 K. However, intense radiation from the sample can blur the edge of the sample image, leading to uncertain measurements. In this study, we investigate the influence of the contrast between the UV background light and sample radiation (B–S) on density measurements. We find that lower B–S contrast can significantly affect the calibration factor that converts image pixels to real size, resulting in underestimated density with scattering and overestimated thermal expansion coefficients at ultrahigh temperatures. This result underscores the necessity of adequate B–S contrast to ensure the precision of density measurements using UV imaging methods for refractory materials beyond 3000 K. By considering the critical minimum value of the B–S contrast, we successfully measure the densities and thermal expansion coefficients of four refractory metal liquids (tungsten, rhenium, osmium, and tantalum) with the improved UV technique, which are crucial for high-temperature industries. This work will be valuable for other imaging methods measuring the thermophysical properties at ultrahigh temperatures. [ABSTRACT FROM AUTHOR]

In this article, we demonstrated the physical application of a portable infrared (IR) security scanning system for the non-contact and stand-off detection of target objects concealed underneath clothing. Such a system combines IR imaging and transfer learning with convolutional neural networks (CNNs) to enhance the detection of weak thermal signals and automate the classification of IR images. A mid-wavelength IR detector was used to record the real-time heat emitted from the clothing surface by human subjects. Concealed objects reduce the transmissivity of IR radiation from the body to the clothing surface, generally showing lower IR intensity compared to regions without objects. Due to limited resources for training data, the transfer learning approach was applied by fine-tuning a pre-trained CNN ResNet-50 model using the ImageNet database. Two image types were investigated here, i.e., raw thermal and Fuzzy-c clustered images. Receiver operating characteristic curves were built using a holdout set, showing an area-under-the-curve of 0.8934 and 0.9681 for the raw and Fuzzy-c clustered image models, respectively. The gradient-weighted class activation mapping visualization method was used to improve target identification, showing an accurate prediction of the object area. It was also found that complex clothing, such as those composed of materials of different transmissivity, could mislead the model in classification. The proposed IR-based detector has shown potential as a non-contact, stand-off security scanning system that can be deployed in diverse locations and ensure the safety of civilians. [ABSTRACT FROM AUTHOR]

Defect detection on wafers holds immense significance in producing micro- and nano-semiconductors. As manufacturing processes grow in complexity, wafer maps may display a mixture of defect types, necessitating the utilization of more intricate deep learning models for effective feature learning. However, sophisticated models come with a demand for substantial computational resources. In this paper, we propose an efficient deep learning framework designed explicitly for mix-type wafer map defect pattern recognition. Our proposed model incorporates several crucial design elements, including lightweight convolutions, bottleneck residual connections, efficient channel attention mechanisms, and optimized activation functions, enabling it to learn spatial and channel features efficiently. We conduct evaluations on a real-world dataset containing 38 mixed-type defect patterns. The experimental results demonstrate that our framework maintains a high level of accuracy while possessing a compact parameter size and remarkably fast inference speed. [ABSTRACT FROM AUTHOR]

This study aims to develop a novel portable reversible thermochromic patch and evaluate its potential applications in temperature monitoring and indication. The film is based on the concept of electronic charge transfer thermochromic coatings, enabling accurate detection of high temperatures. Experimental results demonstrate that when the temperature of the monitored device's circuit reaches 70 °C or above, the color of the film changes from deep blue to colorless, indicating potential overheating in the power grid. Using salt spray corrosion testing and cycle performance testing, we validate the film's excellent corrosion resistance in harsh natural environments and its provision of reliable color indication. The film possesses portability and ease of attachment to various electrical devices and circuits requiring temperature sensing. By connecting the color information of the film to a computer system, we can achieve digital temperature monitoring, providing potential applications in future smart grid development. [ABSTRACT FROM AUTHOR]

Metal based electroreflective devices (MERDs) have gained wide attention in recent years in the fields of smart windows and automotive glass due to their excellent photo-thermal modulation capability in the visible and near-infrared wavelength bands due to their high reflectivity. However, the high porosity due to the island-like growth of silver particles makes the devices susceptible to electrodeposited film breakage during prolonged energization or bleaching. This will seriously affect the effectiveness and stability of the device. In this paper, it is shown that the problem of Ag electrodeposition film fragmentation can be effectively overcome by replacing dimethyl sulfoxide solvent by N-methylpyrrolidone (NMP). The optical constants during film deposition were tested by the spectroscopic ellipsometry technique, and the porosity was calculated (reduced by 21.6%). The results show that MERDs with NMP as the solvent have more and more dispersed silver deposition sites, which leads to lower porosity of the silver deposited film during the deposition process. In addition, the lower turn-on voltage, greater contrast, longer memory effect, and greater coloring efficiency reveal that they are more promising for energy-saving applications. [ABSTRACT FROM AUTHOR]