Search Results (713)

Gas bubble accumulation and transport play a critical role in the electrochemical performance and reaction uniformity of proton exchange membrane water electrolysis (PEMWE), particularly at high current density. However, the quantitative coupling between bubble coverage and local electrochemical activity remains insufficiently clarified. In this work, a visualization PEMWE combined with a printed circuit board (PCB)-based segmented measurement technique was developed to simultaneously characterize the spatial distributions of bubble coverage and local current density (LCD) under different current densities and operating temperatures. The results showed that both bubble coverage and LCD exhibited pronounced in-plane non-uniformity. The LCD generally displayed lower values in the central region and higher values near the edges, whereas high bubble coverage regions were mainly concentrated in the central and outlet-side areas. As the average current density increased from 0.5 A/cm² to 2.0 A/cm², the LCD range expanded from 0.43 to 0.53 A/cm² to 1.75–2.20 A/cm², while the local bubble coverage increased from 0.24 to 0.34 to 0.86–0.91. A clear negative spatial correlation between bubble coverage and LCD was identified, and this correlation became stronger with increasing current density. Moreover, the correlation exhibited marked spatial dependence, following the E5 > C3 > E1 > A5 > A1 order. Increasing the operating temperature from 50 to 70 °C alleviated the local heterogeneity, but it did not alter the fundamental coupling trend. These results demonstrate that bubble accumulation is a key factor governing current redistribution and local reaction non-uniformity in PEMWE, and they provide guidance for flow field optimization and high current density operation. Full article

This research demonstrates the principle and optimization methodology to create economic and miniaturized high-resolution micro-moisture sensors. The interdigitated fringe electric field-based moisture measurement principle is firstly investigated to sketch the key parameters of printed circuit board (PCB)-based sensors for further performance optimization. Then, a comprehensive study is conducted to analyze parameter variations with conclusions of suggested design rules to achieve higher measurement sensitivity. Two prototypes are designed and manufactured to validate the proposed theoretical contributions. Water droplets are employed to control the ambient relative humidity, which is adopted as the actual moisture variable in this work. A double-correlated sampling circuit is used for capacitance sensing. Both of them demonstrate a linearity of 1% and sensitivity of 0.1 pF/mg levels, but prototype 2 gains a better batch consistency, which is beneficial for commercialization. Further data analysis suggests that the equivalent input–output sensitivity reaches a level of 1.2403 pF/%RH (relative humidity), which is significantly higher than other types of published interdigitated fringe electric field-type moisture sensors. The optimized prototypes also show advantages of miniaturized size, low cost and high consistency, which can potentially impact the industry applications. Full article

A significant portion of plastic products is not accepted by curbside recycling companies and goes to landfills or incineration, causing an adverse impact on the environment. This study investigated the effects of utilizing post-consumer plastic and e-waste in concrete. A plastic product made of thermoplastic polypropylene (PP) was ground into fine particles and used for 10% volumetric replacement of sand, while bare printed circuit boards (PCBs) were pulverized into powder and used for 10% cement replacement by mass. This study introduces a unique utilization of grounded powder PCBs by partially replacing cement in concrete. Furthermore, reinforced concrete beams with the replacements were constructed and tested under flexure for structural behavior evaluation. The results of this study show an average of 11% reduction in both the compressive strength of concrete and the maximum load capacity of the beams incorporating plastic products. A life cycle assessment study was conducted using a functional unit of 1.0 cubic yard concrete production. The system boundary for the environmental assessment of the concrete in this study includes only the production phase, which is from the cradle to the end gate of the ready-mix concrete plant. The environmental impact estimation of a 10% reduction in constituents of concrete showed a 10% reduction in most LCA measures where cement was replaced compared to a 1% effect for the fine aggregate replacement. Full article

This study presents a low-cost, small-scale single-chamber microbial fuel cell (MFC) toxicity biosensor fabricated on a printed circuit board (PCB) and a 3D-printed chamber with a volume of 120 μL. The anode consists of a screen-printed carbon electrode on the PCB, while the air cathode is a carbon paper electrode. To address poor adhesion of microorganisms to the smooth anode surface, femtosecond laser processing was used to fabricate a micropore array with 40 μm pores on the electrode. This method can create micropores on the anode surface without damaging the screen-printed electrodes, the PCB substrate, or the pads. These micropores increase the anode’s surface area and hydrophilicity, allowing more microbial coatings to firmly adhere to its surface. In this study, the MFC utilised Rhizobium rosettiformans W3, extracted from activated sludge at a wastewater treatment plant, as the anode microorganism. Its aerobic nature simplifies the design of MFCs, enabling a single-chamber structure and miniaturisation. Using formaldehyde solution as a toxicity sample to test the biosensor’s performance, a 0.1% concentration significantly reduced the sensor’s output power. Full article

The aim of this study was to investigate the potential for co-bioleaching of ground printed circuit boards (PCBs) and flotation tailings using a single-stage biohydrometallurgical process. The ground PCB sample was a finely divided waste product from industrial shredding, which was collected using an air filtration system. The flotation tailings sample was mainly composed of pyrite (49%), quartz (29%), gypsum (8%), feldspar (8%), and chlorite (6%). The experiment was carried out in laboratory-scale reactors at 35 °C with constant aeration and a flotation tailings pulp density of 5% (solid-to-liquid ratio). In a control reactor, only flotation tailings were leached. In an experimental reactor, both flotation tailings and ground PCBs were leached simultaneously. The experiment was conducted in two stages. In the first stage, the experiment was carried out in a batch mode. The second stage involved two reactors operating continuously in cascade. During the experiment, we monitored the dynamics of several key parameters as a function of PCB concentration, including pH, redox potential, the concentrations of Fe³⁺ and Fe²⁺ ions, and the number of microbial cells. The 16S rRNA gene analysis revealed that the presence of PCBs had a significant effect on the composition of the microbial community. The concentration of PCB was gradually increased in order to examine the limits of the process and optimize potential economic benefits. The increase was done in 3 stages: 5 g/L in the first stage, from 5 to 12 g/L in the second stage, and up to 35.5 g/L in the third stage. However, this increase had a negative effect on the pyrite oxidation rate and the effectiveness of PCB bioleaching in continuous mode. The bioleaching efficiency of copper from printed circuit boards (PCBs) was above 70% in batch mode and above 80% in continuous mode at PCB concentrations up to 12 g per liter. Copper recovery decreased to around 53.1–61.6% as the PCB concentration continued to increase. The nickel leaching efficiency in batch mode was 46.3 ± 4.8%. In continuous mode, the nickel recovery decreased as the PCB concentration increased, reaching 48.53% in the first stage, then declining to 37.62% in the second stage and finally dropping to 27.06% in the third stage, depending on the higher concentration of PCB. Full article

Ambient backscatter systems enable passive sensing and information transfer by utilizing the reflection and modulation of incident radio-frequency (RF) signals. However, in real-world scenarios involving non-cooperative targets such as off-the-shelf printed circuit boards (PCBs), the backscattered signal is extremely weak and often obscured by strong direct-path self-interference (SI) at the receiver. This issue becomes even more severe when unintentional PCB structures act as radiating elements. In this work, we explore ambient backscatter leakage from a compromised PCB using a realistic measurement setup that includes separated transmit and receive antennas and a direct-conversion Universal Software Radio Peripheral (USRP)-based receiver. We demonstrate that residual carrier frequency offset (CFO), caused by oscillator mismatch and hardware imperfections, can spread the dominant SI in the baseband and completely mask the weak backscattered signal. To solve this problem, a software-based post-processing framework is applied. This method leverages the complex baseband representation enabled by the homodyne receiver to jointly manage the carrier and SI components without relying on intermediate-frequency processing or prior knowledge of the target signal parameters. Experimental results show that this approach significantly improves the detectability of weak backscattered baseband information that would otherwise be concealed within the raw I/Q data. This study emphasizes the importance of CFO-aware digital processing in ambient backscatter systems and offers new insights into unintended electromagnetic leakage mechanisms from commercial PCB platforms. Full article

This study developed predictive mathematical models for UV laser penetration depth in FR-4-based rigid–flex printed circuit boards, addressing the critical need for precise material removal in applications like protective plug removal. Utilizing a comprehensive Design of Experiments framework, specifically two-level full factorial designs, the influence of key operational parameters—number of loops, scanning speed, and focal position offset—on material removal was systematically investigated in both laminate and multilayer substrates. Empirical models were established for both substrate types, identifying significant factors and interactions that govern penetration depth with physical justification. Comparative analysis revealed that the multilayer model consistently predicted deeper penetration (6–17 µm) than the laminate model under identical conditions, primarily due to reduced heat-associated phenomena with prepreg, yet the laminate model offered a reasonable approximation for complex stack-ups. Rigorous validation through confirmation experiments, achieving 100% success in electrical integrity tests with compliant plug removal, unequivocally demonstrated the models’ robustness and reliability. This research provided a crucial tool for optimizing UV laser micromachining processes, significantly reducing parameter identification times and minimizing scrap generation, thereby enhancing the efficiency and reliability of advanced rigid–flex PCB manufacturing. Full article

In the field of printed circuit board (PCB) manufacturing, surface defect detection serves as a critical process in the production line, directly impacting the quality and safety of subsequent electronic products. However, accurately detecting tiny surface defects in real time remains a significant challenge given the complex layouts of PCBs. To address this issue, this study proposes a novel Ghost-BiFPN-Efficient-YOLOv8 (GBE-YOLOv8) model architecture for PCB defect detection based on an improved YOLOv8n. The backbone network of the model employs lightweight Ghost Conv to partially replace regular convolutions, thereby reducing computational complexity and parameter count. The neck network incorporates a multi-stage feature fusion module named G-C2f and a dynamic weighting module named BiFPN-Concat to enhance the model’s ability to characterize PCB defects. The model’s head network employs an Efficient Head that combines mixed depthwise convolution and partial convolution, further optimizing detection accuracy and computational efficiency. Simultaneously, a comprehensive evaluation of model performance was conducted using publicly available datasets. And the working mechanisms of each improved method were analyzed through class activation heatmaps to further enhance the interpretability of the model. Experimental results demonstrate that compared to the baseline model and several other state-of-the-art object detection algorithms, the proposed method exhibits significant advantages across various evaluation metrics, and its mAP@0.5, mAP@0.5:0.95, parameters, GFLOPs and FPS achieve 98.9%, 61.4%, 2.6 M, 7.5 and 252, respectively. Furthermore, each optimization method achieves the expected design purpose, and the combined application of all optimization methods enables the model to strike an optimal balance between detection accuracy and computational complexity. Consequently, this research can provide a reliable technical solution for high-precision real-time detection of surface defects on PCBs in industrial production lines. Full article

This paper introduces a cost-effective customised Printed Circuit Board (PCB) designed to establish an accurate Internet of Things (IoT) platform integrated with established Digital Twin (DT) models for advanced structural monitoring. The study focuses on developing a low-cost, precise PCB to synchronise real-time data between physical structures and their DT counterparts. The methodology includes a robust communication architecture utilising MQTT protocols, facilitating reliable data transmission and efficient integration with MATLAB for processing. Validation tests demonstrate high accuracy in data capture, with less than 1% deviation from conventional systems across multiple structural damage scenarios. This research highlights the potential of cost-effective PCB solutions for enhancing SHM and developing more resilient, proactive infrastructure management strategies. Full article

The optimization of the MOSFET gate drive parameters is crucial for the trade-off between switching loss and electromagnetic interference (EMI). However, the nonlinear coupling among gate drive parameters, board-level parasitic, and switching performance limits the effectiveness of traditional MOSFET drive design methods. This paper proposes an adaptive tuning framework based on the proximal policy optimization (PPO) algorithm. An analytical switching model incorporating board-level parasitics is first derived to analyze the coupling between drive parameters and switching performance. The optimization problem is then formulated as a Markov decision process (MDP). Within this framework, domain randomization is applied during training. This enables the agent to learn a generalizable optimization strategy that remains robust across the varying parasitic inductances encountered in different PCB layouts. Compared to the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II), the proposed method uses the trained policy for direct inference. This reduces computation time by $98.7\%$ while maintaining a multi-objective performance difference within $10.06\%$. In addition, hardware verification shows a 10.7% average deviation between the measured and simulated results. These results demonstrate that the proposed method provides an efficient and scalable solution for MOSFET gate drive optimization. Full article

In electronic manufacturing, efficient detection of printed circuit board (PCB) surface defects is essential for reducing rework rates and minimizing material waste, thereby supporting sustainable manufacturing. To address the challenge that existing methods struggle to balance detection accuracy and real-time performance in complex industrial environments, this paper proposes a lightweight and high-performance PCB surface defect detection model, termed SDD-RT-DETR. Built upon Real-Time Detection Transformer (RT-DETR), the proposed model introduces a Faster-Block backbone to improve feature extraction efficiency, replaces the original feature fusion module with HS-FPN to enhance multi-scale representation, and employs the Wise-Focaler-MPDIoU loss to optimize bounding box regression. Experiments conducted on an expanded PCB defect dataset containing 3403 images show that SDD-RT-DETR achieves improvements of 2.3% in mAP and 3.6% in inference speed over the baseline, while reducing parameters by 5.04 M and FLOPs by 12.7 G. These results demonstrate that the proposed method effectively balances accuracy, efficiency, and computational cost, offering a practical solution for low-energy and sustainable intelligent electronic manufacturing systems. Full article

Automated optical inspection (AOI) for printed circuit boards (PCBs) requires localizing small, sparse defects under illumination drift and minor placement misalignment, while supporting fast, auditable pass/fail decisions. This paper presents a training-free, reference-based digital image processing framework with no learning/training stage that compares each defective query image with a small library of defect-free reference templates (for the same PCB layout/revision) using a small set of interpretable control parameters. A reference is selected by coarse-to-fine matching (fast pre-screening followed by SSIM refinement on a central region), and an optional global alignment is applied only when it increases SSIM to limit defect-driven over-correction. Defects are highlighted by a defect-likelihood field that fuses an SSIM-derived structural dissimilarity map with a normalized absolute-difference map, followed by connected-component extraction to produce confidence-ranked bounding boxes. The method achieves Precision = 0.9663, Recall = 0.9987, and F1 = 0.9822 at the best-F1 operating point (0.149 false positives per image). Under the adopted box-matching protocol, average precision reaches 0.984. Precision–recall and FROC curves are reported to support threshold selection under different false-alarm budgets. Full article

Deep learning models have become central to automated Printed Circuit Board (PCB) defect detection. However, recent work has raised concerns about how reliably these models express confidence in their predictions, particularly when deployed in safety-critical inspection systems. This study conducts an empirical investigation of epistemic uncertainty across representative architectures used in PCB inspection: the two-stage Faster R-CNN detector, the one-stage YOLOv8 detector, and their corresponding classification counterparts, ResNet-50 and YOLOv8-Cls. Monte Carlo Dropout (MCD) was applied during inference to compute predictive entropy, mutual information, softmax variance, and bounding-box variability across multiple stochastic forward passes on both multiclass and binary inspection datasets. On the multiclass SolDef_AI dataset, Faster R-CNN achieved substantially stronger detection performance (mAP = 0.7607, F1 = 0.9304) and lower predictive entropy, with more stable localisation. In contrast, YOLOv8 produced markedly weaker performance (mAP = 0.2369, F1 = 0.3130) alongside higher entropy and greater bounding-box variability. On the binary Jiafuwen datasets, the YOLOv8-Cls model achieved higher overall performance (F1 = 0.6493) compared with the ResNet-50 classifier (F1 = 0.4904), reflecting its strength in simpler binary inspection tasks. Across uncertainty metrics, predictive entropy and mutual information were more sensitive to dataset size, showing higher and more variable values in the smaller multiclass dataset, whereas softmax variance and bounding-box variability appeared more architecture-dependent. These findings demonstrate that architectural choice, dataset structure, and task formulation jointly influence both performance and uncertainty behaviour. By integrating conventional metrics with uncertainty estimates, this study provides a transparent benchmark for assessing model confidence in automated optical inspection of PCBs. Full article

Electromagnetic compatibility (EMC) diagnostics for high-density through-silicon via (TSV)-based chips face significant challenges due to complex three-dimensional electromagnetic coupling and inefficient source reconstruction workflows. This paper proposes a universal contribution-driven dipole preprocessing technique tailored for dipole array-based source reconstruction methods, addressing the critical efficiency-accuracy trade-off inherent in traditional approaches. The core innovation is an influence factor-based evaluation-elimination mechanism that extracts effective dipole components aligned with the structural characteristics of TSV-based chips and multilayer printed circuit boards, while eliminating redundant dipoles independently of the downstream source reconstruction algorithm. Validation on a multilayer PCB (1 GHz) and a TSV-based chip (4 GHz) demonstrates that the technique maintains high reconstruction accuracy, with error increase limited to ≤0.2% for the simulated PCB and ≤0.05% for the physically measured TSV-based chip. Computational time is reduced by 28–61% for the PCB and 20–28% for the TSV chip compared to traditional source reconstruction without preprocessing. For TSV-based chips exhibiting complex electromagnetic behavior, the technique delivers consistent performance across different dipole configurations, providing a fast, robust, and universal EMC diagnostic tool for high-density electronic devices. Full article

The printed circuit board (PCB), a core component of electronic products, is playing an increasingly critical role in quality defect detection. Traditional methods suffer from low efficiency and high missed detection rates, rendering them insufficient to meet the industrial requirements for PCB defect detection. To address this issue, this paper proposes an ESI-YOLOv11n model for PCB defect detection that incorporates multi-scale feature fusion. The specific improvements are as follows: First, Spatial and Channel Reconstruction Convolution (ScConv) is incorporated to optimize the C3k2 module, creating a dynamic adaptive feature extraction unit that strengthens its ability to capture features of small defects. Second, an Efficient Multi-Scale Attention (EMA) mechanism is integrated into the Neck layer, dynamically adjusting the weight distribution of multi-scale feature maps to enhance efficiency of feature fusion and improve detection performance. Finally, the Inner concept is integrated with the CIoU loss function, resulting in the novel Inner-CIoU loss function. This loss function optimizes the model by utilizing auxiliary box mechanisms and geometric constraints, leading to more accurate regression results. Experimental results show that the improved model achieves an average precision of 95.9% and a recall rate of 93.3%, which are 9.3% and 11.5% higher than those of the original model, respectively, while having a parameter size of only 13.3 Mb. The model effectively reduces the missed detection rate and false detection rate, significantly enhances the PCB defect detection performance, and demonstrates superior comprehensive performance compared with current mainstream detection models. Full article