Search Results (1,450)

With the rapid advances of the electronics industry, a large amount of acidic etching waste solutions (AEWS) for etching Printed Circuit Board (PCB) are generated, which require complete remediation and sustainable recycling to avoid environmental pollution and wasting of resources. Herein, the novel purification technology for the acidic copper-containing etching waste solution was exploited via integrated alkali gradient pH control (3.0, 3.2, and 3.5). At pH 3.0, the system demonstrated selective metal removal with 94.02% efficiency for Fe and 82.60% for Mn. Elevating the pH to 3.2 enabled effective elimination of Zn (59.32%), Cr (59.46%), and Al (33.24%), while maintaining minimal copper loss (8.16%). Further pH adjustment to 3.5 achieved enhanced removal efficiencies of 97.86% (Fe), 91.30% (Mn), 59.38% (Zn), 62.10% (Cr), 21.66% (Ca), 34.05% (Al), and 26.66% (Co), with copper retention remaining high at 70.83% (29.17% loss). Furthermore, using the purified AEWS (pH 3.2) as precursor, high-purity nano-CuO was successfully synthesized through a Cu₂(OH)₃Cl conversion-calcination process, exhibiting 99.20% CuO purity with 0.0012% chlorine content and <0.1% metallic impurities. The development and application of the purification technology for AEWS containing copper, along with the production methodology for high-purity CuO, were significant to the fields of electronic information industry, environmental engineering, green industry and sustainable development of the ecological environment. Full article

In recent years, machine learning (ML) techniques have gained significant attention in time series classification tasks, particularly in industrial applications where early detection of abnormal conditions is crucial. This study proposes an intelligent monitoring framework based on a multimodal convolutional neural network (CNN) to classify normal and abnormal copper ion (Cu²⁺) concentration states in the etching process in the printed circuit board (PCB) industry. Maintaining precise control Cu²⁺ concentration is critical in ensuring the quality and reliability of the etching processes. A sliding window approach is employed to segment the data into fixed-length intervals, enabling localized temporal feature extraction. The model fuses two input modalities—raw one-dimensional (1D) time series data and two-dimensional (2D) recurrence plots—allowing it to capture both temporal dynamics and spatial recurrence patterns. Comparative experiments with traditional machine learning classifiers and single-modality CNNs demonstrate that the proposed multimodal CNN significantly outperforms baseline models in terms of accuracy, precision, recall, F1-score, and G-measure. The results highlight the potential of multimodal deep learning in enhancing process monitoring and early fault detection in chemical-based manufacturing. This work contributes to the development of intelligent, adaptive quality control systems in the PCB industry. Full article

Taranto is the main harbor in Southern Italy and one of the most industrialized cities in the country, largely due to the presence of a large industrial area that includes a major oil refinery, a cement plant, and the former ILVA steel factory, which is one of the largest steel plants in Europe. A human biomonitoring study was conducted on breast milk from two groups of women residing in areas with different levels of exposure to polychlorodibenzo-p-dioxins (PCDDs), polychlorodibenzofurans (PCDFs), and polychlorobiphenyls (PCBs). The study aimed to assess the differences in exposure between the two groups of general people, with one group classified as “exposed” and the other as “non-exposed”. Between 2015 and 2018, 150 breast milk samples were collected: 76 from the exposed group and 74 from the non-exposed group. A specific questionnaire was also administered to the donors. The data were analyzed using a robust regression approach. The results showed significant differences in the concentrations of all analyte classes between the two groups. The difference in concentration from the non-exposed to the exposed group was highly significant (TOT_TE, 5.70 vs. 7.35 pgWHO-TE/g, PCDD + PCDF 3.34 vs. 4.53 pgWHO-TE/g, DL-PCB 2.35 vs. 2.80 pgWHO-TE/g; p << 0.05), with the most notable difference observed for the Σ₁₀ (PCDFs) family (~37%). Additionally, two distinct theoretical exposure profiles were identified: one for women residing in urban peripheries and another for those living in city/town centers. Women in the peripheries were characterized by a profile of four to six chlorinated dioxin/furan congeners plus two PCB congeners, while women in the city centers exhibited a profile of six to eight chlorinated PCDD and PCDF congeners plus five to six chlorinated PCBs. Among women residing in urban peripheries, those living in the peripheries of Statte and Taranto showed the highest exposure levels. All the results appear to witness the highest exposure of the exposed women deriving from the steel plant of concern. In addition, the highest exposure levels for the analytical sum of Σ₆ (NDL-PCBs) were found in women from a municipality classified as non-exposed: Ginosa (periphery). Full article

Duct fan motors must provide high torque within limited space to maintain airflow while requiring low vibration characteristics to minimize fluid resistance caused by fan oscillation. Axial Flux Permanent Magnet Motor (AFPM) offers higher torque performance than Radial Flux Permanent Magnet Motor (RFPM) due to their large radial and short axial dimensions. In particular, the coreless AFPM structure enables superior low-vibration performance. Conventional AFPM typically employs a core-type stator, which presents manufacturing difficulties. In core-type AFPM, applying a multi-stator configuration linearly increases winding takt time in proportion to the number of stators. Conversely, a Printed Circuit Board (PCB) stator AFPM significantly reduces stator production time, making it favorable for implementing multi-stator topologies. The use of multi-stator structures enables various topological configurations depending on (1) stator placement, (2) magnetization pattern of permanent magnets, and (3) rotor arrangement—each offering specific advantages. This study evaluates and analyzes the performance of different topologies based on efficient arrangements of magnets and stators, aiming to identify the optimal structure for duct fan applications. The validity of the proposed approach and design was verified through three-dimensional finite element analysis (FEA). Full article

This study proposes a multi-via structure as a loss-reduction design technique to mitigate current crowding in a slotless axial flux permanent magnet motor (AFPM) equipped with printed circuit board (PCB) stators. The PCB stator enables high current density operation through parallel copper-foil stacking and supports an ultra-compact structural configuration. However, current concentration in the via regions can increase copper loss and phase resistance. In this work, the via position and diameter were defined as design variables to perform a sensitivity analysis of current distribution and phase resistance variation. The effects of current density dispersion and the potential for copper loss reduction were evaluated using three-dimensional finite-element analysis (FEA). The results confirm that adopting a multi-via structure improves current path uniformity and reduces electrical losses, thereby enhancing overall efficiency. Furthermore, the analysis shows that excessive via enlargement or overuse does not necessarily yield optimal results and, in certain cases, may lead to localized current peaks. These findings demonstrate that the multi-via structure is an effective and appropriate design strategy for PCB stators and highlight the importance of optimized via placement tailored to each stator configuration. Full article

Dromedary camels raised under semi-extensive management can act as One Health sentinels for environmental exposures and food chain surveillance, yet serum reference information remains scarce. Our objective was to provide the most comprehensive assessment to date of physiological and toxicological serum profiles in dromedary camels (Camelus dromedarius) from the Canary Islands. We included 114 clinically healthy animals of different sex, age, and reproductive status. Serum samples were analyzed for essential, toxic, and potentially toxic elements using inductively coupled plasma mass spectrometry (ICP-MS). In addition, a high-throughput multi-residue method based on QuEChERS extraction followed by UHPLC-MS/MS and GC-MS/MS was used to screen for 360 organic compounds, including pesticides, veterinary drugs, human pharmaceuticals, and persistent organic pollutants. Essential elements showed biologically consistent variations according to sex, age group, and pregnancy status. Males had higher levels of selenium and copper, while calves showed elevated concentrations of manganese and zinc. Pregnant females exhibited lower iron, zinc, and selenium levels, consistent with increased fetal demand. These results provide preliminary reference values for healthy camels, stratified by physiological status. In contrast, classical toxic elements such as arsenic, mercury, lead, and cadmium were found at very low or undetectable concentrations. Several potentially toxic elements, including barium, strontium, and rare earth elements, were detected sporadically but without toxicological concern. Only 13 organic compounds (3.6%) were detected in any sample, and concentrations were consistently low. The most prevalent was the PAH acenaphthene (55.3%), followed by the fungicide procymidone and the PAH fluorene. Notably, no residues of the usually detected 4,4′-DDE or PCB congeners were found in any sample. These findings confirm the low environmental and dietary exposure of camels under low-intensity farming systems and highlight their value as sentinel species for food safety and environmental monitoring. Full article

Spaceborne electronic equipment printed circuit boards (PCBs) installed under vertical mounting boundary conditions are exposed to a tougher environment than horizontal mounting due to the boundary conditions under the launch environment. Steinberg’s theory, which was previously applied to evaluate the structural safety of spaceborne electronic equipment, generates large errors and leads to inaccurate results. Although the vertical mounting of spaceborne electronic equipment is increasing due to its thermal/structural advantages, no high-reliability methodology or research has been developed to secure structural safety in the launch environment. In this study, the Oh-Park methodology, a highly reliable technique for evaluating the structural safety of solder joints in space-use electrical components based on the strain of the PCB, was verified for vertical spaceborne electronic equipment. Consequently, fatigue life tests were performed on specimens with structures similar to those of the actual spaceborne electronic equipment. The results were compared and analyzed with predictions made using Steinberg’s theory and Oh-Park methodology, respectively. The structural safety assessment using the Oh-Park methodology represented the test results more accurately, indicating that the critical strain-based methodology is effective for the design of vertical spaceborne electronic equipment exhibiting more complex behaviors due to their structural characteristics. Full article

To address the challenge of detecting small defects in Printed Circuit Boards (PCBs), a YOLO-HDEW model based on the enhanced YOLOv8 architecture is proposed. A high-resolution detection layer is introduced at the P2 feature level to improve sensitivity to small targets. Depthwise Separable Convolution (DSConv) is used for downsampling, reducing parameter complexity. An Edge-enhanced Multi-scale Parallel Attention mechanism (EMP-Attention) is integrated to capture multi-scale and edge features. The EMP mechanism is incorporated into the C2f module to form the C2f-EMP module, and dynamic non-monotonic Wise-IoU (W-IoU) loss is employed to enhance bounding box regression. The model is evaluated on the PKU-Market-PCB, DeepPCB, and NEU-DET datasets, with experimental results showing that YOLO-HDEW achieves 98.1% accuracy, 91.6% recall, 90.3% mAP@0.5, and 61.7% mAP@0.5:0.95, surpassing YOLOv8 by 1.5%, 2.3%, 1.2%, and 1.9%, respectively. Additionally, the model demonstrates strong generalization performance on the DeePCB and NEU-DET datasets. These results indicate that YOLO-HDEW significantly improves detection accuracy while maintaining a manageable model size, offering an effective solution for PCB defect detection. Full article

The increasing use of reclaimed wastewater in agriculture raises growing concerns about the accumulation of priority organic micropollutants in edible crops. In this study, we developed and validated a novel QuEChERS–SPME–GC/MS method for the simultaneous determination of 15 polycyclic aromatic hydrocarbons (PAHs), 3 nitro-PAHs, and 14 polychlorinated biphenyls congeners in Eruca vesicaria (rocket) leaves. The method was optimized to address the matrix complexity of leafy vegetables and included a two-step dispersive solid-phase extraction (d-SPE) cleanup and aqueous dilution prior to SPME. Validation showed excellent performance, with MDLs between 0.1 and 6.7 µg/kg, recoveries generally between 70 and 120%, and precision (RSD%) below 20%. The greenness of the protocol was assessed using the AGREE metric, yielding a score of 0.60. Application to rocket samples irrigated with treated wastewater revealed no significant accumulation of target pollutants compared to commercial samples. All PCB and N-PAH congeners were below detection limits, and PAH concentrations were low and mostly limited to lighter compounds. Human health risk assessment based on toxic equivalent concentrations confirmed that estimated cancer risk (CR) values 10⁻⁹–10⁻⁸ were well below accepted safety thresholds. These findings support the safe use of reclaimed water for leafy crop irrigation under proper treatment conditions and highlight the suitability of the method for trace-level food safety monitoring. Full article

This article introduces a substrate-integrated, low-cost, and low-profile E-band high-gain Fabry–Perot (FP)/leaky-wave (LW) antenna. This design enables the full integration of a high-gain antenna within a single-layer substrate for millimeter-wave (mm-wave) applications. The antenna design layout comprises a partially reflective surface (PRS) mounted on a thin, metal-coated, low-cost I-Tera MT40 dielectric substrate. The proposed antenna differs from conventional air-cavity-based FP/LW antennas, as it is fabricated on a low-cost dielectric substrate, eliminating the need for an air cavity, which restricts integration with printed circuit boards (PCBs) and planar circuits. The antenna is excited using a rectangular WR12 waveguide located beneath the ground plane. Impedance matching is achieved by employing a rectangular iris. The formulation for analyzing leaky waves within a cavity is thoroughly discussed using the Transverse Resonance Method (TRM). The proposed FP antenna achieves a maximum realized gain of 14.6 dBi with good impedance matching ($|S_{11}|$ = –14 dB). Finally, the proposed antenna is fabricated, and its performance is validated through experimental measurements. Full article

Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics^® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article

Detection of defects on steel surface is crucial for industrial quality control. To address the issues of structural complexity, high parameter volume, and poor real-time performance in current detection models, this study proposes a lightweight model based on an improved YOLOv11. The model first reconstructs the backbone network by introducing a Reversible Connected Multi-Column Network (RevCol) to effectively preserve multi-level feature information. Second, the lightweight FasterNet is embedded into the C3k2 module, utilizing Partial Convolution (PConv) to reduce computational overhead. Additionally, a Group Convolution-driven EfficientDetect head is designed to maintain high-performance feature extraction while minimizing consumption of computational resources. Finally, a novel WISEPIoU loss function is developed by integrating WISE-IoU and POWERFUL-IoU to accelerate the model convergence and optimize the accuracy of bounding box regression. The experiments on the NEU-DET dataset demonstrate that the improved model achieves a parameter reduction of 39.1% from the baseline and computational complexity of 49.2% reduction in comparison with the baseline, with an mAP@0.5 of 0.758 and real-time performance of 91 FPS. On the DeepPCB dataset, the model exhibits reduction of parameters and computations by 39.1% and 49.2%, respectively, with mAP@0.5 = 0.985 and real-time performance of 64 FPS. The study validates that the proposed lightweight framework effectively balances accuracy and efficiency, and proves to be a practical solution for real-time defect detection in resource-constrained environments. Full article

This study investigates the concentrations and patterns of dioxins and dioxin-like PCBs (TEQ Diox+PCB-DL) and non-dioxin-like PCBs (PCB-NDL) in eels from Lake Garda, assessing their relationship with biometric and lipid parameters. TEQ Diox+PCB-DL levels ranged from 1.70 to 77.1 pg/g (median: 9.90 pg/g), while PCB-NDL levels spanned from 14.0 to 1620 ng/g (median: 65.5 ng/g). Significant, albeit low, correlations were found: length and weight were negatively correlated, and lipid content was positively correlated, with both contaminants. Multivariable regression confirmed length and lipid percentage as significant predictors, although the models explained a limited proportion of variance (R²: 0.23 and 0.17). Classification-based analyses showed that irregularly contaminated eels were shorter and had a higher lipid content. Multinomial logistic regression supported these findings, but showed limited predictive accuracy (AUC = 0.63). Notably, 28 out of 90 samples exceeded the EU regulatory limit for TEQ Diox+PCB-DL, and several surpassed the threshold for PCB-NDL, highlighting potential public health risks. Given the lipophilic nature and toxicity of these compounds, continued monitoring is warranted. The findings underscore the need for broader environmental assessments to better understand pollutant dynamics and support regulatory actions, including the extended ban on eel fishing in the region. Full article

Discrete Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC MOSFETs) are characterized by their lower parasitic parameters and single-chip design, enabling them to achieve even faster switching speeds. However, the rapid rate of change in voltage (dv/dt) and current (di/dt) can lead to overshoot and oscillation in both voltage and current, ultimately limiting the performance of high-frequency operations. To address this issue, this paper presents a high-switching-frequency motor controller that utilizes discrete SiC MOSFETs. To achieve a high switching frequency for the controller while minimizing current oscillation and voltage overshoot, a novel electronic system architecture is proposed. Additionally, a passive driving circuit is designed to suppress gate oscillation without the need for additional control circuits. A new printed circuit board (PCB) laminate stack featuring low parasitic inductance, high current conduction capacity, and efficient heat dissipation is also developed using advanced wiring technology and a specialized heat dissipation structure. Compared to traditional methods, the proposed circuit and bus design features a simpler structure, a higher power density, and achieves a 13% reduction in current overshoot, along with a 15.7% decrease in switching loss. The silicon carbide (SiC) controller developed from this research has successfully undergone double-pulse and power testing. The results indicate that the designed controller can operate reliably over extended periods at a switching frequency of 50 kHz, achieving a maximum efficiency of 98.2% and a power density of 9 kW/kg (10 kW/L). The switching frequency and quality density achieved by the controller have not been observed in previous studies. This controller is suitable for use in the development of new energy electrical systems. Full article

This study investigates the presence of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in 165 yogurt samples collected from farms across 11 Lebanese regions. As the first nationwide assessment of these contaminants in yogurt, it addresses a critical gap in Lebanon’s food safety monitoring. Levels of PCBs, ΣDDTs, ΣHCH, HCB, endosulfan, methoxychlor, and dieldrin were quantified, revealing widespread contamination. A total of 40.6% of samples recorded total PCB concentrations exceeding European maximum residue limits (MRLs), with a national mean of 39.26 ng/g fat. Keserwan, and North and South Lebanon showed the highest PCB contamination, likely linked to thermal power generation activities. For OCPs, mean concentrations of all tested compounds exceeded their respective MRLs across all regions. Levels of ΣDDTs surpassed the MRL in 100% of samples with the highest mean concentration at 376.79 ng/g fat, followed by endosulfan (70.32 ng/g fat) and β-HCH (65.32 ng/g fat). Elevated OCP levels were especially noted in Bekaa, Baalbek-Hermel, and South and North Lebanon, likely reflecting intensive agricultural practices and the ongoing use of contaminants. Estimated daily intakes (EDIs) indicated potential dietary exposure risks, particularly from PCBs, ΣDDTs, and ΣHCH. These findings underscore the urgent need for regulatory oversight and national food safety monitoring to ensure public health protection. Full article