In Case You Missed It
Circularity
“AI-Enhanced Sorting Enabling Direct, High-Purity Urban Mining of Tantalum: A Novel Pathway from E-Waste to Critical Materials”
Authors: D. Xia, et al.
Abstract: Tantalum’s supply chain instability demands efficient urban mining from e-waste. Here, the authors present an AI-enhanced process that combines intelligent sorting with sustainable hydrometallurgy for high-yield/high-purity Ta recovery. A hybrid sorting system, cascading an interpretable convolutional neural network (CNN) with automated multi-energy x-ray transmission (MEXRT) spectroscopy, achieved 99.6% precision and 96.9% recall at 3000 components/hour, resolving the Ta/Nb ambiguity. Spatial activation mapping illustrated the visual sorting mechanism, facilitating feature-driven upgrading. Meanwhile, Canny edge detection and K-edge detection enabled real-time and pixel-wise spectral analysis under multithreaded processing. Downstream, streamlined physical separation and thermodynamically guided reverse leaching selectively recovered Ta with 98.2% efficiency under mild conditions. Advanced characterization using transmission electron microscopy and ion beam analysis revealed a quantifiable core-shell Ta/Ta2O5 structure in leached products, guiding calcination into >99.8% pure Ta2O5. This work establishes a closed-loop urban mining framework, demonstrating how AI and tailored refining enable a circular economy for critical metals. (Resources, Conservation & Recycling 227 (2026) 108717. https://doi.org/10.1016/j.resconrec.2025.108717)
Inspection
“PCB Defect Detection Using Machine Learning: YOLOv5 and Inception v3 CNN-Based Approach”
Authors: Tanu Kumari, Theertha Raj, Pasuluru Sanath Darahas, N. Neelima and V. Bhavana
Abstract: Identification of PCB board defects early in the manufacturing process is crucial, as PCB quality control plays an important role in the electronics manufacturing industry. Defective PCBs can lead to product failures, increased costs, and reliability issues. Proposed is a machine learning program for detecting and classifying defects in PCBs using a YOLOv5 object detection algorithm and an Inception v3 CNN-based approach. The dataset consists of annotated PCB images for the YOLOv5 algorithm, and the images are categorized into six defect types: missing holes, mouse bite, open circuit, short circuit, spurs, and spurious copper. The preprocessed defect images are analyzed using the YOLOv5 model for defect location, while the Inception v3 CNN model performs precise defect classification. The results and the output clearly show that the proposed approach achieves high accuracy in both detection and classification tasks. Hence, the aim is to provide an efficient and automated solution for PCB inspection, outperforming conventional manual checks. (2025 3rd International Conference on Smart Systems for Applications in Electrical Sciences, https://doi.org/10.1109/icsses64899.2025.11009644, June 2025)
“EM-YOLO: High-Precision Electronic Component Detection via Multi-Scale Attention and Dynamic Feature Fusion”
Authors: Zeyi Xu, Jiahui Han, Hongying Qin, Kangsong Gao, Kai Xie and Jianbiao He
Abstract: To address the frequent missed detections and suboptimal accuracy in electronic component circuit board inspection, a target detection algorithm for electronic components, EM-YOLO, is designed with YOLOv11 as the core framework. The overall design employs a three-level optimization strategy: First, in the backbone network part, an efficient feature extraction module C3k2_EMA is designed. Incorporating the Efficient Multi-scale Attention (EMA) mechanism enhances the network’s feature representation capability for tiny components. Second, in the neck network part, the BiSPD-FPN structure is proposed, which adds a high-resolution feature map at the P2 level. By combining the bidirectional feature pyramid network (BiFPN) with spatial pyramid depthwise convolution (SPDConv), it optimizes the multi-scale feature fusion capability and reduces detail loss during the downsampling process. Finally, in the detection head part, the Focal-DIoU loss function is introduced to optimize the bounding box regression process and improve the localization accuracy of densely arranged components. Experimental results show that, on the self-made dataset and the public dataset PCB Electronic Components Dataset, the detection accuracy (mAP@0.5) of the improved algorithm has increased by 0.5% and 3.9%, respectively, compared with the benchmark algorithm, while the false negative rate (FNR) has decreased by 1.3% and 4%, respectively, both outperforming mainstream algorithms. Therefore, the algorithm in this paper exhibits good detection accuracy, and the problem of missed detections occurring in the detection process has also been effectively alleviated. It possesses good generalization ability and provides an effective detection scheme for component detection. (Scientific Reports, vol. 15, no. 44531, Dec. 24, 2025; https://www.nature.com/articles/s41598-025-28116-0)
RF/Microwave Design
“Quad-Band Metamaterial Absorber with High Shielding Effectiveness Using Bold X-Shaped Ring Resonator”
Authors: Altaf Hussain, et al.
Abstract: A novel X-shaped modified split-ring resonator (MSRR) broadband microwave metamaterial absorber for covert applications in the C, X and Ku bands is presented. The absorber features a 0.035mm-thick annealed copper layer with X-shaped resonators on a 1.6mm-thick FR4 dielectric substrate, with a unit cell of 0.254λ × 0.254λ at 7.64GHz. CST Microwave Studio simulations show absorption peaks at 7.64GHz (98.4%), 8.41GHz (97%), 11.4GHz (99.2%), and 12.66GHz (99%). Parametric analyses are employed to optimize these frequencies using E-field, H-field and surface current distributions. The design achieves high absorption for transverse electric (TE) and transverse magnetic (TM) polarization across incidence angles up to 30°, ideal for electromagnetic interference (EMI) shielding and stealth in military contexts. (Journal of Electronic Materials, November 2025; https://link.springer.com/article/10.1007/s11664-025-12512-3)