Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
The rapid development of the electronics industry creates a need for small-sized materials with good mechanical properties and electronic performance to improve efficiency during production. However, in thin materials deformation is prone to occur during the production process which can damage the mechanical properties of the material. To minimize the deformation that occurs, heat treatment is performed. Heat treatment is carried out at temperatures of 400 °C, 650 °C, and 900 °C and different cooling, namely furnace cooling, air cooling, and water cooling. Observations to evaluate the effect of heat treatment, with heating at various temperatures and different cooling rates on the mechanical properties and electronic performance of 304 stainless steel thin foil. Tensile tests were conducted to determine changes in the mechanical properties of the samples, while the electronic performance to be observed is the conductivity carried out by the steady state method on two hot plates. The results show that the higher the temperature and the faster the cooling rate during heat treatment, the conductivity of the sample increases, the best conductivity is obtained from the water cooling sample heated at 900 °C with a conductivity value of 20.38 W/mK with a time range of 515 seconds. Although conductivity increased after heat treatment, the average value of mechanical properties decreased, from the tensile test conducted, the highest UTS sample was 847.75 MPa in the water cooling sample with 650 °C heating. The rapid development of the electronics industry creates a need for small-sized materials with good electrical conductivity. Surface roughness (Ra) was measured using Aziz equation which was found in 2022....
The growing demand for lightweight, flexible, and efficient materials for electromagnetic interference (EMI) shielding has driven the development of conductive polymer composites (CPCs) as sustainable and reliable alternatives. These materials are essential for protecting communication systems, electronic components, and ensuring the efficient operation of expanding 5G networks. This study presents the development and characterization of PLA/MWCNT and TPU/MWCNT composites, fabricated by melt mixing and compression molding with different loadings of multi- walled carbon nanotubes (MWCNTs). Rheological analyses indicated that the dispersion of MWCNTs begins at around 3%, with a more pronounced effect in PLA. In particular, PLA/ MWCNT composites with 12% filler, where the nanotubes are effectively dispersed, exhibited electrical conductivity on the order of 10−3 S/cm. On the other hand, although TPU/MWCNT composites also showed an increase in conductivity with the addition of MWCNT, they did not reach the same order of magnitude (10−3 S/cm), even at 12% filler content. This result may be associated with a less efficient dispersion of MWCNT within the TPU matrix. Electromagnetic shielding effectiveness (EMI SE) increased with MWCNT content, exceeding 60 dB in PLA and reaching up to 50 dB in TPU. Anechoic chamber tests with a 3D- printed PLA/MWCNT (12%) box confirmed attenuations above 40 dB at 5.9 GHz and between 10 and 15 dB at 3.5 GHz, SEM analyses confirmed the rheological and electrical results, indicating a more homogeneous dispersion of nanotubes in the PLA/MWCNT composites than in the TPU/MWCNT ones. The results highlight PLA/MWCNT as a promising material for EMI shielding in electronics, 5G networks, and aerospace applications....
Achieving precise control over electronic modulation has always been challenging in the strategy of modifying transition metal-nitrogen/carbon (M-NC) with heteroatoms for oxygen reduction and evolution reaction (ORR/ OER). In this study, we propose a novel precursor coordination strategy that successfully achieves accurate control over the particle size of Fe metal and the micro-distance between S and Fe species through differentiated precursor coordination effects, thereby enabling direct electronic modulation and remote electronic modulation of Fe species. For the two distinct modes of electronic modulation, the direct electronically modulated Fe/SLNC exhibits exceptional ORR activity (E1/2 = 0.89 V) and an oxygen overpotential difference between ORR and OER (ΔE = 0.70 V), rendering it highly promising for applications in rechargeable zinc-air batteries. Density functional theory (DFT) theoretical calculations demonstrate that direct electron modulation significantly reduces energy barriers for ORR/OER and effectively enhances reaction rates. Analysis based on d-band center theory reveals that direct electron modulation lowers the d-band center position on active sites of Fe and promotes desorption processes of oxygen-containing intermediates. These findings provide valuable insights for future doping strategies aimed at modulating metal active sites using heteroatoms....
Background: Glaucoma is a leading cause of irreversible blindness worldwide. Predicting a patient’s future clinical trajectory would help physicians personalize management. We present a novel approach for predicting patient visual field (VF) progression by combining Functional Principal Component Analysis (FPCA) with electronic health record (EHR) data. Methods: We identified glaucoma patients using diagnosis codes who had >=3 VF tests. We developed a 2-stage modeling pipeline: 1) Patients were split 80:10:10 into train, validation, and test sets and classified as fast-progressors or slow-progressors. 2) FPCA was used to predict mean deviation (MD) trajectories over 10 years after the baseline year of VF exams, using the first 2 principal components. To make predictions, the model uses up to one year of baseline VF and EHR data as input, but can flexibly make predictions from as few as a single VF test. Results: 15,764 VF tests belonging to 2,372 patients were included, of which 8.92% of eyes were fast progressors. On the held-out test set, predictions over 10 years of future MD trajectories using baseline VF and EHR clinical data yielded an R2 of 0.646 and an RMSE of 3.67 for fast-progressors, and an R2 of 0.728 and an RMSE of 3.09 for slow-progressors. Performance was higher when predicting over the near term (fast progressors: year 1 R2 0.920, RMSE 1.83; year 5 R2 0.515, RMSE 4.26; slow progressors: year 1 R2 0.918, RMSE 1.771; year 5 R2 0.717, RMSE 3.12). Conclusion: A novel modeling approach combining FPCA with clinical data from EHR was able to model longitudinal clinical trajectories of glaucoma patients. This method is well-suited for modeling longitudinal healthcare data, handling sparse and irregular observation schedules with a varying number of inputs....
The escalating accumulation of waste printed circuit boards (WPCBs) underscores the urgent need for efficient recovery of valuable resources. Notably, WPCBs harbor a considerable number of intact electronic components that remain functional or could be repurposed. Nevertheless, the automated recognition and sorting of these components remain highly challenging, owing to their miniature dimensions, diverse model types, and the absence of publicly available, high-quality datasets. To address these challenges, this paper introduces a novel image dataset of discarded electronic components and proposes a deep learning-based data augmentation model that combines classical augmentation methods with DCGAN and SRGAN to achieve dataset size augmentation. This paper further conducts Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) evaluation on the generated images to ensure their suitability for downstream classification tasks. Experimental results demonstrate significant improvements in classification accuracy, with AlexNet, VGG19, ResNet18, ResNet101, and ResNet152 achieving increases of 6.6%, 9.7%, 4%, 5.4%, and 6.2%, respectively, compared to classical augmentation. This method enables precise identification to facilitate the downstream recovery of intact electronic components, thereby contributing to the conservation of natural resources and the effective mitigation of environmental pollution....
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