An Overview of Challenges for the Future of Hydrogen.

Hydrogen's wide availability and versatile production methods establish it as a primary green energy source, driving substantial interest among the public, industry, and governments due to its future fuel potential. Notable investment is directed toward hydrogen research and material innovation for transmission, storage, fuel cells, and sensors. Ensuring safe and dependable hydrogen facilities is paramount, given the challenges in accident control. Addressing material compatibility issues within hydrogen systems remains a critical focus. Challenges, roadmaps, and scenarios steer long-term planning and technology outlooks. Strategic visions align actions and policies, encompassing societal and ecological dimensions. The confluence of hydrogen's promise with material progress holds the prospect of reshaping our energy landscape sustainably. Forming collective future perspectives to foresee this emerging technology's potential benefits is valuable. Our review article comprehensively explores the forthcoming challenges in hydrogen technology. We extensively examine the challenges and opportunities associated with hydrogen production, incorporating CO2 capture technology. Furthermore, the interaction of materials and composites with hydrogen, particularly in the context of hydrogen transmission, pipeline, and infrastructure, are discussed to understand the interplay between materials and hydrogen dynamics. Additionally, the exploration extends to the embrittlement phenomena during storage and transmission, coupled with a comprehensive examination of the advancements and hurdles intrinsic to hydrogen fuel cells. Finally, our exploration encompasses addressing hydrogen safety from an industrial perspective. By illuminating these dimensions, our article provides a panoramic view of the evolving hydrogen landscape.

Volume Resistivity of Viton Polymer under Thermal Aging.

This study examines the influence of various electrical parameters on the volume resistivity of the Viton fluoroelastomer. The transient current, the temperature dependence of volume resistivity, the voltage dependence of resistivity, and the surface morphology of Viton insulators are investigated for new and aged specimens. An accelerated aging process has been employed in order to simulate the natural aging of insulators in service. A detailed comparison between the new and aged samples is presented. The transient effect, which is a challenge to the resistivity measurement of insulators, has been investigated. The first 60 s of the resistivity measurement test showed a significant influence from the transient effect and should be excluded from the data. The volume resistivity of both new and aged samples decreased when the temperature increased. However, the resistivity of the aged sample was lower than the new one at all tested temperatures. When the temperature increased from 35 to 190 °C, resistivity decreased from 4.77 × 1010 to 6.99 × 108 Ω-cm for the new sample and from 2.6 × 1010 to 6.68 × 108 Ω-cm for the aged sample under 500 V. Additionally, the results from this study showed that the volume resistivity is inversely proportional to the applied voltage. Finally, scanning electron microscope (SEM) micrographs/images allowed us to closely examine the surface morphology of new and aged Viton samples. The surface of aged samples has been recognized with higher surface roughness and more significant surface cracks leading to poor performance under high voltage applications.

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence.

Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of those methods as they are often challenged by limited datasets, performance legitimacy on imbalanced data, and have their results typically reported without proper confidence intervals. Considering the opportunity to address these issues, in this study, we propose and test six modified deep learning models, including VGG16, InceptionResNetV2, ResNet50, MobileNetV2, ResNet101, and VGG19 to detect SARS-CoV-2 infection from chest X-ray images. Results are evaluated in terms of accuracy, precision, recall, and f- score using a small and balanced dataset (Study One), and a larger and imbalanced dataset (Study Two). With 95% confidence interval, VGG16 and MobileNetV2 show that, on both datasets, the model could identify patients with COVID-19 symptoms with an accuracy of up to 100%. We also present a pilot test of VGG16 models on a multi-class dataset, showing promising results by achieving 91% accuracy in detecting COVID-19, normal, and Pneumonia patients. Furthermore, we demonstrated that poorly performing models in Study One (ResNet50 and ResNet101) had their accuracy rise from 70% to 93% once trained with the comparatively larger dataset of Study Two. Still, models like InceptionResNetV2 and VGG19's demonstrated an accuracy of 97% on both datasets, which posits the effectiveness of our proposed methods, ultimately presenting a reasonable and accessible alternative to identify patients with COVID-19.