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Quantum Beam Sci., Volume 8, Issue 2 (June 2024) – 3 articles

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17 pages, 3654 KiB  
Article
Modification of Cu Oxide and Cu Nitride Films by Energetic Ion Impact
by Noriaki Matsunami, Masao Sataka, Satoru Okayasu and Bun Tsuchiya
Quantum Beam Sci. 2024, 8(2), 12; https://doi.org/10.3390/qubs8020012 - 10 Apr 2024
Viewed by 573
Abstract
We have investigated lattice disordering of cupper oxide (Cu2O) and copper nitride (Cu3N) films induced by high- and low-energy ion impact, knowing that the effects of electronic excitation and elastic collision play roles by these ions, respectively. For high-energy [...] Read more.
We have investigated lattice disordering of cupper oxide (Cu2O) and copper nitride (Cu3N) films induced by high- and low-energy ion impact, knowing that the effects of electronic excitation and elastic collision play roles by these ions, respectively. For high-energy ion impact, degradation of X-ray diffraction (XRD) intensity per ion fluence or lattice disordering cross-section (YXD) fits to the power-law: YXD = (BXDSe)NXD, with Se and BXD being the electronic stopping power and a constant. For Cu2O and Cu3N, NXD is obtained to be 2.42 and 1.75, and BXD is 0.223 and 0.54 (kev/nm)−1. It appears that for low-energy ion impact, YXD is nearly proportional to the nuclear stopping power (Sn). The efficiency of energy deposition, YXD/Se, as well as Ysp/Se, is compared with YXD/Sn, as well as Ysp/Sn. The efficiency ratio RXD = (YXD/Se)/(YXD/Sn) is evaluated to be ~0.1 and ~0.2 at Se = 15 keV/nm for Cu2O and Cu3N, meaning that the efficiency of electronic energy deposition is smaller than that of nuclear energy deposition. Rsp = (Ysp/Se)/(Ysp/Sn) is evaluated to be 0.46 for Cu2O and 0.7 for Cu3N at Se = 15 keV/nm. Full article
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19 pages, 1763 KiB  
Article
Estimating Lung Volume Capacity from X-ray Images Using Deep Learning
by Samip Ghimire and Santosh Subedi
Quantum Beam Sci. 2024, 8(2), 11; https://doi.org/10.3390/qubs8020011 - 28 Mar 2024
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Abstract
Estimating lung volume capacity is crucial in clinical medicine, especially in disease diagnostics. However, the existing estimation methods are complex and expensive, which require experts to handle and consequently are more error-prone and time-consuming. Thus, developing an automatic measurement system without a human [...] Read more.
Estimating lung volume capacity is crucial in clinical medicine, especially in disease diagnostics. However, the existing estimation methods are complex and expensive, which require experts to handle and consequently are more error-prone and time-consuming. Thus, developing an automatic measurement system without a human operator that is less prone to human error and, thus, more accurate has always been a prerequisite. The limitation of radiation dose and various medical conditions in technologies like computed tomography was also the primary concern in the past. Although qualitative prediction of lung volume may be a trivial task, designing clinically relevant and automated methods that effectively incorporate imaging data is a challenging problem. This paper proposes a novel multi-tasking-based automatic lung volume estimation method using deep learning that jointly learns segmentation and regression of volume estimation. The two networks, namely, segmentation and regression networks, are sequentially operated with some shared layers. The segmentation network segments the X-ray images, whose output is regressed by the regression network to determine the final lung volume. Besides, the dataset used in the proposed method is collected from three different secondary sources. The experimental results show that the proposed multi-tasking approach performs better than the individual networks. Further analysis of the multi-tasking approach with two different networks, namely, UNet and HRNet, shows that the network with HRNet performs better than the network with UNet with less volume estimation mean square error of 0.0010. Full article
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33 pages, 27060 KiB  
Review
Lithium-Ion Batteries under the X-ray Lens: Resolving Challenges and Propelling Advancements
by Mahdieh Samimi, Mehran Saadabadi and Hassan Hosseinlaghab
Quantum Beam Sci. 2024, 8(2), 10; https://doi.org/10.3390/qubs8020010 - 27 Mar 2024
Viewed by 966
Abstract
The quest for high-performance lithium-ion batteries (LIBs) is at the forefront of energy storage research, necessitating a profound understanding of intricate processes like phase transformations and thermal runaway events. This review paper explores the pivotal role of X-ray spectroscopies in unraveling the mysteries [...] Read more.
The quest for high-performance lithium-ion batteries (LIBs) is at the forefront of energy storage research, necessitating a profound understanding of intricate processes like phase transformations and thermal runaway events. This review paper explores the pivotal role of X-ray spectroscopies in unraveling the mysteries embedded within LIBs, focusing on the utilization of advanced techniques for comprehensive insights. This explores recent advancements in in situ characterization tools, prominently featuring X-ray diffraction (XRD), X-ray tomography (XRT), and transmission X-ray microscopy (TXM). Each technique contributes to a comprehensive understanding of structure, morphology, chemistry, and kinetics in LIBs, offering a selective analysis that optimizes battery electrodes and enhances overall performance. The investigation commences by highlighting the indispensability of tracking phase transformations. Existing challenges in traditional methods, like X-ray absorption spectroscopy (XAS), become evident when faced with nanoscale inhomogeneities during the delithiation process. Recognizing this limitation, the review emphasizes the significance of advanced techniques featuring nanoscale resolution. These tools offer unprecedented insights into material structures and surface chemistry during LIB operation, empowering researchers to address the challenges posed by thermal runaway. Such insights prove critical in unraveling interfacial transport mechanisms and phase transformations, providing a roadmap for the development of safe and high-performance energy storage systems. The integration of X-ray spectroscopies not only enhances our understanding of fundamental processes within LIBs but also propels the development of safer, more efficient, and reliable energy storage solutions. In spite of those benefits, X-ray spectroscopies have some limitations in regard to studying LIBs, as referred to in this review. Full article
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