Quantum Beam Science

17 pages, 3654 KiB

Open AccessArticle

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 385

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 (Y_XD) fits to the power-law: Y_XD = (B_XDS_e)^NXD, with S_e and B_XD being the electronic stopping power and a constant. For Cu₂O and Cu₃N, N_XD is obtained to be 2.42 and 1.75, and B_XD is 0.223 and 0.54 (kev/nm)⁻¹. It appears that for low-energy ion impact, Y_XD is nearly proportional to the nuclear stopping power (S_n). The efficiency of energy deposition, Y_XD/S_e, as well as Y_sp/S_e, is compared with Y_XD/S_n, as well as Y_sp/S_n. The efficiency ratio R_XD = (Y_XD/S_e)/(Y_XD/S_n) is evaluated to be ~0.1 and ~0.2 at S_e = 15 keV/nm for Cu₂O and Cu₃N, meaning that the efficiency of electronic energy deposition is smaller than that of nuclear energy deposition. R_sp = (Y_sp/S_e)/(Y_sp/S_n) is evaluated to be 0.46 for Cu₂O and 0.7 for Cu₃N at S_e = 15 keV/nm. Full article

► Show Figures

Figure 1

19 pages, 1763 KiB

Open AccessArticle

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

Viewed by 578

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 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

► Show Figures

Figure 1

33 pages, 27060 KiB

Open AccessReview

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 673

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 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

► Show Figures

Figure 1

Journal Menu

Journal Browser

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

Further Information

Guidelines

MDPI Initiatives

Follow MDPI