Topological Data Analysis of Ion Migration Mechanism

Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase ($\alpha$-phase) of AgI, to show its effectiveness on the ion-migration mechanism analysis.Time-averaged persistence diagrams of $\alpha$-AgI, which quantitatively records the shape and size of the ring structures in the given atomic configurations, clearly showed the emergence of the four-membered rings formed by two Ag and two I ions at high temperatures. They were identified as common structures during the Ag ion migration. The averaged potential energy change due to the deformation of four-membered ring during Ag migration agrees well with the activation energy calculated from the conductivity Arrhenius plot. The concerted motion of two Ag ions via the four-membered ring was also successfully extracted from molecular dynamics simulations by our approach, providing the new insight into the specific mechanism of the concerted motion.Comment: 8 pages, 7 figure

Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images

Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones

Colossal reversible barocaloric effects in a plastic crystal mediated by lattice vibrations and ion diffusion

Solid-state methods for cooling and heating promise a more sustainable alternative to current compression cycles of greenhouse gases and inefficient fuel-burning heaters. Barocaloric effects (BCE) driven by hydrostatic pressure ($p$) are especially encouraging in terms of large adiabatic temperature changes ($|\Delta T| \sim 10$ K) and colossal isothermal entropy changes ($|\Delta S| \sim 100$ JK$^{-1}$kg$^{-1}$). However, BCE typically require large pressure shifts due to irreversibility issues, and sizeable $|\Delta T|$ and $|\Delta S|$ seldom are realized in a same material. Here, we demonstrate the existence of colossal and reversible BCE in LiCB$_{11}$H$_{12}$, a well-known solid electrolyte, near its order-disorder phase transition at $\approx 380$ K. Specifically, for $\Delta p \approx 0.23$ $(0.10)$ GPa we measured $|\Delta S_{\rm rev}| = 280$ $(200)$ JK$^{-1}$kg$^{-1}$ and $|\Delta T_{\rm rev}| = 32$ $(10)$ K, which individually rival with state-of-the-art barocaloric shifts obtained under similar pressure conditions. Furthermore, over a wide temperature range, pressure shifts of the order of $0.1$ GPa yield huge reversible barocaloric strengths of $\approx 2$ JK$^{-1}$kg$^{-1}$MPa$^{-1}$. Molecular dynamics simulations were carried out to quantify the role of lattice vibrations, molecular reorientations and ion diffusion on the disclosed colossal BCE. Interestingly, lattice vibrations were found to contribute the most to $|\Delta S|$ while the diffusion of lithium ions, despite adding up only slightly to the accompanying entropy change, was crucial in enabling the molecular order-disorder phase transition. Our work expands the knowledge on plastic crystals and should motivate the investigation of BCE in a variety of solid electrolytes displaying ion diffusion and concomitant molecular orientational disorder.Comment: 13 pages, 7 figure

Mixed alkali-ion transport and storage in atomic-disordered honeycomb layered NaKNi2TeO6

Honeycomb layered oxides constitute an emerging class of materials that show interesting physicochemical and electrochemical properties. However, the development of these materials is still limited. Here, we report the combined use of alkali atoms (Na and K) to produce a mixed-alkali honeycomb layered oxide material, namely, NaKNi2TeO6. Via transmission electron microscopy measurements, we reveal the local atomic structural disorders characterised by aperiodic stacking and incoherency in the alternating arrangement of Na and K atoms. We also investigate the possibility of mixed electrochemical transport and storage of Na+ and K+ ions in NaKNi2TeO6. In particular, we report an average discharge cell voltage of about 4 V and a specific capacity of around 80 mAh g–1 at low specific currents (i.e., < 10 mA g–1) when a NaKNi2TeO6-based positive electrode is combined with a room-temperature NaK liquid alloy negative electrode using an ionic liquid-based electrolyte solution. These results represent a step towards the use of tailored cathode active materials for “dendrite-free” electrochemical energy storage systems exploiting room-temperature liquid alkali metal alloy materials

A blockchain-enabled internet of medical things system for breast cancer detection in healthcare

Intelligent and sustainable healthcare systems can considerably benefit from applying Computational Intelligence (CI) and Artificial Intelligence (AI). These technological breakthroughs can reduce the ecological footprint and raise the bar for excellence. Yet, the broad adoption of such technologies for cutting-edge Internet of Things (IoT) applications generates enormous amounts of data, which can heavily strain the available computational resources. The major motivation behind this study is to provide evidence that Gated Recurrent Units (GRUs), a sophisticated subclass of Recurrent Neural Networks (RNNs), can outperform traditional RNNs. These technologies can be effective in identifying and treating breast cancer. This study collects data from tagged IoT devices and trains a GRU-RNN classifier. The Wisconsin Diagnostic Breast Cancer (WDBC) data tests the system’s accuracy. The results show the proposed Internet of Medical Things (IoMT) is more effective than the current methods in recall, accuracy, and precision while preserving 95% of the original GRU-RNN

Ring Mechanism of Fast Na+Ion Transport in Na2LiFeTeO6: Insight from MolecularDynamics Simulation

Honeycomb layered oxides have attracted recent attention because of their rich crystal chemistry. However, the atomistic mechanisms of cationic transport in these structures remain vastly unexplored. Herein, we perform an extensive, systematic molecular dynamics study on Na2LiFeTeO6 using combined force-field and first-principles-based molecular dynamics simulations. We use are fined set of inter-atomic potential parameters of a previously reported potential model that represents various structural and transport properties of this recently reported promising material for all-solid-state battery applications. The present simulation study elucidates the roles of octahedral ordering and entropic contributions in Na+-ion distribution in the ab-plane. Our theoretical simulation also develops a ring-like atomistic diffusion mechanism and relevant atomistic energy barriers that help to understand the origin of fast ion conduction in honeycomb layered oxides

Modelling Cationic Diffusion in Nickel-Based Honeycomb Layered Tellurates using Vashishta-Rahman Interatomic Potential and Relevant Insights

Although the fascinatingly rich crystal chemistry of honeycomb layered oxides has been accredited as the propelling force behind their remarkable electrochemistry, the atomistic mechanisms surrounding their operations remain unexplored. Thus, herein, we present an extensive molecular dynamics study performed systematically using a reliable set of inter-atomic potential parameters of A2Ni2TeO6 (where A = Li, Na, and K). We demonstrate the effectiveness of the Vashishta-Rahman form of the inter-atomic potential in reproducing various structural and transport properties of this promising class of materials and predict an exponential increase in cationic diffusion with larger inter-layer distances. The simulations demonstrate the correlation between broadened inter-layer (inter-slab) distances associated with the larger ionic radii of K and Na compared to Li and the enhanced cationic conduction exhibited in K2Ni2TeO6 and Na2Ni2TeO6 relative to Li2Ni2TeO6. Whence, our findings connect lower potential energy barriers, favourable cationic paths and wider bottleneck size along the cationic diffusion channel within frameworks (comprised of larger mobile cations) to the improved cationic diffusion experimentally observed in honeycomb layered oxides. Furthermore, we elucidate the role of inter-layer distance and cationic size in cationic diffusion. Our theoretical studies reveal the dominance of inter-layer distance over cationic size, a crucial insight into the further performance enhancement of honeycomb layered oxides.<br /