To realize the development of high‐performance electrodes materials, real‐time observation tool that can visualize dynamical changes of electrode materials upon lithiation and sodiation is highly ...critical. Recently, in situ transmission electron microscopy (TEM) has gained much attraction as it provides rich information on both phase and morphological transitions of electrode materials upon lithiation and sodiation in an atomic scale. Nevertheless, two significant limitations are present for current in situ TEM study: i) most of the in situ TEM experiments use a specially designed TEM holder, which is costly and suitable for use in only selective types of TEM; and ii) recently developed in situ TEM techniques with no applied bias have inherent limitations in the size of the sample (difficult for a size above 50 nm). Overcoming these two aforementioned issues, bias‐free graphene solid cells (GSCs) are now introduced, which can conduct real‐time observation of electrode materials in any size dimensions and morphologies without using bias. In this work, dynamical changes of hollow SnO2 nanotubes (NTs) are visualized upon lithiation and sodiation in real time using GSCs, where rich information is obtained on the morphology‐dependent, ion‐dependent characteristics of dynamical changes.
A novel bias‐free graphene solid cell is suggested as a cost‐effective and facile in situ transmission electron microscopy (TEM) tool to observe electrode materials. Upon e‐beam irradiation, lithium fluoride (LiF) and sodium fluoride (NaF) can be easily decomposed to trigger lithiation and sodiation, which can proceed to observe dynamical changes of electrode materials inside TEM.
Optimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven ...domain-expert-derived-descriptors from literature, we use an inverse design surrogate model to build up the experimental parameters-property relationship. Without struggling with the trial-and-error method, the model enables design variables prediction that serves as an effective strategy for cathode retrosynthesis. More importantly, not only did we overcome the data scarcity problem, but the machine learning model has guided us to achieve cathode with high discharge capacity and Coulombic efficiency of 209.5 mAh/g and 86%, respectively. This work demonstrates an inverse design-to-device pipeline with unprecedented potential to accelerate the discovery of high-energy-density cathodes.
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•Inverse-design surrogate model is employed for discharge capacity prediction of lithium-ion batteries cathode materials.•Statistical imputation technique is exploited to solve the missing values and inconsistency in training data.•The proposed method enables the realization of high discharge capacity of 209.5 mAh/g with 86% coulombic efficiency.•We identified eleven descriptors from literature and reverse-engineered the synthesis parameters with high accuracy.
Modern semiconductor fabrication is challenged by difficulties in overcoming physical and chemical constraints. A major challenge is the wet etching of dummy gate silicon, which involves the removal ...of materials inside confined spaces of a few nanometers. These chemical processes are significantly different in the nanoscale and bulk. Previously, electrical double-layer formation, bubble entrapment, poor wettability, and insoluble intermediate precipitation have been proposed. However, the exact suppression mechanisms remain unclear due to the lack of direct observation methods. Herein, we investigate limiting factors for the etching kinetics of silicon with tetramethylammonium hydroxide at the nanoscale by using liquid-phase transmission electron microscopy, three-dimensional electron tomography, and first-principles calculations. We reveal suppressed chemical reactions, unstripping phenomena, and stochastic etching behaviors that have never been observed on a macroscopic scale. We expect that solutions can be suggested from this comprehensive insight into the scale-dependent limiting factors of fabrication.
Binary metal sulfides have been explored as sodium storage materials owing to their high theoretical capacity and high stable cyclability. Nevertheless, their relative high charge voltage and ...relatively low practical capacity make them less attractive as an anode material. To resolve the problem, addition of alloying elements is considerable. Copper antimony sulfide is investigated as a representative case. In this study, we do not only perform electrochemical characterization on CuSbS2, but also investigate its nonequilibrium sodiation pathway employing in-/ex situ transmission electron microscopy, in situ X-ray diffraction, and density functional theory calculations. Our finding provides valuable insights on sodium storage into ternary metal sulfide including an alloying element.
Table transformations are a critical skill to master in order to fluently work with data. In introductory data science courses, however, students have found these transformations particularly ...challenging to learn. One complex transformation is the pivot transformation, which reorganizes a table based on aggregation and summarizing along selected columns and rows. Current assessments test student understanding in static scenarios. Thus, there is an opportunity to help students explicitly work through the steps and variables needed to express a pivot transformation in a randomizable manner. As such, we explore whether a dynamic digital assessment for the pivot transformation can effectively achieve mastery learning towards this skill. Our design is inspired by Parsons problems, in which answer components (pivot table output labels and values) can be composed into the output of a pivot transformation. A question can be derived from a small randomized dataset, and randomized Pandas code that operates upon the dataset and can be autograded. We plan to conduct a pilot study with data science students to investigate whether 1) using a programmable online platform to practice pivot tables helps improve performance on exams, and 2) randomization and instant feedback on the online platform contribute to improved student learning.
Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these ...disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders.
A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease.
For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77).
This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.
