Atomic Structure Transformations of C-doped Ge2Sb2Te5 Using In-Situ X-ray Techniques.
Abstract not provided.
Alverson, Danielle↗
Engineering topics
Olds, Daniel
Publications and source records attributed to Olds, Daniel.
Molecular Structure and Phase Equilibria of Molten Fluoride Salt with and without Dissolved Cesium: FLiNaK–CsF (5 mol %)
Not Available
36 MATERIALS SCIENCE↗
Revitalizing Iron Redox by Anion-Insertion-Assisted Ferro- and Ferri-Hydroxides Conversion at Low Alkalinity
Iron hydroxides are desirable alkaline battery electrodes for low cost and environmental beneficence. However, hydrogen evolution on charging and Fe 3 O 4 formation on discharging cause low storage capacity and poor cycling life. Here, we report that green rust (GR) (Fe 2+ 4 Fe 3+ 2 (HO – ) 12 SO 4 ), formed via sulfate insertion, promotes Fe(OH) 2 /FeOOH conversion and shows a discharge capacity of ~211 mAh g –1 in half-cells and Coulombic efficiency of 93% after 300 cycles in full-cells. Theoretical calculations show that Fe(OH) 2 /FeOOH conversion is facilitated by intercalated sulfate anions. Classical molecular dynamics simulations reveal that electrolyte alkalinity strongly impacts the energetics of sulfate solvation, and low alkalinity ensures fast transport of sulfate ions. Anion-insertion-assisted Fe(OH) 2 /FeOOH conversion, also achieved with Cl – ion, paves a pathway toward efficient utilization of Fe-based electrodes for sustainable applications.
25 ENERGY STORAGE↗
Machine learning enabling high-throughput and remote operations at large-scale user facilities
Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.
36 MATERIALS SCIENCE↗