Engineering topics

Blum, Volker

Publications and source records attributed to Blum, Volker.

3 records

DOE OSTI2024

Site Disorder Drives Cyanide Dynamics and Fast Ion Transport in Li 6 PS 5 CN

Halide argyrodite solid-state electrolytes of the general formula Li 6 PS 5 X exhibit complex static and dynamic disorder that plays a crucial role in ion transport processes. Here, we unravel the rich interplay between site disorder and dynamics in the plastic crystal argyrodite Li 6 PS 5 CN and the impact on ion diffusion processes through a suite of experimental and computational methodologies, including temperature-dependent synchrotron powder X-ray diffraction, AC electrochemical impedance spectroscopy, 7 Li solid-state NMR, and machine learning-assisted molecular dynamics simulations. Sulfide and (pseudo)halide site disorder between the two anion sublattices unilaterally improves long-range lithium diffusion irrespective of the (pseudo)halide identity, which demonstrates the importance of site disorder in dictating bulk ionic conductivity in the argyrodite family. Furthermore, we find that anion site disorder modulates the presence and time scales of cyanide rotational dynamics. Ordered configurations of anions enable fast, quasi-free rotations of cyanides that occur on time scales of 10 11 Hz at T = 300 K. In contrast, we find that cyanide dynamics are slow or frozen in Li 6 PS 5 CN when site disorder between the cyanide and sulfide sublattices is present at T = 300 K. We rationalize the observed differences in cyanide dynamics in the context of elastic dipole interactions between neighboring cyanide anions and local strain induced by the configurations of site disorder that may impact the energetic landscape for cyanide rotational dynamics. Through this study, we find that anion disorder plays a decisive role in dictating the extent and time scales of both lithium ion and cyanide dynamics in Li 6 PS 5 CN.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hydrogen Bonding Analysis of Structural Transition-Induced Symmetry Breaking and Spin Splitting in a Hybrid Perovskite Employing a Synergistic Diffraction-DFT Approach

Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) offer an outstanding opportunity for spin-related technologies owing in part to their tunable structural symmetry breaking and distortions driven by organic–inorganic hydrogen (H) bonds. However, understanding how H-bonds tailor inorganic symmetry and distortions and therefore enhance spin splitting for more effective spin manipulation remains imprecise due to challenges in measuring H atom positions using X-ray diffraction. Here, we report a thermally induced structural transition (at ~209 K) for a 2D HOIP, (2-BrPEA) 2 PbI 4 [2-BrPEA = 2-(2-bromophenyl)ethylammonium], which induces inversion asymmetry and a strong spin splitting (ΔE > 30 meV). While X-ray diffraction generally establishes heavy atom coordinates, we utilize neutron diffraction for accurate H atom position determination, demonstrating that the structural transition-induced rearrangement of H-bonds with distinct bond strengths asymmetrically shifts associated iodine atom positions. Further, consequences of this shift include an increased structural asymmetry, an enhanced difference between adjacent interoctahedra distortions (i.e., Pb–I–Pb bond angles), and therefore significant spin splitting. We further show that H-only density-functional theory (DFT) relaxation of the X-ray structure shifts H atoms to positions that are consistent with the neutron experimental data, validating a convenient pathway to more generally improve upon HOIP H-bonding analyses derived from quicker/less-expensive X-ray data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗