Aldridge Lancaster (trainheaven2)

To lower the introduction and maintenance costs of autonomous power supplies for driving Internet-of-things (IoT) devices, we have developed low-cost Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules. Our development approach combines computational science, experiments, mapping measurements, and machine learning (ML). FAST materials have a good balance of mechanical properties and excellent chemical stability, superior to that of conventional Bi-Te-based materials. However, it remains challenging to enhance the power factor (PF) and lower the thermal conductivity of FAST materials to develop reliable power generation devices. This forum paper describes the current status of materials development based on experiments and ML with limited data, together with power generation module fabrication related to FAST materials with a view to commercialization. Combining bulk combinatorial methods with diffusion couple and mapping measurements could accelerate the search to enhance PF for FAST materials. see more We report that ML prediction is a powerful tool for finding unexpected off-stoichiometric compositions of the Fe-Al-Si system and dopant concentrations of a fourth element to enhance the PF, i.e., Co substitution for Fe atoms in FAST materials.Alzheimer's disease (AD) is characterized by the presence of extracellular senile plaques formed by β-amyloid (Aβ) peptides in the patient's brain. Previous studies have shown that the plaques in the AD brains are colocalized with the advanced glycation end products, which is mainly formed from a series of nonenzymatic reactions of proteins with reducing sugars or reactive dicarbonyls. Glycation was also demonstrated to increase the neurotoxicity of the Aβ peptides. To clarify the impact of glycation on Aβ aggregation, we synthesized two glycated Aβ42 peptides by replacing Lys16 and Lys28 with Nε-carboxymethyllysine respectively to mimic the occurrence of protein glycation. Afterward, we monitored the aggregation kinetics and conformational change for two glycated peptides. We also used fluorescence correlation spectroscopy to probe the early stage of peptide oligomerization and tested their abilities in copper binding and reactive oxygen species production. Our data show that glycation significantly slows down the aggregation process and induces more cytotoxicity especially at position 28. We speculated that the higher toxicity might result from a relatively stable oligomeric form of peptide and not from ROS production. The data shown here emphasized that glycated proteins would be an important therapeutic target in AD treatments.Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict the structures and dynamics at normal operating conditions, high-temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long time scales (e.g., nanosecond) and large system sizes (e.g., 105 atoms) while maintaining ab initio density functio