Helbotorres5348

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. 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. this website 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 functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties.Thermally resistant air-stable organic triradicals with a quartet ground state and a large energy gap between spin states are still unique compounds. In this work, we succeeded to design and prepare the first highly stable triradical, consisting of oxoverdazyl and nitronyl nitroxide radical fragments, with a quartet ground state. The triradical and its diradical precursor were synthesized via a palladium-catalyzed cross-coupling reaction of diiodoverdazyl with nitronyl nitroxide-2-ide gold(I) complex. Both paramagnetic compounds were fully characterized by single-crystal X-ray diffraction analysis, superconducting quantum interference device magnetometry, EPR spectroscopy in various matrices, and cyclic voltammetry. In the diradical, the verdazyl and nitronyl nitroxide centers demonstrated full reversibility of redox process, while for the triradical, the electrochemical reduction and oxidation proceed at practically the same redox potentials, but become quasi-reversible. A series of high-level CASSCF/NEVPT2 calculations was performed to predict inter- and intramolecular exchange interactions in crystals of di- and triradicals and to establish their magnetic motifs. Based on the predicted magnetic motifs, the temperature dependences of the magnetic susceptibility were analyzed, and the singlet-triplet (135 ± 10 cm-1) and doublet-quartet (17 ± 2 and 152 ± 19 cm-1) splitting was found to be moderate. Unique high stability of synthesized verdazyl-nitronylnitroxide triradical opens new perspectives for further functionalization and design of high-spin systems with four or more spins.Silver (Ag) is a widely used heavy metal, and its oxidation state (Ag+) causes serious harm to organisms after bioaccumulation and biomagnification, posing urgent demand for the rapid, efficient, and simply operated Ag+ detection techniques. In this work, a fast, portable, and label-free Ag+ detection sensor based on a Ti3C2Tx MXene field-effect transistor (FET) is reported. The Ti3C2Tx MXene works as the sensing element in the FET sensor, which shows excellent sensing performance, i.e., fast response (few seconds) and good sensitivity and selectivity to Ag+ without any detection label or probe. Utilizing the visual photograph, transmission electron microscopy image, and Ag elemental mapping analysis, the sensing mechanism of the label-free Ti3C2Tx MXene FET sensor is demonstrated to be the in situ reduction of Ag+ and the formation of Ag nanoparticles (AgNPs). Moreover, Ag+ detection in real samples shows that the proposed FET devices have satisfactory sensing capability for Ag+ in tap water and river water.