Publications by authors named "A Dive"

LiLaZrO (LLZO) and related ceramic solid electrolytes feature excellent stability and reasonable ionic conductivity, but processing remains challenging. High-temperature co-sintering is required for successful integration with the electrode, which is energetically costly and can lead to unacceptable cathode degradation. The introduction of dopants can promote lower-temperature processing by improving deformability and disrupting lattice integrity; however, an unbiased, systematic study correlating these properties to the dopant chemistry and composition is lacking.

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Context: Nidogen-2 () hypermethylation has been implicated in many types of cancers, such as lung, bladder, and gastric carcinomas. However, its role has not yet been studied adequately in head and neck squamous cell carcinomas (HNSCC). HNSCCs constituting a major portion of the global cancer load, it is of importance to diagnose and treat them at earliest.

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Unlabelled: Isoprene, a Natural Volatile Organic Compound (NVOC) is one of the chief by-products of plant metabolism with important applications in the synthesis of rubber and pharmaceuticals as a platform molecule. Isoprene was obtained earlier from petroleum sources; however, to synthesise it new fermentation-based strategies are being adopted. Bioinformatics tools were utilised to isolate the Isoprene Synthase (IspS) gene which converts the precursors Isopentenyl Diphosphate (IPP) and Dimethylallyl Diphosphate (DMAPP) into isoprene.

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Retrospective studies showed a relationship between vitamin D status and COVID-19 severity and mortality, with an inverse relation between SARS-CoV-2 positivity and circulating calcifediol levels. The objective of this pilot study was to investigate the effect of vitamin D supplementation on the length of hospital stay and clinical improvement in patients with vitamin D deficiency hospitalized with COVID-19. The study was randomized, double blind and placebo controlled.

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Batteries based on solid-state electrolytes, including LiLaZrO (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions.

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