arXiv subjects

Ajay S. Karakoti

Publications and source records attributed to Ajay S. Karakoti.

4 recordsLinked to original sources

arXiv2024

Molecular-Resolution Imaging of Ice Crystallized from Liquid Water

Despite the ubiquity of ice, a molecular-resolution image of ice crystallized from liquid water or the resulting defect structure has never been obtained. Here, we report the stabilization and angstrom-resolution electron imaging of ice Ih crystallized from liquid water by developing cryogenic liquid-cell transmission electron microscopy (CRYOLIC-TEM). We combine lattice mapping with molecular dynamics simulations to reveal that ice formation is highly tolerant to nanoscale defects such as misoriented subdomains and trapped gas bubbles, which are stabilized by molecular-scale structural motifs. Importantly, bubble surfaces adopt low-energy nanofacets and create negligible strain fields in the surrounding crystal. These bubbles can dynamically nucleate, grow, migrate, dissolve, and coalesce under electron irradiation and be monitored in situ near a steady state. This work opens the door to understanding water crystallization behaviors at an unprecedented spatial resolution.

cond-mat.mtrl-sci↗

arXiv2024

Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. Here we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines (VMs) in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na$_x$Li$_{3-x}$YCl$_6$ ($0 < x < 3$) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe that this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

cond-mat.mtrl-sci↗

arXiv2021

Redox Active Cerium Oxide Nanoparticles: Current Status and Burning Issues

Research on cerium oxide nanoparticles (nanoceria) has captivated the scientific community due to their unique physical and chemical properties, such as redox activity and oxygen buffering capacity, which made them available for many technical applications, including biomedical applications. The redox mimetic antioxidant properties of nanoceria have been effective in the treatment of many diseases caused by reactive oxygen species (ROS) and reactive nitrogen species. The mechanism of ROS scavenging activity of nanoceria is still elusive, and its redox activity is controversial due to mixed reports in the literature showing pro-oxidant and antioxidant activity. In lieu of its current research interest, it is critical to understand the behaviour of nanoceria in the biological environment and provide answers to some of the critical and open issues. This review critically analyses the status of research on the application of nanoceria to treat diseases caused by ROS. It reviews the proposed mechanism of action and shows the effect of surface coatings on its redox activity. It also discusses some of the crucial issues in deciphering the mechanism and redox activity of nanoceria and suggests areas of future research.

cond-mat.mtrl-sci↗

arXiv2020

Polymer coated cerium oxide nanoparticles as oxidoreductase-like catalysts

Cerium oxide nanoparticles have been shown to mimic oxidoreductase enzymes by catalyzing the decomposition of organic substrates and reactive oxygen species. This mimicry can be found in superoxide radicals and hydrogen peroxides, harmful molecules produced in oxidative stress asso-ciated diseases. Despite the fact that nanoparticle functionalization is mandatory in the context of nanomedicine, the influence of polymer coatings on their enzyme-like catalytic activity is poorly understood. In this work, six polymer coated cerium oxide nanoparticles are prepared by associa-tion of 7.8 nm cerium oxide cores with two poly(sodium acrylate) and four poly(ethylene glycol) (PEG) grafted copolymers with different terminal or anchoring end groups, such as phosphonic acids. The superoxide dismutase-, catalase-, peroxidase- and oxidase-like catalytic activities of the coated nanoparticles were systematically studied. It is shown that the polymer coatings do not af-fect the superoxide dismutase-like, impair the catalase-like and oxidase-like and surprisingly im-proves peroxidase-like catalytic activities of cerium oxide nanoparticles. It is also demonstrated that the particles coated with the PEG-grafted copolymers perform better than the poly(acrylic acid) coated ones as oxidoreductase-like enzymes, a result that confirms the benefit of having phosphon-ic acids as anchoring groups at the particle surface.

physics.app-ph↗