Search for: All records

Graphene with in-plane nanoholes, named holey graphene, shows great potential in electrochemical applications due to its fast mass transport and improved electrochemical activity. Scalable nanomanufacturing of holey graphene is generally based on chemical etching using hydrogen peroxide to form through-the-thickness nanoholes on the basal plane of graphene. In this study, we probe into the fundamental mechanisms of nanohole formation under peroxide etching via an integrated experimental and computational effort. The research results show that the growth of nanoholes during the etching of graphene oxide is achieved by a three-stage reduction–oxidation–reduction procedure. First, it is demonstrated that vacancy defects are formed via a partial reduction-based pretreatment. Second, hydrogen peroxide reacts preferentially with the edge-sites of defect areas on graphene oxide sheets, leading to the formation of various oxygen-containing functional groups. Third, the carbon atoms around the defects are removed along with the neighboring carbon atoms via reduction. By advancing the understanding of process mechanisms, we further demonstrate an improved nanomanufacturing strategy, in which graphene oxide with a high density of defects is introduced for peroxide etching, leading to enhanced nanohole formation. 

Nanodiamonds (NDs) have been widely explored for applications in drug delivery, optical bioimaging, sensors, quantum computing, and others. Room-temperature nanomanufacturing of NDs in open air using confined laser shock detonation (CLSD) emerges as a novel manufacturing strategy for ND fabrication. However, the fundamental process mechanism remains unclear. This work investigates the underlying mechanisms responsible for nanomanufacturing of NDs during CLSD with a focus on the laser-matter interaction, the role of the confining effect, and the graphite-to-diamond transition. Specifically, a first-principles model is integrated with a molecular dynamics simulation to describe the laser-induced thermo-hydrodynamic phenomena and the graphite-to-diamond phase transition during CLSD. The simulation results elucidate the confining effect in determining the material’s responses to laser irradiation in terms of the temporal and spatial evolutions of temperature, pressure, electron number density, and particle velocity. The integrated model demonstrates the capability of predicting the laser energy threshold for ND synthesis and the efficiency of ND nucleation under varying processing parameters. This research will provide significant insights into CLSD and advance this nanomanufacturing strategy for the fabrication of NDs and other high-temperature-high-pressure synthesized nanomaterials towards extensive applications.

 

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .