A5028249540- Machine Learning in Materials Science
- Catalysis and Hydrodesulfurization Studies
- Quantum Dots Synthesis And Properties
- Catalysis for Biomass Conversion
- Chalcogenide Semiconductor Thin Films
- Electrocatalysts for Energy Conversion
- Computational Drug Discovery Methods
- Catalysts for Methane Reforming
- Advanced Electron Microscopy Techniques and Applications
- Electron and X-Ray Spectroscopy Techniques
- Spectroscopy and Chemometric Analyses
- Catalytic Processes in Materials Science
- Nanomaterials for catalytic reactions
- X-ray Diffraction in Crystallography
- Innovative Microfluidic and Catalytic Techniques Innovation
- Assisted Reproductive Technology and Twin Pregnancy
- Grief, Bereavement, and Mental Health
- Maternal and Perinatal Health Interventions
University of Southern California
2019-2024
The understanding and control of colloidal nanocrystal syntheses are essential for discovery optimization desired properties therefore play a key role in the applications these materials. Typical one variable at time (OVAT) methods limit ability researchers to achieve such goals by providing one-dimensional insight into complex, multidimensional experimental domain, wasting precious resources process. Design experiments (DoE) conjunction with response surface methodology (RSM) offers an...
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as experiments (DoE) machine learning are an effective more efficient way predictably control synthesis. Here, we present Viewpoint recent progress leveraging predicting controlling outcomes We first compare how choice (statistical DoE vs...
The hydrogenation of CO
Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu-Se phase diagram is complex contains multiple crystal structures addition to several metastable that not found on thermodynamic diagram. Consequently, ability synthetically navigate this space poses significant challenge. We demonstrate data-driven learning can successfully map minimal number experiments. combine...
Nanoparticles of nickel phosphide are finding wide ranging utility as catalysts for hydrodesulfurization, hydrogen evolution reaction, and hydrodeoxygenation bio-oils. Herein, we present a methodology to tailor monodisperse nanoparticles in terms size phase through the use statistical response surface methodology. Colloidal were synthesized by replacing octadecene (ODE), commonly used organic solvent, more sustainable phosphonium-based ionic liquid (IL). The replacement ODE with IL resulted...
Thiospinels, such as CoNi2S4, are showing promise for numerous applications, including catalysts the hydrogen evolution reaction, hydrodesulfurization, and oxygen reduction reactions; however, CoNi2S4 has not been synthesized small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes size typically rely upon one variable at a time (OVAT) that only labor intensive but also lack ability identify higher-order...
Transition metal carbides (TMCs) have attracted significant attention because of their applications toward a wide range catalytic transformations. However, the practicality synthesis is still limited harsh conditions in which most TMCs are prepared. Recently, solution-phase phase-pure α-MoC1–x nanoparticles was presented. While this synthetic route yielded with exceptional performance, reaction parameter space not explored, and catalyst throughput optimized for scale-up. Continuous flow...
Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals their ensemble behavior. Despite this, traditional methods to quantify are laborious. New developments in automated classification will accelerate these analyses but assessment machine learning models limited by human accuracy ground truth, causing even unsupervised have inherent bias. Herein, we introduce synthetic image rendering solve...