Tag: Bryan Goldsmith
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Accelerating innovation with machine learning
Chemical Engineering faculty and students are using machine learning to open up new possibilities in research.
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Three Michigan faculty contribute to 7th edition of H. Scott Fogler’s landmark reaction engineering textbook
The seventh edition of Elements of Chemical Reaction Engineering is co-authored by three Michigan Chemical Engineering faculty members: Bryan Goldsmith, Eranda Nikolla and Nirala Singh.
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Michigan Chemical Engineering leads collaborative effort to address global nitrate pollution
The research led by Bryan Goldsmith and Nirala Singh will develop a low-cost system for nitrate capture and conversion to improve water treatment for resource-limited communities and industries.
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$1.3M to improve urea production and reduce carbon dioxide emissions
Rather than contributing to emissions, the production of an essential fertilizer could consume carbon dioxide, and a U-M team will explore such a method.
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Bryan Goldsmith receives 1938E award
Considered one of the most prestigious awards given by the College, the 1938E is given to only one assistant professor in the College each year in recognition of exceptional mentorship, teaching and contribution to their department.
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Bryan Goldsmith receives NSF CAREER Award
Bryan Goldsmith receives National Science Foundation CAREER Award to enhance the conversion of nitrate into valuable ammonia for waste management across industry, food and water systems.
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Mitigating Carbon Dioxide and Nitrate Emissions Using Renewable Energy
Testing a novel chemical process that could simultaneously reduce emissions and remediate pollutants.
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Bryan Goldsmith receives OpenEye Outstanding Junior Faculty Award in Computational Chemistry
Dow Corning Assistant Professor Bryan Goldsmith receives the OpenEye Outstanding Junior Faculty Award in Computational Chemistry. The American Chemical Society (ACS) grants the award biannually through their Computers in Chemistry (COMP) Division.
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Interpretable machine learning in catalysis
Recent research from U-M ChE professors Suljo Linic and Bryan Goldsmith and their co-advised PhD student Jacques Esterhuizen explores recent advances in machine learning approaches for heterogeneous catalysis.
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Machine learning links material composition and performance in catalysts
Understanding how to design better catalysts could enable sustainable energy tech and make everyday chemicals more environmentally friendly.
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Chemistry and energy: Machine learning to understand catalyst interactions
Toward harnessing machine learning to design the materials we want.
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Bryan Goldsmith is recognized by the American Institute of Chemical Engineers
Named to AIChE’s “35 under 35” list in honor of his accomplishments in energy and environmental research.