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Record W3128333264 · doi:10.1021/acscatal.0c05325

A Career in Catalysis: James A. Dumesic

2021· article· en· W3128333264 on OpenAlexaff
Carl R.F. Lund, Bruce J. Tatarchuk, Nelson Cardona-Martı́nez, Josephine M. Hill, Marco A. Sánchez-Castillo, George W. Huber, Yuriy Román‐Leshkov, Dante Simonetti, Yomaira J. Pagán‐Torres, Thomas J. Schwartz, Ali Hussain Motagamwala

Bibliographic record

VenueACS Catalysis · 2021
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCatalysisChemistryPsychologyOrganic chemistry

Abstract

fetched live from OpenAlex

After 43 years as a professor in the Chemical and Biological Engineering Department at the University of Wisconsin-Madison, James A. Dumesic retired in 2019. Jim is one of the most influential researchers in the field of heterogeneous catalysis. In this Account, we discuss the scientific discoveries that he made and the intellectual processes that he followed during his illustrious career to steer the field of heterogeneous catalysis into new frontiers. He began his career by fundamentally probing the nature of active sites on heterogeneous catalysts using in situ Mössbauer spectroscopy, electron microscopy, FTIR, and kinetic analysis. This tool kit was used to elucidate the “strong metal support interaction” (SMSI) effect. He developed new microcalorimetric tools to measure the energetics of adsorbates on catalyst surfaces. Jim pioneered microkinetic analysis as a tool to describe heterogeneous reaction kinetics incorporating the essential surface chemistry into kinetic analysis, thereby providing a novel strategy for kinetic assisted catalyst design. Density functional theory (DFT) was combined with FTIR and microcalorimetry to elucidate catalytic surface reactions which were then incorporated into microkinetic models. In the early 2000s, Jim developed the aqueous-phase catalytic processing of biomass-derived oxygenates into fuels and chemicals. This led to the development of new processes to make diesel fuel, jet fuel, gasoline, aromatics, and oxygenated chemicals from renewable resources, several of these technologies are in the process of being commercialized. Jim tailored nanostructured catalytic materials by atomic layer deposition and controlled surface reactions to withstand harsh aqueous-phase biomass processing conditions. He used careful selection and tuning of the solvent composition to achieve substantial control over the activity and selectivity of various biomass upgrading reactions and developed a theory to explain these solvent effects. Underlying these discoveries was a thought process that used fundamental surface chemistry to explain the relationship between the structure, properties, and performance of the catalytic system. Leveraging this thought process across many different reaction classes helped to establish both new tools for catalysis research and to develop new processes for the sustainable production of fuels and chemicals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.183
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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