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Record W3048799828 · doi:10.1126/science.abc3183

Using nature’s blueprint to expand catalysis with Earth-abundant metals

2020· review· en· W3048799828 on OpenAlexaff
R. Morris Bullock, Jingguang G. Chen, Laura Gagliardi, Paul J. Chirik, Omar K. Farha, Christopher H. Hendon, Christopher W. Jones, John A. Keith, Jerzy Klosin, Shelley D. Minteer, Robert H. Morris, Alexander T. Radosevich, Thomas B. Rauchfuss, Neil A. Strotman, Aleksandra Vojvodić, Thomas R. Ward, Jenny Y. Yang, Yogesh Surendranath

Bibliographic record

VenueScience · 2020
Typereview
Languageen
FieldChemistry
TopicMetal-Catalyzed Oxygenation Mechanisms
Canadian institutionsUniversity of TorontoDow Chemical (Canada)
FundersBasic Energy SciencesNational Institute of General Medical SciencesOffice of ScienceNational Institutes of HealthU.S. Department of Energy
KeywordsRhodiumBlueprintCatalysisPalladiumChemistryPrecious metalPlatinumNanotechnologyMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Numerous redox transformations that are essential to life are catalyzed by metalloenzymes that feature Earth-abundant metals. In contrast, platinum-group metals have been the cornerstone of many industrial catalytic reactions for decades, providing high activity, thermal stability, and tolerance to chemical poisons. We assert that nature's blueprint provides the fundamental principles for vastly expanding the use of abundant metals in catalysis. We highlight the key physical properties of abundant metals that distinguish them from precious metals, and we look to nature to understand how the inherent attributes of abundant metals can be embraced to produce highly efficient catalysts for reactions crucial to the sustainable production and transformation 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.077
GPT teacher head0.367
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations581
Published2020
Admission routes1
Has abstractyes

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