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Record W4378471175 · doi:10.1021/prechem.3c00022

Precise Synthesis at the Atomic Scale

2023· review· en· W4378471175 on OpenAlexafffund
Shufang Ji, Caroline Jun, Yuanjun Chen, Dingsheng Wang

Bibliographic record

VenuePrecision Chemistry · 2023
Typereview
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaBanting Research Foundation
KeywordsAtomic unitsCatalysisAtom (system on chip)NanotechnologyRational designMaterials scienceComputer scienceChemistryPhysicsOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

Precise synthesis at the atomic scale is a highly desirable and controllable route for the preparation of heterogeneous catalysts with the desired structure and properties, which promotes the rational design of highly efficient catalysts and facilitates the understanding of structure-properties relationship. The precise construction of the active sites of the catalysts provides important opportunities for atomic insight into the correlation between structure and catalytic performance. In this review, the atomic-level tuning strategies for the precise synthesis of heterogeneous catalysts are summarized with the emphasis on the precise control of the structure of active sites, including single atom sites, dual atom sites and complex active sites. Furthermore, we illustrate the crucial role of atomic-level regulation of structure in determining the catalytic performance by providing typical catalysis examples in different reactions. In the end, some perspectives on the further development of precise synthesis of catalysts at the atomic level are presented.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.050
GPT teacher head0.341
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

Citations33
Published2023
Admission routes2
Has abstractyes

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