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Record W2981494409 · doi:10.1002/anie.201912367

Zeolite‐Encaged Single‐Atom Rhodium Catalysts: Highly‐Efficient Hydrogen Generation and Shape‐Selective Tandem Hydrogenation of Nitroarenes

2019· review· en· W2981494409 on OpenAlexaff
Qiming Sun, Ning Wang, Tianjun Zhang, Risheng Bai, Álvaro Mayoral, Peng Zhang, Qinghong Zhang, Osamu Terasaki, Jihong Yu

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typereview
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsDalhousie University
FundersNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectShanghaiTech UniversityNational Natural Science Foundation of China
KeywordsCatalysisChemistryZeoliteAmmonia boraneRhodiumHydrolysisInorganic chemistryHydrogen productionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Single‐atom catalysts are emerging as a new frontier in heterogeneous catalysis because of their maximum atom utilization efficiency, but they usually suffer from inferior stability. Herein, we synthesized single‐atom Rh catalysts embedded in MFI ‐type zeolites under hydrothermal conditions and subsequent ligand‐protected direct H 2 reduction. C s ‐corrected scanning transmission electron microscopy and extended X‐ray absorption analyses revealed that single Rh atoms were encapsulated within 5‐membered rings and stabilized by zeolite framework oxygen atoms. The resultant catalysts exhibited excellent H 2 generation rates from ammonia borane (AB) hydrolysis, up to 699 min −1 at 298 K, representing the top level among heterogeneous catalysts for AB hydrolysis. The catalysts also showed superior catalytic performance in shape‐selective tandem hydrogenation of various nitroarenes by coupling with AB hydrolysis, giving >99 % yield of corresponding amine products.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.043
GPT teacher head0.289
Teacher spread0.246 · 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

Citations446
Published2019
Admission routes1
Has abstractyes

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Same venueAngewandte Chemie International EditionSame topicNanomaterials for catalytic reactionsFrench-language works237,207