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Record W4410195463 · doi:10.1002/anie.202501370

Engineering Charge Polarized Au Sites for Low‐Temperature Acetylene Hydrochlorination

2025· article· en· W4410195463 on OpenAlexaff
Chun Li, Ruoting Liu, Zilong Zhang, Fangmin Zuo, Tingting Jiang, Haifeng Zhang, Bolin Wang, Nieves López‐Salas

Bibliographic record

VenueAngewandte Chemie International Edition · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsWestern University
FundersPeople's Government of Jilin ProvinceNational Natural Science Foundation of China
KeywordsAcetyleneCharge (physics)Materials scienceEnvironmental scienceEngineering physicsChemistryPhysicsOrganic chemistryParticle physics

Abstract

fetched live from OpenAlex

Abstract Cationic gold catalyzed acetylene hydrochlorination represents a classical landmark in eliminating global mercury pollution, but their sustainable implementation is hindered by acetylene‐dependence design criteria and high operating temperatures. Herein, a platform of carbon‐supported single‐atoms Au catalysts (Au/BC and Au/NC) with polarized charge characteristics are developed via engineering Au sites with hosted B, N configurations. The negatively charged Au/BC catalyst unlocks the low‐temperature inactivity (413–423K) of the Au/NC catalyst while exhibiting superior catalytic performance in the 433–473K operating temperature range. We confirm that the classical scaling relationships on acetylene can be broken by narrowing the adsorption capacity between acetylene and HCl on Au δ⁻ sites via facilitating the back‐donation of d electrons into the antibonding orbitals of acetylene. Prolonging the durability of Au catalysts is achieved through preceding an additional robust Au δ⁻ → Au δ⁺ cycle prior to the classic Au δ⁺ → Au 0 route. This work opens a promising avenue for low temperature vinyl chloride production.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.008
GPT teacher head0.257
Teacher spread0.248 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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