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Record W4401544277 · doi:10.1002/cctc.202400287

A Specific Review of CO<sub>2</sub> Catalytic Conversion Reactions Based on the Concept of Catalytic Sites Contiguity

2024· review· en· W4401544277 on OpenAlexaff
Jingye Chen, Xu Zhao, Mohsen Shakouri, Hui Wang

Bibliographic record

VenueChemCatChem · 2024
Typereview
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
Fundersnot available
KeywordsCatalysisMethanationCarbon monoxideChemistryWater-gas shift reactionMethaneHydrogenSelectivitySyngasMethanolChemical engineeringCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Thermocatalytic conversions of carbon dioxide (CO 2 ) to value‐added products offer promising approaches to achieving net negative emissions. The catalysts for CO 2 conversions, particularly for CO 2 hydrogenation reactions, usually involve more than one catalytic sites working together. In this review, we first introduce the advanced characterization techniques used to identify the catalytic sites in CO 2 hydrogenation catalysts, sites for hydrogen (H 2 ) activation and CO 2 adsorption/activation. We then discuss how the dual or multiple‐site configurations influence the catalytic activity and selectivity in reactions such as reverse water‐gas shift (RWGS), CO 2 methanation, and CO 2 hydrogenation for methanol (MeOH). We finally explain the Catalytic Sites Contiguity (CSC) concept that our research group developed from the work in CO 2 reforming of methane and use it to understand the relationship between the spatial arrangement of catalytic sites and the efficiency of reactant activation and conversion in recent publications on MeOH synthesis from CO 2 hydrogenation. We hope our insights into the impact of CSC on catalytic performance lead to a potential top‐down design method in optimizing the CO 2 hydrogenation catalysts.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.688
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.295
Teacher spread0.252 · 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 designSystematic review
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

Citations13
Published2024
Admission routes1
Has abstractyes

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