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Record W4393156387 · doi:10.1002/cphc.202300608

CO<sub>2</sub> Hydrogenation to CH<sub>3</sub>OH on Metal‐Doped TiO<sub>2</sub>(110): Mechanisms, Strain Effect and a New Thermodynamic‐Kinetic Relation

2024· article· en· W4393156387 on OpenAlexaff
Huili Lu, Deshuai Yang, Zhao‐Xu Chen

Bibliographic record

VenueChemPhysChem · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsKinetic energyDopingStrain (injury)MetalPhysical chemistryMaterials scienceThermodynamicsChemistryInorganic chemistryMetallurgyPhysics

Abstract

fetched live from OpenAlex

Abstract Surface strain and linear thermodynamic‐kinetic relation are interesting topics in catalysis. Development of low temperature methanol catalysts of high activity and selectivity is of particularly importance for conversion of CO 2 to methanol. In the present paper CO 2 hydrogenation to methanol on Znx@TiO 2 (110) (x=0–2) was explored using density functional calculations and microkinetic simulations. The reaction mechanisms on the three model systems were determined and it is shown that Zn2@TiO 2 (110) is the most active. The most favorable pathway on Zn2@TiO 2 (110) is identified and CO 2 +H to HCOO is found to be the rate‐controlling step. It is demonstrated that there is a linear relation (named AEB relation) between the adsorption energies of the initial states and the barriers for the controlling step on the 18 systems studied. Calculations on strained surfaces show that the AEB relation exists within ±1 % strain. Sr2@TiO 2 (110) and −1 % strained CaZn and ZnCu doped TiO 2 (110) are potential good low temperature catalysts and deserve experimental testing.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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