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Record W4318706860 · doi:10.1038/s41467-023-36259-9

COx hydrogenation to methanol and other hydrocarbons under mild conditions with Mo3S4@ZSM-5

2023· article· en· W4318706860 on OpenAlexaff
Gui Liu, Pengfei Liu, Deming Meng, Taotao Zhao, Xiaofeng Qian, Qiang He, Xuefeng Guo, Jizhen Qi, Luming Peng, Nianhua Xue, Yan Zhu, Jingyuan Ma, Qiang Wang, Xi Liu, Liwei Chen, Weiping Ding

Bibliographic record

VenueNature Communications · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsMinistry of Education and Child Care
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMethanolZSM-5ChemistryCatalysisOrganic chemistryZeolite

Abstract

fetched live from OpenAlex

Abstract The hydrogenation of CO2 or CO to single organic product has received widespread attentions. Here we show a highly efficient and selective catalyst, Mo3S4@ions-ZSM-5, with molybdenum sulfide clusters ([Mo3S4]n+) confined in zeolitic cages of ZSM-5 molecular sieve for the reactions. Using continuous fixed bed reactor, for CO2 hydrogenation to methanol, the catalyst Mo3S4@NaZSM-5 shows methanol selectivity larger than 98% at 10.2% of carbon dioxide conversion at 180 °C and maintains the catalytic performance without any degeneration during continuous reaction of 1000 h. For CO hydrogenation, the catalyst Mo3S4@HZSM-5 exhibits a selectivity to C2 and C3 hydrocarbons stably larger than 98% in organics at 260 °C. The structure of the catalysts and the mechanism of COx hydrogenation over the catalysts are fully characterized experimentally and theorectically. Based on the results, we envision that the Mo3S4@ions-ZSM-5 catalysts display the importance of active clusters surrounded by permeable materials as mesocatalysts for discovery of new reactions.

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 score0.002
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 head0.025
GPT teacher head0.307
Teacher spread0.282 · 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

Citations51
Published2023
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicCatalysts for Methane ReformingFrench-language works237,207