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Record W4407257973 · doi:10.1002/aic.18764

Sequential hydrogenation enhanced by bidirectional hydrogen spillover over cascade catalyst

2025· article· en· W4407257973 on OpenAlexaff
Shuai Wang, Rongyao Wang, Yong Wang, Yipin Lv, Lianghao Song, Huaiqing Zhao, Xuchuan Jiang, Riming Hu, Guozhu Chen

Bibliographic record

VenueAIChE Journal · 2025
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaJinan Science and Technology Bureau
KeywordsCatalysisCascadeHydrogen spilloverHydrogenChemistrySpillover effectCascade reactionMaterials scienceCombinatorial chemistryMetalChemical engineeringNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Formulating a synergetic strategy to govern the catalytic function of dual metal sites is paramount to achieving precise control of cascade reactions. Herein, we construct a dual‐site cascade catalyst with Pt and Ru species localized in the micropores and mesopores of zeolite, respectively. This architecture enables the spatial separation of Pt and Ru sites in nanoscale proximity. Compared to mono/bi‐metallic catalysts, this cascade catalyst enables a 4.4–9.5 times enhancement in activity during the sequential hydrogenation of nitroaromatics to cyclohexylamine. Particularly, bidirectional hydrogen spillover assists hydrogenation between Pt and Ru sites is confirmed, where active hydrogen migrates from the less catalytic activity metal to the adjacent metal sites during the first/second step in the cascade reaction. Characterization studies and density functional theory calculations suggest that bidirectional hydrogen spillover enhances the coverage of active hydrogen at the active sites for each hydrogenation step, thereby reducing the energy barrier of the rate‐controlling step. This intriguing phenomenon reveals the mechanism of accelerated hydrogenation and presents an opportunity for devising immensely efficient cascade 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.051
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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)0.0030.000

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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