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Record W2986795513 · doi:10.1002/cjce.23678

AuPd bimetal immobilized on amine‐functionalized SBA‐15 for hydrogen generation from formic acid: The effect of the ratio of toluene to DMF

2019· article· en· W2986795513 on OpenAlexvenueno aff
Mo Liu, Qiulin Zhang, Yuzhen Shi, Huimin Wang, Guangcheng Wei, Tengxiang Zhang, Haiyang Sun, Jifeng Wang, Yaqing Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsFormic acidBimetalAmine gas treatingTolueneCatalysisChemistryNuclear chemistryHydrogenInorganic chemistryPolymer chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The volume ratio of toluene to N,N‐dimethylformamide (DMF) was adjusted during the amine functionalization of SBA‐15 to change the amine content of SBA‐15. XPS, FTIR, and TGA analyses indicated that under the experimental synthetic conditions the number of amine groups varies with the ratio of toluene/DMF, and the highest content of amine could be obtained with a volume ratio of toluene/DMF = 3:2. The hydrogen production experiment of formic acid decomposition showed that the hydrogen production efficiency over the Au‐Pd‐SBA‐15‐NH 2 catalysts increased with the increase in the surface amine content of the Au‐Pd‐SBA‐15‐NH 2 . The optimal Au‐Pd‐SBA‐15‐NH 2 ‐TD (toluene/DMF = 3:2) catalyst proved to have the smallest Au‐Pd bimetal nanoparticle size and exhibited a turnover frequency (TOF) = 631 hours −1 and an activation energy (Ea) = 20.8 kJ · mol −1 at 25°C. The catalytic performance of hydrogen generation from the formic acid was improved due to the synergistic effect between the amine‐functionalized SBA‐15 and the Au‐Pd bimetal (metal‐support interactions).

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.108
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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.

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

Citations8
Published2019
Admission routes1
Has abstractyes

Explore more

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