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On-demand hydrogen production at room temperature through silylation of alcohol-amines with no added catalysts

2025· article· en· W4407160675 on OpenAlexafffund
S. M. Martin, Abdelhamid Sayari

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilylationCatalysisAlcoholHydrogen productionHydrogenChemistryProduction (economics)Organic chemistry

Abstract

fetched live from OpenAlex

Hydrogen-based energy is an eco-friendly alternative to fossil fuels. However, its use is limited due to the lack of readily available hydrogen resources. This work focuses on the production of hydrogen on-demand by coupling hydrosilanes and alcohol-amines. This is a single step reaction that takes place at room temperature with no added catalysts, which represent significant advantages compared to alternative procedures reported in the literature. The scope of the reaction was investigated using 24 alcohol-amines and 8 hydrosilanes. Depending on the reactants used, kinetic essays demonstrated that for most reactants, more than 70 % hydrogen yield was obtained within 30 min of reaction. The generation of hydrogen was confirmed by gas chromatography, and the silylated products were identified by 13 C and 29 Si-NMR. Results suggested that both OH and NH x were involved in hydrogen production, with the hydroxyl groups being significantly more reactive. In addition to being a reactant, the amine group was found to be an internal catalyst. • Hydrosilanes react with alcohol-amines with no added catalysts to produce hydrogen. • Hydrogen yields exceed 70 % in 30 min at room conditions depending on reactants. • Hydroxyls are more reactive than amine groups toward the dehydrogenative coupling. • Amine groups also act as internal 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 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 score0.238
Threshold uncertainty score0.722

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)0.0000.000

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 head0.241
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.

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

Citations4
Published2025
Admission routes2
Has abstractyes

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