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Direct enthalpy measurement for hydrogenation of liquid organic hydrogen carriers by Differential Scanning Calorimetry under H2 pressure

2024· preprint· en· W4395675422 on OpenAlexaff
Florian D’Ambra, V. Faucheux, Emmanuel Nicolas, Thibault Cantat, Gérard Gebel, Parviz Hajiyev

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicChemical Thermodynamics and Molecular Structure
Canadian institutionsFrancophone University Association
FundersEuropean Research CouncilFuel Cells and Hydrogen Joint UndertakingCentre National de la Recherche Scientifique
KeywordsBifunctionalAcetophenoneEnthalpyDifferential scanning calorimetryChemistrySteric effectsCatalysisHydrogenPhysical chemistryOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

In this contribution, we report the hydrogenation enthalpy direct measurement of well-studied Liquid Organic Hydrogen Carriers (LOHC) (Dibenzyltoluene, N-Ethylcarbazole, 2-Octanone) and of a new LOHC (Acetophenone) by differential scanning calorimetry (DSC) under H2 pressure. The obtained hydrogenation enthalpies showed good correspondence with DFT predictions and the literature. Moreover, multistep hydrogenation processes were investigated, evidencing that transfer hydrogenation occurred during the reaction. The study of bifunctional LOHC such as acetophenone and its derivatives showcased the potential of direct enthalpy measurement for rapid LOHC screening. Besides, a DFT correction factor for this specific bifunctional LOHC class was experimentally obtained, enabling more precise DFT predictions for similar systems. Kinetic parameters such as the activation energies for the catalytic Ru/Al2O3 system were also determined to be in the 45-60 kJ/mol range, compatible with the H2 adsorption on the Ru surface. The detrimental effect of steric hinderance was also observed. Finally, the reaction order in H2 was experimentally estimated to ~1 for all of the studied LOHCs.

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 categoriesMeta-epidemiology (narrow)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.235
Teacher spread0.222 · 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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