An overview of the kinetics and catalysis of hydrogen storage on organic liquids
Bibliographic record
Abstract
Abstract The potential for chemical H2 storage on liquid organic hydrogen carriers (LOHCs) has focused attention on the catalytic reactions needed to store and release H2 from the LOHCs. Herein we review our recent studies on the use of N‐ethylcarbazole and carbazole as LOHCs. Experimental data show that the hydrogenation reactions are relatively facile, although N‐ethylcarbazole hydrogenates 10×'s faster than carbazole on a 5 wt% Ru/Al2O3 catalyst at 150°C. Dehydrogenation of dodecahydro‐N‐ethylcarbazole is more difficult than hydrogenation and is structure sensitive on Pd catalysts. Maximum activity and 100% selectivity to the completely dehydrogenated product, N‐ethylcarbazole, was achieved over a 4 wt% Pd/SiO2 catalyst with dPd ∼ 9 nm. The dehydrogenation TOF of dodecahydrocarbazole and dodecahydrofluorene were much lower than dodecahydro‐N‐ethylcarbazole. DFT was used to identify the dehydrogenation mechanism and explain the experimental observations. Both theoretical and experimental results lead to the conclusion that dodecahydro‐N‐ethylcarbazole is a better H2storage candidate than dodecahydrocarbazole.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".