Surfactant-Mediated Interfacial Hydrogen Evolution Reaction
Bibliographic record
Abstract
Hydrogen is a highly promising clean energy source without greenhouse gas emissions. Liquid organic hydrogen carriers (LOHCs) offer a promising alternative for convenient storage and transportation. This study investigates the interfacial hydrogen evolution reaction between polymethylhydrosiloxane (PMH), a representative LOHC, and water, focusing on controlling reaction kinetics by modifying interfacial properties with surfactants. The hydrogen production rate at a planar interface between PMH and water catalyzed by sodium hydroxide revealed that surfactants such as Tween 20 and sodium dodecyl sulfate (SDS) can slow down the hydrogen formation by 5 to 20 times, possibly due to an overcrowded interface effect. In contrast, cationic surfactants, such as hexadecyltrimethylammonium bromide (CTAB) and other quaternary ammonium surfactants, act as pseudo phase-transfer catalysts and accelerate the hydrogen formation rate up to 3-fold at a concentration of 0.05 times their critical micelle concentration. As the PMH microdroplets were dispersed in the surfactant aqueous solution, the conversion yields of hydrogen with cationic surfactants reached up to 45%, which is significantly higher than the yields achieved with Tween 20 or SDS. The effects of the surfactant type were further confirmed by following hydrogen bubble growth in a single PMH droplet. Overall, our findings demonstrate that selecting an appropriate surfactant can provide an effective control over the interfacial reaction rate of dehydrogenation of LOHCs. This offers strategies for manipulating liquid-liquid interfaces and controlling in-demand hydrogen production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".