Binary microdroplets as liquid hydrogen carriers for enhanced hydrogen production
Bibliographic record
Abstract
Liquid organic hydrogen carriers (LOHCs) are promising media for hydrogen storage, transport, and generation. However, their dehydrogenation in aqueous environment is severely limited by phase immiscibility, resulting in sluggish reaction kinetics. In this study, we investigated a base-catalyzed hydrogen evolution reaction using binary LOHC-alcohol microdroplets combined with a 0.5 M NaOH solution. As efficient microscopic reacting entities, the binary microdroplets at certain mixing ratios achieved up to a 8-fold increase in hydrogen production. This enhancement is particularly pronounced for the binary mixture of polymeric LOHC and a long-chain alcohol with a maximal production rate at an equal mixing ratio where both in-drop and on-drop reactions take place. By following the hydrogen bubble evolution from a single binary droplet, we uncovered a transition in hydrogen bubble formation modes, from in-drop at low mixing ratios, to clustering at intermediate ratios, and on-drop at high ratios, each correlating with distinct hydrogen production rates. Notably, the droplet-based approach achieved a high hydrogen yield in the absence of hazardous solvents or metal catalysts. The produced hydrogen is further demonstrated to power a fuel cell, showcasing its direct application in energy generation. These findings highlight the potential of tuning the composition of reactive microdroplets to unlock highly efficient hydrogen production and utilization pathways. • Binary microdroplets of hydrogen carrier and alcohol react with water for efficient H 2 evolution. • Microdroplets exhibit dual reaction sites: within the microdroplet and at the interface. • Optimal mixing yields an 8-fold increase in hydrogen production. • Higher mixing ratios shift H 2 bubble growth from in-drop to on-drop .
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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 | 0.001 |
| 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".