Spill Behaviour of Hydrogen Carriers as Alternative Fuels for Ships
Bibliographic record
Abstract
Hydrogen carriers, such as liquid organic hydrogen carriers (LOHCs) and borohydrides, are promising zero-emission alternative fuels for ships.Bringing these hydrogen carriers on board, however, creates new challenges.A major challenge is their spill behaviour.Knowing the spill behaviour is paramount to avoid large-scale environmental disasters.This paper investigates the spill behaviour of four hydrogen carriers (and their conjugates): sodium borohydride, ammonia borane, dibenzyltoluene, and n-ethylcarbazole.The hydrogen carriers were all dissolved in artificial seawater to test their behaviour.Sodium borohydride reacts with seawater, as it also reacts with pure water.However, contrary to expectations, it reacts faster with seawater than regular water.The reaction mechanism behind this is unknown.Ammonia borane does not visibly react with normal water or with seawater.Dibenzyltoluene sinks and forms tiny bubbles which are easily perturbed.Unfortunately, perhydro dibenzyltoluene could not be tested due to technical problems.Nethylcarbazole breaks up into smaller pieces and predominantly stays afloat, likely due to the surface tension of water.Perhydro nethylcarbazole floats but is barely visible in seawater due to its transparency.Preventive measures must be established to avoid largescale spills if these substances are utilised on ships, as they are likely challenging to clean up.
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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".