Heat to H<sub>2</sub>:<sub/>Using Waste Heat to Set Up Concentration Differences for Reverse Electrodialysis Hydrogen Production
Bibliographic record
Abstract
The present work suggests two concepts for producing hydrogen by reverse electrodialysis. Reverse electrodialysis is a technology that uses concentration differences to create electrical energy. In this work, the energy is utilised as direct hydrogen production within a closed-loop system. For both system alternatives, waste heat is used to set up the mentioned concentration differences. The first concept is evaporation, where heat is added to boil off excess water from a concentrated solution and thereby increase its concentration. The second concept removes heat in order to precipitate excess salt. For the precipitation concept to work, a salt where the solubility is highly dependent on temperature must be used. KNO 3 fulfils this requirement. As part of a proof of concept, the conductivity of membranes soaked in KNO 3 was investigated. The conductivity of the salt in two commercialised membranes, Fumatech FKE-50 and FAS-30, was measured and compared to NaCl in the same membranes. The conductivity of K + in FKE-50 was found to be 4.5 and 6.6 mS cm −1 at 25 ◦ C and 40 ◦ C respectively. The conductivity of NO −3 in FAS-30 was found to be 4.3 mS cm −1 and 6.5 mS cm −1 at 25 ◦ C and 40 ◦ C respectively. Neither of the membranes change conductivity with soaking concentrations. The conductivity at 40 ◦ C compared to 25 ◦ C is significantly better in the FKE membrane, and seemingly better in the FAS membrane. Potential peak power densities for a RED unit cell is 1.29 W m −2 with the precipitation system, and 28.1 W m −2 when evaporation is used.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".