Open-Source DC-DC Converter Enabling Direct Integration of Solar Photovoltaics with Anion Exchange Membrane Electrolyzer for Green Hydrogen Production
Bibliographic record
Abstract
Fully sustainable hydrogen production demands renewable energy sources. This study uses an approach that combines solar photovoltaic (PV) systems with minimal batteries to tailor the energy supply to the unique demands of anion exchange membrane (AEM) electrolyzers. An open source DC-DC adjustable converter is designed, prototyped, and tested to enable an AEM to operate at its optimum efficiency without disrupting the continuous operation of existing loads. A structured operating schedule is simulated to align PV performance with AEM electrolyzer characteristics. The results show the >90% efficiency open-source converter was able to directly power the electrolyzer while taking advantage of solar energy surplus for hydrogen production. By strategically scheduling the electrolyzer to maximize output and minimize waste the system only utilizes excess solar energy. By employing this sustainable method, the study highlights a scalable solution that not only enhances the efficiency of hydrogen production, but also promotes the deployment of PV.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".