Formic Acid Dehydrogenation Using Noble-Metal Nanoheterogeneous Catalysts: Towards Sustainable Hydrogen-Based Energy
Bibliographic record
Abstract
The need for sustainable energy sources is now more urgent than ever, and hydrogen is significant in the future of energy. However, several obstacles remain in the way of widespread hydrogen use, most of which are related to transport and storage. Dilute formic acid (FA) is recognized asa a safe fuel for low-temperature fuel cells. This review examines FA as a potential hydrogen storage molecule that can be dehydrogenated to yield highly pure hydrogen (H2) and carbon dioxide (CO2) with very little carbon monoxide (CO) gas produced via nanoheterogeneous catalysts. It also present the use of Au and Pd as nanoheterogeneous catalysts for formic acid liquid phase decomposition, focusing on the influence of noble metals in monometallic, bimetallic, and trimetallic compositions on the catalytic dehydrogenation of FA under mild temperatures (20–50 °C). The review shows that FA production from CO2 without a base by direct catalytic carbon dioxide hydrogenation is far more sustainable than existing techniques. Finally, using FA as an energy carrier to selectively release hydrogen for fuel cell power generation appears to be a potential technique.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".