Biotechnological Production of Fuel Hydrogen and Its Market Deployment
Bibliographic record
Abstract
Overcoming the effects of fossil fuel use requires the development and use of carbon neutral fuels. Hydrogen is an ideal green fuel since its combustion gives only water vapour and much higher conversion efficiencies can be obtained using fuel cells, potentially making hydrogen potentially much more attractive than other fuels. However, the vast majority of hydrogen used annually, >96%, is made from fossil fuels. In order for hydrogen to become a practical and sustainable energy vector, renewable methods for its production must be developed. A variety of biological paths to hydrogen production are under investigation. Hydrogen production through dark fermentation has been extensively studied. A variety of microorganisms are capable of producing hydrogen, and processes with mixed microbial consortia can make use of various agricultural, industrial, or municipal wastes. Hydrogen production by photosynthetic bacteria potentially offers advantages over other processes including high substrate conversion yields and the ability to use a wide spectrum of light, and to consume a wide range of substrates from organic acids to sugars derived from wastes. Hydrogen production can be maximized by carrying out a combination of dark fermentation and photofermentation using a variety of different processes. Further research is required to bring biohydrogen production to a level where it is ready for market deployment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".