Unveiling the potentials of biohydrogen as an alternative energy source: Strategies, challenges and future perspectives
Bibliographic record
Abstract
Bio-hydrogen emerges as an environmentally friendly energy carrier, promising to diminish our reliance on fossil fuels. Employing biological approaches for hydrogen production aids in the dual objectives of waste management and energy generation. The economic viability of producing renewable bio-hydrogen from waste biomass is considerable, though the realization of extensive industrial-scale production remains an ongoing aspiration. This review underscores present viewpoints on the generation of bio-hydrogen as an alternative energy reservoir. The facilitation of bio-hydrogen production encompasses techniques like photolysis, fermentation, and electrochemical processes. To augment bio-hydrogen production, optimizing various influential production factors is imperative. Employing bioreactors with tailored designs and configurations can significantly enhance productivity. The incorporation of hybrid and novel strategies to bolster bio-hydrogen production, is recognized as a sturdy strategy. This comprehensive review highlights that biological methods, particularly photo and dark fermentation using various microorganisms, are the most prominent and promising techniques for sustainable bio-hydrogen production. While advancements in bioreactor design, genetic engineering, and the application of nano-materials (especially Ni and Fe) have improved yields, large-scale implementation remains hindered by economic and technological challenges, requiring further research and policy support. • Biohydrogen production methods are described thoroughly. • Bioreactors and other factors influence on biohydrogen are also elucidated. • Nano-materials and nanotechnology devices for biohydrogen production are also discussed. • Ni and Fe are most promising nanomaterials for H 2 production. • Constraints and future challenges are presented based on identified literature gaps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".