Progress of hydrogen production from food waste – A systematic, content, and bibliometric review
Bibliographic record
Abstract
Food waste (FW) presents difficulties for waste management which is a significant worldwide issue. However, there is a growing energy crisis globally, and there is not enough fossil fuel available to support the growing demand. This can however be overcome using environmentally acceptable and sustainable energy such as biohydrogen. This study thus employed a systematic, content-based, and bibliometric review approach to analyze the literature on hydrogen production from FW resources within the last two decades. The study used the bibliometric analysis tools (i.e., Biblioshiny in R and the VOSviewer) to analyze and visualize a total of 2,022 pertinent documents on the subject matter obtained from the Scopus database. According to the analyzed data, biohydrogen, a biofuel produced through biological processes, has the potential to reduce greenhouse gas emissions, but its widespread adoption requires addressing production rate, yield, and process scaling. China turned out to be the nation with the most papers on the subject, totaling 2,610. The USA (829), India (501), Italy (471), South Korea (419), Brazil (374), Japan (285), Germany (257), Spain (256), Canada (187), and the UK (187) were the other top-performing nations. The study ended with future research directions that researchers can work on in the future. The findings of this study could guide future research on the conversion of food waste to hydrogen energy based on the research gaps identified in the study.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.156 | 0.137 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".