Progress of hydrogen production from food waste – A systematic, content, and bibliometric review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it