Catalytic Conversion of Glycerol into Hydrogen and Value-Added Chemicals: Recent Research Advances
Bibliographic record
Abstract
In recent decades, the use of biomass as alternative resources to produce renewable and sustainable biofuels such as biodiesel has gained attention given the situation of the progressive exhaustion of easily accessible fossil fuels, increasing environmental concerns, and a dramatically growing global population. The conventional transesterification of edible, nonedible, or waste cooking oils to produce biodiesel is always accompanied by the formation of glycerol as the by-product. Undeniably, it is essential to economically use this by-product to produce a range of valuable fuels and chemicals to ensure the sustainability of the transesterification process. Therefore, recently, glycerol has been used as a feedstock for the production of value-added H2 and chemicals. In this review, the recent advances in the catalytic conversion of glycerol to H2 and high-value chemicals are thoroughly discussed. Specifically, the activity, stability, and recyclability of the catalysts used in the steam reforming of glycerol for H2 production are covered. In addition, the behavior and performance of heterogeneous catalysts in terms of the roles of active metal and support toward the formation of acrolein, lactic acid, 1,3-propanediol, and 1,2-propanediol from glycerol are reviewed. Recommendations for future research and main conclusions are provided. Overall, this review offers guidance and directions for the sufficient and economical utilization of glycerol to generate fuels and high value chemicals, which will ultimately benefit industry, environment, and economy.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".