Chemicals Production from Glycerol through Heterogeneous Catalysis: A Review
Bibliographic record
Abstract
Utilization of biofuels generated from renewable sources has attracted broad attention due to their benefits such as reducing consumption of fossil fuels, sustainability, and consequently prevention of global warming. The production of biodiesel causes a huge amount of by-product, crude glycerol, to accumulate. Glycerol, because of its unique structure having three hydroxyl groups, can be converted to a variety of industrially valuable products. In recent decades, increasing studies have been carried out on different catalytic pathways to selectively produce a wide range of glycerol derivatives. In the current review, the main routes including carboxylation, oxidation, etherification, hydrogenolysis, esterification, and dehydration to convert glycerol to value-added products are investigated. In order to achieve more glycerol conversion and higher desired product selectivity, acquisition of knowledge on the catalysts, the type of acidic or basic, the supports, and studying various reaction pathways and operating parameters are necessary. This review attempts to summarize the knowledge of catalytic reactions and mechanisms leading to value-added derivatives of glycerol. Additionally, the application of main products from glycerol are discussed. In addition, an overview on the market of glycerol, its properties, applications, and prospects is presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".