Technological Insights on Glycerol Valorization into Propanediol through Thermocatalytic and Synthetic Biology Approaches
Bibliographic record
Abstract
The adverse effects of climate change, predominantly propelled by greenhouse gas emissions from fossil fuels, underscore the urgency of seeking sustainable alternatives to fossil fuel use. Amid growing concerns about climate change caused by fossil fuels and petrochemicals, this review focuses on sustainable solutions through the conversion of glycerol into value-added biochemicals. Glycerol, as the main byproduct of biodiesel production, is a particularly attractive chemical due to its potential to be upgraded into value-added building blocks and biochemicals. This review provides a detailed analysis of different thermochemical (catalytic) and synthetic biology (fermentative) pathways for the conversion of glycerol into 1,2-propanediol and 1,3-propanediol, which have proven industrial and commercial applications globally. The synthesis of propanediol from glycerol hydrogenolysis and other catalytic processes using different active metals and acidic oxides is reviewed. The reaction mechanism involved in hydrogenolysis reactions concerning the surface reaction mechanism is systematically discussed. The metabolic activities of promising microorganisms in fermenting glycerol, as the carbon source used to produce propanediol, are illustrated and elaborated. Combining these insights, this review is a comprehensive resource that can foster a better understanding of glycerol transformation into propanediol and its implications for sustainable chemistry and industrial practices. This exploration of alternative methods emphasizes the potential of sustainable approaches to reshape production practices and contribute to climate change mitigation.
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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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".