Chemocatalytic Production of Lactates from Biomass-Derived Sugars
Bibliographic record
Abstract
In recent decades, a great deal of attention has been paid to the exploration of alternative and sustainable resources to produce biofuels and valuable chemicals, with aims of reducing the reliance on depleting confined fossil resources and alleviating serious economic and environmental issues. In line with this, lignocellulosic biomass-derived lactic acid (LA, 2-hydroxypropanoic acid), to be identified as an important biomass-derived commodity chemical, has found wide applications in food, pharmaceuticals, and cosmetics. In spite of the current fermentation of saccharides to produce lactic acid, sustainability issues such as environmental impact and high cost derived from the relative separation and purification process will be growing with the increasing demands of necessary orders. Alternatively, chemocatalytic approaches to manufacture LA from biomass (i.e., inedible cellulose) have attracted extensive attention, which may give rise to higher productivity and lower costs related to product work-up. This work presents a review of the state-of-the-art for the production of LA using homogeneous, heterogeneous acid, and base catalysts, from sugars and real biomass like rice straw, respectively. Furthermore, the corresponding bio-based esters lactate which could serve as green solvents, produced from biomass with chemocatalysis, is also discussed. Advantages of heterogeneous catalytic reaction systems are emphasized. Guidance is suggested to improve the catalytic performance of heterogeneous catalysts for the production of LA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".