Chemo-catalytic Esterification and Transesterification over Organic Polymer-Based Catalysts for Biodiesel Synthesis
Bibliographic record
Abstract
The major sources of fuels in today's world predominantly come from traditional fossil resources such as coal, petroleum and natural gas, which are limited and nonrenewable. Meanwhile, their consumption releases large undesirable greenhouse gas and noxious gases. Therefore, the development of renewable and sustainable feedstocks to replace traditional fossil resources has attracted great interest. Biodiesel, mainly produced through esterification and transesterification reaction from renewable oil resources using acids and bases as catalysts, is deemed as a green and renewable biofuel that shows enormous potential to replace fossil diesel. Compared to homogeneous catalytic systems, the development of efficient and stable heterogeneous catalysts is vital to synthesizing biodiesel in an efficient and green manner. Among the developed solid catalysts, organic polymer- based catalytic materials are an extremely important topic, wherein distinct advantages of higher concentration of active sites and better stability of active groups are associated with each other. In this review, effective catalytic valorization of sustainable feedstocks into biodiesel via transesterification and esterification reactions mediated by functionalized organic polymer-based catalysts is discussed. Special emphasis has been given to the synthetic routes to the versatile organic polymers-based catalytic materials, and some other interesting catalytic roles derived from physicochemical property, like adjustable hydrophilicity and hydrophobicity along with swelling property in transesterification and esterification, are also illustrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".