Corn Stover Lignin as a Solid Acid Catalyst for the Esterification of Oleic Acid
Bibliographic record
Abstract
Abstract The catastrophic ramifications of fossil fuels on the environment have prompted the search for renewable energy sources. Over the recent decades, biodiesel has garnered attention as a promising direct alternative to diesel fuel; however, reliance on homogeneous catalysts and the requirement for refined vegetable oil feedstocks present financial and sustainability concerns. Thus, there exists a need for the development of sustainable and cost‐effective catalytic solutions. Herein, the application of lignin, an abundant and renewable biomass, as an effective heterogeneous catalyst is reported for biodiesel production via the esterification of oleic acid. Lignin is extracted from corn straw using sulfuric acid, which endows sulfonic acid groups (0.85 mmol g −1 ) to its structure allowing it to act as an acid catalyst without additional post‐treatments. Conversion of oleic oil to biodiesel is achieved at 97% using a 1:3 oleic acid to methanol molar ratio with a 5 wt.% catalyst loading at 90 °C after only 30 min. Moreover, the catalyst exhibits a remarkable turnover frequency of 2.61 min −1 proving its efficiency. These findings demonstrate that heterogeneous catalysts can be prepared from biomass waste offering a significantly cheaper and less intensive synthesis process and allowing for a paradigm shift to non‐edible and waste cooking oils.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".