Urban waste upcycling to a recyclable solid acid catalyst for converting levulinic acid platform molecules into high-value products
Bibliographic record
Abstract
The conversion of levulinic acid (LA) into alkyl levulinates is highly significant due to the wide range of applications for these products, including their use as fuel additives, solvents, and fragrances. In order to meet the growing need for environmentally friendly chemical production, this study takes a circular economy approach by upcycling a common urban waste, i.e., pine needles, to synthesize a robust heterogeneous acid catalyst, subsequently used to efficiently upgrade LA into levulinates. By utilizing a single-step procedure under mild operating conditions, the resulting PiNe–SO3H catalyst demonstrated good performances and flexibility in synthesizing diverse bio-derived levulinates. In fact, the catalyst showed an exceptionally broad range of applicability, resulting in isolated yields ranging from ̴ 46% to ̴ 93%, which is an unprecedented achievement. The catalyst's ability to be reused was tested, revealing remarkable performance for up to 10 consecutive cycles with negligible loss in efficiency. Additionally, a significant focus was directed towards developing a method that minimizes waste during the isolation process. This involved optimizing reaction conditions and rationalizing work-up procedures, resulting in low Environmental factor (E-factor) values ranging from 1.2 to 8.9. To comprehensively assess the overall environmental sustainability of the process, various additional green metrics were calculated, and the Ecoscale tool was employed as well. Furthermore, mechanistic investigations elucidated the favored reaction pathway, underscoring that, under the optimized conditions, the prevailing mechanism entails direct esterification, as opposed to the generation of a pseudo-ester intermediate.
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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 | 0.001 |
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".