Engineering the Surface Chemistry and Morphology of Polymeric Carbon Nitrides Towards Greener Heterogeneous Catalysts for Biodiesel Synthesis
Bibliographic record
Abstract
Biodiesel remains one of the most promising alternatives to replace fossil fuel-derived petrodiesel. Nonetheless, conventional biodiesel synthesis relies on homogeneous alkali-based catalysts that involve long and tedious purification steps , increasing biodiesel production costs. Heterogeneous catalysts have emerged as promising alternatives to circumvent these drawbacks, as they can easily be recovered and reused. Herein, polymeric carbon nitride dots and nanosheets are synthesized through a solid-phase reaction between urea and sodium citrate. Their morphology and surface chemistry are tuned by varying the precursor's ratio, and the materials are investigated as catalysts in the transesterification reaction of canola oil to biodiesel. A conversion of > 98% is achieved using a 5 wt% catalyst loading, oil to methanol ratio of 1:36 at 90 °C for 4 h, with the performance maintained over at least five reuse cycles. In addition, the effect of the transesterification reaction parameters on the reaction kinetics is evaluated, which follows a pseudo-first-order (PFO) regime. Combined with a deep understanding of the catalyst's surface, these results have allowed us to propose a reaction mechanism similar to the one observed for homogenous alkali catalysts. These carbon nitride-based nanoparticles offer a metal-free and cost-effective alternative to conventional homogeneous and metal-based heterogeneous catalysts.
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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) | 0.000 | 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".