Influence of the glycerin content in the preparation of calcium diglyceroxide from eggshell applied in the residual oil transesterification
Bibliographic record
Abstract
Abstract Currently, biodiesel is pointed out worldwide as the main alternative in the complementation and substitution of petrochemical diesel. However, the current industrial route of synthesis of this biofuel depends on the cost of raw materials (which are also destined for food purposes) and the expense of the production process. Aiming to remedy this obstacle, the use of solid, sustainable, low‐cost, efficient, and reusable catalysts in residual raw materials, such as waste cooking oils, has been highlighted as a promising alternative. This work focused on studying the influence of the glycerin content used in the preparation by wet impregnation of catalyst calcium diglyceroxide in the efficiency of transesterification of waste cooking oil. The catalyst was synthesized from CaO from chicken eggshell, raw glycerin co‐product from biodiesel, and methanol. The transesterification reactions were performed using 120 g of frying residual oil, methanol: oil molar rate of 6:1, constant shaking, and reaction temperature of 63 ± 1°C for 180 min. The catalyst material synthesized with residual glycerin was active for four reactions (without reactivation of its sites) with high percentages of efficiency of 96.13, 96.85, 95.93, and 91.65, respectively. It was noted that the glycerol purity correlated with changes in the structural morphology of the final compound, as well as changes in the leaching rate, acidity index, water content, and ester content of the blends. It was found that adding 15% water to the lipid material correlated with an increase in ester content (99%) in the synthesized biodiesel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".