Continuous Synthesis and in Situ Monitoring of Biodiesel Production in Different Microfluidic Devices
Bibliographic record
Abstract
Currently, there is great interest in developing new processes for continuous biodiesel synthesis in order to overcome problems imposed by biphasic reaction and thermodynamic equilibrium, as well as to reduce production costs related to the conventional batch reaction processes. The use of microreactors can significantly improve the mixing between reactants and phases, enhancing the transfer rates, and, consequently, increasing biodiesel yields. Therefore, in this work, microreactors with different internal geometries have been fabricated and used for continuous production of biodiesel from castor oil and ethanol. The influences of the microchannel geometry (Tesla-, omega-, and T-shaped) on the performance of the biodiesel synthesis were experimentally studied. Higher biodiesel yields were reached using the Tesla- and omega-shaped microchannels than during the T-shaped microchannels due to better mixture mechanism efficiency. Using a catalyst loading of 1.0 wt % NaOH and a reaction temperature of 50 °C, ethyl ester conversions of 96.7, 95.3, and 93.5% were achieved using Tesla-, omega-, and T-shaped microreactors, respectively. In addition, transesterification reaction in situ monitoring by near-infrared spectroscopy using a fiber-optic probe was evaluated, showing that more studies must be performed in order to allow its use in online monitoring of continuous processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".