An automated microfluidic system for the investigation of asphaltene deposition and dissolution in porous media
Bibliographic record
Abstract
Asphaltenes, among the most complex components of crude oil, vary in their molecular structure, composition, and self-assembly in porous media. This complexity makes them challenging in laboratory characterization methods. In the present work, a novel microfluidic device was designed to access in situ transient, high-fidelity information on asphaltene deposition and dissolution within porous media. The automated microfluidic device features three independent 4.5 μL packed-bed microreactors on the same chip. The deposition of asphaltenes was investigated at five different temperatures (ranging from 25-65 °C) in addition to dissociation with xylenes. Our findings demonstrate a decrease in the dispersity of asphaltene nanoaggregates in the porous media when the deposition temperature is increased. Furthermore, the direct quantification of the dissociation solvent was made possible by in situ Raman spectroscopy. The average occupancy of xylenes and xylene-free porous media (or unrecognized sites) was estimated to be 0.41 and 0.66, respectively. It was observed that asphaltenes deposited at higher deposition temperatures are more difficult to dissociate by xylenes than those deposited at lower temperatures. In order to develop efficient remediation treatments in energy production operations, the convoluted behaviours of asphaltenes in porous media must be understood on a molecular level. Automated microfluidic systems have the potential to streamline treatment designs, improve their efficiency, and enable the design of green chemistry in conventional energy production operations.
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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 | 0.001 | 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) | 0.001 | 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".