Asphaltenes yield curve measurements on a microfluidic platform
Bibliographic record
Abstract
We describe a microfluidic apparatus and method for performing asphaltene yield measurements on crude oil samples. Optical spectroscopy measurements are combined with a microfluidic fluid handling platform to create an automated microfluidic apparatus to measure the asphaltene yield. The microfluidic measurements show good agreement with conventional wet chemistry measurements as well as available models. The initial absorbance of the oil is measured, and asphaltenes are removed from the oil by the gradual addition of n-alkane, which leads to flocculation and subsequent filtration. The absorbance of the de-asphalted oil (maltenes) is then measured and the initial asphaltene content is determined by the change in absorbance. The solubility of asphaltene is evaluated by varying the titrant-to-oil ratio (e.g., n-heptane-oil), which induces no, partial, or full precipitation of asphaltenes depending on the chosen ratio. The absorbance of the filtrate is measured and normalized to the maximum content to determine the fractional precipitation at each ratio. Traditionally, a yield curve comprised of 20 such ratios would require weeks to months to generate, while consuming over 6 L of solvent and more than 100 g of crude oil sample. Using the microfluidic approach described here, the same measurement can be performed in 1 day, with 0.5 L of solvent and 10 g of crude oil sample. The substantial reduction in time and consumables will enable more frequent asphaltene yield measurements and reduce its environmental impact significantly.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".