Measuring Asphaltene Deposition Onset from Crude Oils Using Surface Plasmon Resonance
Bibliographic record
Abstract
We present a surface plasmon resonance (SPR) sensor for crude oils that can be used to directly measure asphaltene deposition onset. Surface plasmon resonance describes a condition in which light incident onto a highly conductive metallic film couples into resonant charge oscillations of the metal. The SPR condition is highly sensitive to slight perturbations in the dielectric environment in the immediate vicinity of the thin-film, less than 1 μm away at visible frequencies. Here, we show that shifts in the peak surface plasmon resonant wavelength can be used to measure the onset of deposition of asphaltenes from a titration experiment. Initially, the SPR peak wavelength of the neat crude oil is measured. Next, the gradual addition of n -heptane dilutes the crude oil and produces a lower SPR peak wavelength, which results from the lower refractive index of the mixture. When the amount of added n -heptane approaches the deposition onset point, asphaltenes precipitate and are deposited onto the thin-film surface. We then observe that the SPR peak wavelength increases as the deposit is formed on the sensing surface. The asphaltenes continue to deposit on the surface until a deposit thicker than the SPR field penetration depth is reached. Even though the crude oil has been substantially diluted in n -heptane, the final SPR peak wavelength exceeds that of the native crude oil due to the dense asphaltene deposit on the surface. Three crude oils were evaluated with the SPR approach for measuring asphaltene deposition onset.
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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 | 0.001 |
| 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".