Investigation of the interaction between nanoparticles, asphaltenes, and silica surfaces by real‐time quartz crystal microbalance with dissipation
Bibliographic record
Abstract
Abstract The effects of nanofluids as wettability alternators and inhibitors of asphaltene precipitation and formation damage were studied in this work. Silicate‐based nanoparticles with different chemical surfaces, named neutral (NN), basic (BN), and acidic (AN), dispersed in NaCl brine were tested to observe their interactions with n‐C7 asphaltenes and silica surfaces using the quartz crystal microbalance with dissipation (QCM‐D). The properties of nanoparticles were characterized using XRD, BET, TPD, and HRTEM. Heptol 70 asphaltenes, pre‐adsorbed/deposited on SiO2 sensors were used for studying the concentration effect of 10 nm‐sized BN‐based nanofluids, which exhibited a decreasing trend in frequency shift in the following order 1 > 10 > 25 mg/L. For toluene asphaltenes, the frequency shifts in BN nanofluids changed with the following order of concentration 100 > 150 > 50 > 25 mg/L. The effect of particle size on frequency shift, tested for toluene asphaltenes demonstrated the following order 10 > 99 > 45 > 20 nm BN. A cycle injection test between asphaltenes and a nanofluid solution was performed to evaluate the effect of the nanoparticles in a sequence injection. Wettability alteration was assessed before and after nanofluid injection using contact angle measurements, which resulted in a decrease after nanofluid injection. In addition, atomic force microscopy (AFM) measurements were performed on some of the samples to support the findings. The QSense data analysis software Q‐Tools was used to determine the thickness of the layer before and after the injection of nanofluids; the trend was similar to the change in frequency for all parameters. Finally, brine‐based nanofluids with 10 nm‐sized BN at 1 and 100 mg/L were more effective in treating the deposited asphaltenes in heptol 70 and toluene, respectively.
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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 | 0.000 |
| 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".