Effective inhibition of crystal growth during wax deposition using <scp>CTAB</scp>‐grafted faujasite nanoparticles
Bibliographic record
Abstract
Abstract In this work, we developed cetrimonium bromide (CTAB)‐grafted faujasite nanoparticles (C‐g‐F) as wax inhibitors for the deposits that were formed from a model oil system consisting of 5 wt.% paraffin wax in toluene. Our developed nanomaterials were fully characterized, hydrophobically modified, and their deposition inhibition performance compared with virgin CTAB was investigated in a model waxy oil through cold finger setup (CF), differential scanning calorimetry (DSC), cold flow yield strength, and cross‐polarized microscopy (CPM). Applying our nano‐inhibitors at a concentration of 100 ppm led to more than 50 wt.% inhibition and a 30% reduction of deposit cold yield strength. Interestingly, the presence of FAU nanoparticles in the core structure of C‐g‐F contributed to better dispersibility, enhancing surface wettability (reducing interfacial tension with wax crystals), facilitating inhibitions, and preventing co‐crystallization greater than the surfactant alone. The morphological analysis results showed that C‐g‐F, due to their novel surface features, could suppress the unordered wax crystals from networking, resulting in uniform spherical‐like wax aggregates. Hence, the application of tiny concentrations of C‐g‐F (10 to 300 ppm) outperformed stand‐alone surfactants in nucleating the wax crystals.
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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) | 0.000 | 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".