Wetting behavior of multi-walled carbon nanotube nanofluids
Bibliographic record
Abstract
Nanofluids-engineered colloidal suspensions in base liquids-have captivated the interest of researchers over the last two decades for various existing as well as emerging technological applications. The main impetus for the synthesis of such novel nanocomposite liquids is the potential to alter properties of the base liquid, such as its viscosity, thermal conductivity, and surface tension, and to introduce specific optical and magnetic properties. Numerous studies suggest trends and explanations for the effects associated with the addition of nanoparticles, and that deviation from the base liquid properties are dependent on nanoparticle concentration. However, there remains a certain ambiguity in the available literature. The wetting behavior and surface tension of nanofluids are particular examples where highly conflicting results exist. In this study, we used multi-walled carbon nanotubes (MWCNTs) functionalized by plasma treatment and dispersed in reverse osmosis water and 99% anhydrous ethanol. Our observations reveal that the surface tension and wetting behavior of the stable aqueous and ethanol-based nanofluids containing plasma functionalized MWCNTs are unaffected by the MWCNT loading up to 120 (0.012) and ∼210 (0.021) ppm (vol%), respectively. The ethanol-based MWCNT nanofluids allowed us to extend the study to higher loadings, and a linear increase of the surface tension past ∼200 ppm was observed. Conversely, nanofluids containing non-functionalized or surfactant-stabilized MWCNTs show drastically different contact angle values when compared to the base liquids even at very low concentrations (less than 100 ppm). We demonstrate that the stability of nanofluid and method of stabilization are crucial parameters in determining the wetting behavior of nanofluids.
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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".