Preparation, characterization, and analysis of multi-walled carbon nanotube-based nanofluid: an aggregate based interpretation
Bibliographic record
Abstract
Nanofluids are gaining attention as an attractive solution for the sustainable machining of difficult-to-cut materials. Despite the enormous recent work in the literature, there are still contradictions concerning the effect of different preparation factors on the characteristics of nanofluids and the underlying mechanisms governing them. In the present study, the effect of varying the preparation factors, namely, multi-walled carbon nanotube (MWCNT) concentration, sonication time, and surfactant amount on various nanofluid characteristics and the interactions among these characteristics were studied. The characteristics are divided into two categories: (a) dispersion/stability and (b) viscosity/wettability. The analysis showed strong interactions between these two categories which were mainly attributed to aggregates' formation and dynamics. For the stability/dispersion responses, the effect of aggregation and saturation phenomena is discussed in relation to the different preparation factors. Our analysis shows that the nanofluid viscosity is strongly dependent on aggregate morphology. As for wettability, a novel mechanism is proposed and used to explain the nanoparticles' influence on wettability based on the nanolayering theory. Finally, multi objective optimization (MOO) based on grey relational analysis (GRA) was performed. It was found that moderate MWCNT concentration, high sonication time, and low surfactant amount show the optimal characteristics within the current study design variables search domain. The novelty in the present study lies in its consideration of the simultaneous interaction between the nanofluids' properties and stability. Unlike the common practice in the literature, which focuses on one or two aspects of nanofluids, our approach broadens the analysis and provides in-depth insights into the nanofluid as a complete physical system.
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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 | 0.001 | 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".