An interferometric method to visualize and quantify nanofluid stability
Bibliographic record
Abstract
• Interferometric method to assess the stability of transparent nanofluids. • Nonuniformity of Al 2 O 3 -water nanofluid concentration is visualized and quantified. • Small number of larger particles was a cause of nanofluid instability. • Raises concerns about concentration errors in optical heat transfer studies. Establishing the stability of nanofluids is essential in both laboratory and industrial settings. High stability over time is needed to ensure that the suspensions retain their enhanced properties and provide reliable long-term performance. In the current work, a new optical method is proposed for visualizing and quantifying the stability of transparent nanofluids. The time variation of the concentration distribution and the local concentration gradients have been measured in an Al 2 O 3 -water nanofluid (ϕ=0.16 wt.%) using a Mach-Zehnder interferometer. A nanofluid prepared using standard two-step methods was found to be unstable over a short time interval, despite having a high zeta potential (43.7 mV). The concentration distribution was predicted using a simple gravitational settling model based on Stokes’ flow combined with particle size distribution measurements from dynamic light scattering (DLS). The results indicate that one of the main causes of the sedimentation instability was the presence of a small number of larger particles, which were detected using DLS analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".