Interferometric assessment of methods to improve the stability of Al2O3-water nanofluids
Bibliographic record
Abstract
Enhancing the stability of nanofluids makes them more viable for their application in industrial and medical fields. This study investigates the stabilization of Al 2 O 3 -water nanofluids through various preparation methods, including sedimentation and centrifugation at different relative centrifugal forces (RCF) and durations, along with the addition of sodium dodecylbenzene sulfonate (SDBS) as a stabilizing agent. Stability was assessed using a newly developed interferometric method capable of visualizing and quantifying concentration distributions, supplemented by dynamic light scattering (DLS) and zeta potential measurements. The results showed that both centrifugation and SDBS addition enhance nanofluid stability, with higher RCF and longer centrifugation times yielding higher stability. A nanofluid containing 0.23 wt% Al 2 O 3 -water and 0.23 wt% SDBS was prepared, maintaining stability for 1.5 h without any measurable sedimentation. The study also revealed that while DLS and zeta potential measurements offer useful insights into nanofluid stability, they are not dependable as standalone indicators of stability. • Interferometry effectively assesses methods for improving nanofluid stability. • Centrifugation and SDBS addition improve Al 2 O 3 -water nanofluid stability. • Higher RCF and centrifugation time enhance nanofluid stability. • Centrifuged 0.23 wt% Al 2 O 3 nanofluid with SDBS remained stable for 1.5 h. • DLS and zeta potential are supplementary but not standalone stability indicators.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".