Refractive index and temperature coefficient of refractive index of Al2O3- and SiO2-water nanofluids
Bibliographic record
Abstract
Measurements are made of the refractive index and temperature coefficient of refractive index (∂n/∂T) for two nanofluids, Al 2 O 3 -water and SiO 2 -water, over the temperature range 20–40°C at a wavelength of 632.8 nm. These optical properties are needed for refractive index-based temperature and heat transfer measurements, particularly laser interferometry . A laser beam deflection technique is used to measure the refractive index of the nanofluids. The temperature coefficient of refractive index is determined by measuring the phase change with temperature, using Mach-Zehnder interferometry . Measurements are made for Al 2 O 3 -water nanofluids up to a weight-based concentration of ϕ = 20% and for SiO 2 -water nanofluids up to a weight-based concentration of ϕ = 40%. The refractive index data are used to quantify the errors in interferometric temperature measurements produced by non-homogenous nanofluid suspensions. In the literature, the optical properties of dilute water-based nanofluids are sometimes assumed to be equal to those of water for interferometry experiments. The limits of this approximation are estimated. The current measurements indicate that ∂n/∂T for Al 2 O 3 -water nanofluids can be approximated as that of water with an accuracy better than ±5% for a concentration ϕ < 1%. For SiO 2 -water nanofluid, ∂n/∂T can be approximated as that of water with an accuracy of better than ±5% for a concentration ϕ < 4%.
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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 | 0.001 |
| 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) | 0.001 | 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".