Prediction and Optimization of Thermal Conductivity and Viscosity of Stable Plasmonic TiN Nanofluid Using Response Surface Method For Solar Thermal Application
Bibliographic record
Abstract
<title>Abstract</title> Nanofluids open a new dimension in solar thermal applications due to their enormous thermophysical properties. The preparation of stable, efficient, and low-cost nanofluids is an emerging area of research. According to NIMS (National Institute of Material Science) research, Titanium nitride (TiN) nanoparticles have localized surface plasmon resonance properties. It enables a superior photoabsorption feature. Titanium nitride (TiN) particles of 40–50 nm sizes were selected to prepare distilled water-based nanofluid at a 0-0.1% volume concentration range. The Thermal conductivity and viscosity of TiN nanofluids and base fluid are measured experimentally at temperatures 30℃ to 55℃. Determination of thermal conductivity and viscosity of nanofluid through experimentation is cumbersome. The present study deals with thermal conductivity and viscosity modeling of water-based stable plasmonic TiN nanofluid using the surface response method. ANOVA is used to determine the significance of input variables and their interaction. The performance of both predictive models was measured in terms of correlation coefficient (R<sup>2</sup>) and mean square error (MSE) to acknowledge the best fit. The surface response method optimizes process parameters using reliable and efficient model results for maximum heat transfer enhancement. The maximum thermal conductivity (0.8848 W/mK) and minimum viscosity (0.7822 cP) obtained at 55℃ and 0.0535% volume concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| 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 teacher head, 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".