Critical Review of the Novel Applications and Uses of Nanofluids
Bibliographic record
Abstract
Nanofluids — one simple product of the emerging world nanotechnology — where nanoparticles (nominally 1–100 nm in size) are mixed with conventional base fluids (water, oils, glycols, etc.). Nanofluids have seen enormous growth in popularity since they were proposed by Choi in 1995 [1]. In the year 2010 alone there were nearly 500 research articles where the term nanofluid was used in the title, showing rapid growth from 2000 (12) and 2005 (78). Much of the first decade of nanofluid research was focused on measuring and modeling fundamental thermophysical properties of nanofluids (thermal conductivity, density, viscosity, convection coefficients). Recent research, however, has started to highlight how nanofluids might perform in a wide variety of other applications. These applications range from their use in nanomedicine [2] to their use as solar energy harvesting media [3]. By analyzing the available body of research to date, this article presents trends of where nanofluid research is headed and suggests which applications may benefit the most from employing nanofluids. Overall, this review summarizes the novel applications and uses of nanofluids while setting the stage for future nanofluid use in industry.
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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 | 0.000 | 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 | 0.000 |
| 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 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".