Small particles, big impacts: A review of the diverse applications of nanofluids
Bibliographic record
Abstract
Nanofluids—a simple product of the emerging world of nanotechnology—are suspensions of nanoparticles (nominally 1–100 nm in size) in conventional base fluids such as water, oils, or glycols. Nanofluids have seen enormous growth in popularity since they were proposed by Choi in 1995. In the year 2011 alone, there were nearly 700 research articles where the term nanofluid was used in the title, showing rapid growth from 2006 (175) and 2001 (10). The first decade of nanofluid research was primarily focused on measuring and modeling fundamental thermophysical properties of nanofluids (thermal conductivity, density, viscosity, heat transfer coefficient). Recent research, however, explores the performance of nanofluids in a wide variety of other applications. Analyzing the available body of research to date, this article presents recent trends and future possibilities for nanofluids research and suggests which applications will see the most significant improvement from employing nanofluids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".