On the Road for a Mechanism of Thermal Conductance Enhancement in Nano-Fluids: Part I—General Introduction to the Chemical Actuator Mechanism
Bibliographic record
Abstract
It was concluded after reviewing the literature that the existing medium theory (Maxwell model) cannot explain this anomalous increase of thermal conductivity of nano-particles that are suspended in fluid (nano-fluids). This new mechanism restricts the definition of nano-particles as particle that perfectly obeys the Gibbs rule of thermodynamics and act as a chemical actuator during heat transfer. This driving force for the chemical actuator is the microstructure phase change of the passive layer during heat transfer. The new mechanism require the presence of nano-convection (Brownian motion) in the vicinity of nano-particles surface, explain temperature and particles size dependency, require the presence of a shell or a sheath where chemical interaction occurs between the surface and the fluid and many others. The high rate of heat transfer of the nano-fluid is due to first, the bubbles formations inside the passive layer and their departure from grain boundaries and second due to nano-convection generated in the vicinity of the nano-particles of the nano-particles as result of the chemical actuation of the nano-particles. This nano-convection produces Brownian motion which increase rate of heat transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".