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Record W1983567566 · doi:10.1115/icnmm2009-82206

On the Road for a Mechanism of Thermal Conductance Enhancement in Nano-Fluids: Part I—General Introduction to the Chemical Actuator Mechanism

2009· article· en· W1983567566 on OpenAlexaff
Mahmoud R. Reda

Bibliographic record

VenueASME 2009 7th International Conference on Nanochannels, Microchannels, and Minichannels · 2009
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHeat transferBrownian motionNano-MechanicsThermodynamicsConvective heat transferConvectionNanofluidicsMaterials scienceNanofluidChemical physicsThermal conductivityThermalParticle (ecology)NanotechnologyChemistryPhysicsComposite material

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.248
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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