MétaCan
Menu
Back to cohort
Record W2782524968 · doi:10.1039/c7ra10406e

Surface modification of carbon nanotubes with copper oxide nanoparticles for heat transfer enhancement of nanofluids

2018· article· en· W2782524968 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
FundersKing Abdulaziz City for Science and TechnologyKing Fahd University of Petroleum and Minerals
KeywordsNanofluidCarbon nanotubeThermal conductivitySurface modificationCopperNanoparticleMaterials scienceCopper oxideHeat transferHeat transfer enhancementChemical engineeringHeat transfer fluidNanotechnologyOxideThermalCarbon fibersComposite materialThermodynamicsComposite numberMetallurgyHeat transfer coefficient

Abstract

fetched live from OpenAlex

Over the last few years, nanoparticles have been used as thermal enhancement agents in many heat transfer based fluids to improve the thermal conductivity of the fluids. Recently, many experiments have been carried out to prepare different types of nanofluids (NFs) showing a tremendous increase in thermal conductivity of the base fluids with the addition of a small amount of nanoparticles. However, little experimental work has been proposed to calculate the flow behaviour and heat transfer of nanofluids and the exact mechanism for the increase in effective thermal conductivity in heat exchangers. This study mainly focuses on the development of nanomaterial composites by incorporating copper oxide nanoparticles (CuO) onto the surfaces of carbon nanotubes (CNTs). The CNT-CuO nanocomposite was used to prepare water-based heat transfer NFs. The morphological surfaces and loading contents of the CNT-CuO nanocomposite were characterized using field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and thermogravimetric analysis (TGA) while the physical and thermal properties of the water-based nanofluids were characterized using differential scanning calorimetry (DSC), the Mathis TCi system and a viscosity meter for measuring the heat capacity, thermal conductivity and viscosity of the synthesized NFs, respectively. The heat transfer and the pressure drop studies of the NFs were conducted by a horizontal steel tube counter-flow heat exchanger under turbulent flow conditions. The experimental results showed that the developed NFs with different concentrations of modified CNTs (0.01, 0.05 and 0.1 wt%) have yielded a significant increase in specific heat capacity (102% higher than pure water) and thermal conductivity (26% higher than pure water) even at low concentration. The results also revealed that the heat rate of the NF was higher than that of the base liquid (water) and increased with increasing the concentration of nanoparticles. Furthermore, no significant effect of the nanoparticles on the pressure drop of the system was observed.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.029
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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