Heat‐transfer and hydrodynamic performance investigation of graphene‐titanium dioxide composite nanofluid in micro‐heat exchangers
Bibliographic record
Abstract
Abstract Nanofluids have made a breakthrough contribution toward maximizing the efficiency of heat exchangers. Consequently, graphene nanofluids have attracted significant attention, as they yield the best heat‐transfer enhancement among all nanofluids; however, graphene is highly expensive, and further studies on hybrid graphene nanofluids are required to optimize costs. Research has been performed on the performance of pure graphene nanofluids, though little has been conducted on hybrid graphene nanofluids. Therefore, we investigated the hydrodynamic and convective heat‐transfer performance of graphene‐titanium dioxide composite (GTNC) nanofluid in a micro‐heat exchanger, and compared the results with the performance of pure graphene and pure titanium dioxide nanofluids under similar conditions. Herein, we report the synthesis of GTNC using our novel green micro‐synthesis technique and the preparation of graphene, titanium dioxide, and GTNC nanofluids using a two‐step method at three concentrations (0.02~0.08 wt%) using a surfactant. Furthermore, the stability and thermo‐physical properties of nanofluids were investigated, and nanofluids were studied for their effect on the performance of a counter‐current laminar flow micro‐heat exchanger at different flow rates (Re = 750‐1460). Findings show that thermal conductivity enhancement of GTNC nanofluid and pure graphene nanofluid at the highest mass fraction (0.08%) was 25.8% and 31.6%, respectively, while the maximum enhancement of convective heat‐transfer coefficient was 64.6% and 87%, respectively. These results indicate that hybrid GTNC nanofluid showed proximate thermal performance and stability level to graphene nanofluid with 50% less graphene content, which improves the economics of the process.
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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 | 0.000 | 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 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".