Photothermal Convergence Performance of Mono and Hybrid Nanofluids in Solar Systems
Bibliographic record
Abstract
The global warming associated with fossil fuels have placed a concerted effort on exploiting other forms of energy resources that are sustainable, environmentally friendly, and abundantly available.Solar energy can be easily harvested and primarily converted into electrical and thermal energy forms.The efficient harvesting of direct solar energy is a key element in maximizing the utilization of solar energy.As working fluids, nanofluids are shown to exhibit outstanding photothermal conversion in the harvesting of direct solar energy.The paper explores experimental data from various research studies conducted on the photothermal conversion of nanofluids in direct absorption solar systems, and presents a comparison in the performance of mono and hybrid nanofluids.Hybrid nanofluids are shown to exhibit much greater thermal performance than mono nanofluids due to their synergistic thermal properties.Certain hybrid nanoparticles are shown to manifest captivating results for their photothermal conversion efficiency enhancements.
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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) | 0.001 | 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".