Advances in Enhancing the Photothermal Performance of Nanofluid-Based Direct Absorption Solar Collectors
Bibliographic record
Abstract
The integration of nanofluids into solar collectors has gained increasing attention due to their potential to enhance heat transfer and support the transition toward low-carbon energy systems. However, a systematic understanding of their photothermal performance under the direct absorption mode remains lacking. This review addresses this gap by critically analyzing the role of nanofluids in solar energy harvesting, with a particular focus on the direct absorption mechanisms. Nanofluids enhance solar radiation absorption through improved light absorption by nanoparticles, surface plasmon resonance in metals, and enhanced heat conduction and scattering effects. The novelty of this work lies in its comparative evaluation of advanced nanofluids, including magnetic nanofluids, plasma nanofluids, and nanophase change slurries, highlighting their unique capabilities in flow manipulation, thermal storage, and optical energy capture. Future research directions are identified, such as the life cycle assessment (LCA) of nanofluids in solar systems, applications of hybrid nanofluids, development of predictive models for nanofluid properties, optimization of nanofluid performance, and integration of Direct Absorption Solar Collectors (DASCs). In addition, challenges related to the stability, production cost, and toxicity of nanofluids are critically analyzed and discussed for practical applications. This paper offers guidance for the design and application of high-performance nanofluids in next-generation solar energy systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".