Spectrum splitting through CuS–ZnO/water hybrid nanofluid for agricultural greenhouse cooling applications: An experimental study
Bibliographic record
Abstract
Abstract In the present work, CuS–ZnO/water hybrid nanofluids (in concentrations of 0.0025 mass% and 0.005 mass%) are synthesized using a two-step method with nanoparticles composition of 95% CuS and 5% ZnO. The optically tuned nanofluid filter on the agricultural greenhouse roof can reduce the cooling load by transmitting the visible spectrum and absorbing the near-infrared radiation in the solar spectrum. The size distribution of nanoparticles, stability and optical transmission of both concentrations in the visible and near-infrared regions are examined. Two hollow containers (i.e., ducts) with thicknesses of 4 mm and 8 mm are prepared. Each of these ducts is attached to a greenhouse unit and placed in front of a solar simulator. The experimental results reveal that applying CuS–ZnO nanofluid reduces the inside temperature of the greenhouse unit under all irradiance and ambient temperature ranges. The cooling system gains an average of 27.4% less heat from the greenhouse unit when the CuS–ZnO nanofluid flows through an 8 mm duct compared to no-fluid case (empty duct). The photothermal conversion efficiency of nanofluid is found to be higher than the one for water. The crop growth factor of 82.2% is obtained for 8 mm duct case, and the photosynthetic photon flux density inside the greenhouse unit is reduced without affecting the growth of many plants. Furthermore, the payback period of the nanofluid system (with 8 mm duct) is calculated as 0.42 years, and the application of optically tuned nanofluid can help reduce the cooling system's size and energy requirement for cooling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".