Thermal Characteristics and Dryer Performance Analysis of Double Pass Solar Collector Powered by Copper and Iron Oxide
Bibliographic record
Abstract
Abstract Solar renewable energy is prospective for various engineering applications including heat exchanger and air dryer applications. The drying of agricultural products is influenced by the weather, which can limit their dryness on cloudy days and reduce drying efficiency, often necessitating additional measures to complete the drying process. This research aims to enrich the functional characteristics of a double-pass solar collector configured with a dryer unit for drying agriculture products, namely, potato chips, banana chips, and red chilies. The solar collector features a hybrid black paint coating prepared by mixing copper oxide (CuO) and iron oxide (Fe3O4) via a spray pyrolysis route with 0.3 µm thickness. The effect of hybrid coating on air temperature, energy input, thermal efficiency, drying rate, moisture ratio, and exergy efficiency of the solar-coupled dryer was estimated and compared with non-coating conditions. The result of hybrid nano-enhanced coating shows superior thermal performance and dryer performance than other coating conditions. The peak air temperature, energy input, and average efficiency are about 66.5 °C, 359.7 W, and 69.7%, respectively. Furthermore, the red chilies show a better average drying rate, moisture ratio, and exergy efficiency of about 0.81 kg/h, 0.39, and 8.4%, respectively.
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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".