Characterization, optimization, and performance evaluation of PCM with Al2O3 and ZnO hybrid nanoparticles for photovoltaic thermal energy storage
Bibliographic record
Abstract
The electrical efficiency of the photovoltaic (PV) panel is affected significantly with increased cell temperature. Among various approaches, the use of Phase Change Materials (PCMs) with nanoparticles is currently one of the most effective for reducing and managing the temperature of PV panels. In this study, paraffin wax as PCM with different loading levels (0.5%, 1%, and 2%) of hybrid nanoparticles Al2O3 and ZnO were successfully synthesized and their effects on the performance of the Photovoltaic-Thermal (PVT) system were investigated experimentally. Additionally, a prediction model was developed to analyze the interaction between the operating factors (independent variable) and response factors (dependent variable) of the PVT/PCM and PVT with Hybrid nano-PCM (PVT/HNPCM) systems based on response surface methodology (RSM). Experimental results showed that compared to only PCM, the thermal conductivity of HNPCM increased by 24.68%, 28.57%, and 41.56% for the inclusion of 0.5%, 1%, and 2% hybrid nanomaterial respectively. The electrical efficiency of the PVT/HNPCM, and PVT/PCM system enhanced by 31.46% and 28.70% respectively compared to the conventional PV system in this study. With a cooling-water mass flow rate of 0.0021 kg/s, the highest thermal efficiency of 47% was achieved for the PVT/PCM system, whereas 51.28% was achieved for the PVT/HNPCM system. The analysis of the variance test yielded a P value <0.0001 which is less than 0.05 for the model of overall efficiency for PVT/PCM and PVT/HNPCM system, indicating the suggested model's appropriateness and statistical significance. These optimal conditions are observed when the solar intensity ranges from 774 W/m2 to 809 W/m2 and the mass flow rate is 0.002 kg/s for both the PVT/PCM and PVT/HNPCM systems. However, these systems advance sustainable urban development and climate goals by combining PV panels' electrical generation with thermal energy harvesting, boosting overall energy efficiency in the built environment.
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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".