Bibliographic record
Abstract
The performance of PV (photovoltaic) module is strongly dependent on its operating temperature. Most of the energy absorbed by the panel is converted to heat which is normally lost and provides no value. This work investigated experimentally the PV performance through using three separated PV panels. Three identical photovoltaic (PV) panels have been installed side by side to investigate the effect of cooling on the PV panels performance, one of them is used as a baseline ; the second is cooled using pure water; while the third is cooled using nanofluids. Aluminum Oxide (Al2O3) and Copper Oxide (CuO ) nanoparticles were added to the pure water to form the nanofluid, each one with different concentration in order to find the optimum concentration of Al2O3 and CuO. Meteorological data was measured using a weather station. Also, the temperature of the cooling fluids together with the backside temperature of the PV Panels was recorded. It was found that an increase in the efficiency of the panel of 2% was obtained when 0.4 % Al2O3 by weight was mixed with the pure water. This increase in efficiency was 2.34% when 0.6 % CuO
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".