INVESTIGATION AND CHARACTERIZATION OF ELEMENTAL COMPOSITION OF SOLAR PANEL
Bibliographic record
Abstract
This research focused on the investigation and characterization of the elemental composition of solar panels available in Nigeria, with a view to making recommendations for the indigenous production of solar panels using locally sourced materials as semiconductors. The ZEISS Ultra Plus Scanning Electron Microscope (SEM) machine was used to obtain the structural pattern of the samples. In order to carry out the elemental composition of the samples, EDX analysis was performed using the Energy Dispersive X-ray (EDX) machine, and the result was measured in atomic weight %. The result showed that the elements present in the solar panel from China were evenly distributed, the same as the elements present in the panel from Canada, while the elements present in the panel from Germany were not evenly distributed. In sample A (China), the number of elements present was Silicon (45.00%), Oxygen (34.30%), Sodium (16.20%), and Aluminum (4.50%); in sample B (Canada), Silicon (50.43%), Oxygen (39.90%), Zinc (4.80%), and Aluminum (4.87%) were present; In comparison, in sample C (Germany) with Silicon (50.42%), Oxygen (39.90%), Nickel (4.83%), and Aluminum (4.85%) were present. Silicon is the main element present as it is the semiconductor material used. It was used because of its semiconducting properties, ability to release electrons when exposed to sunlight (either monocrystalline or polycrystalline), and stability and reliability. Other elements like aluminum and silver form metal contact on the solar cell's surface, and they serve as catalysts that facilitate the flow of electrons. Thus, this study has laid the foundation for elements available in solar panels to guide indigenous researchers and business ventures interested in the elemental constituents of solar panels for business and production purposes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".