Optical Performance Enhancement of GaAsBi/P3HT Hybrid Solar CellIncorporatingMetallic Nanoparticlesinthe Absorber Layer
Bibliographic record
Abstract
GaA<inf>s1-x</inf>Bi<inf>x</inf> alloy semiconductors have been an attractive material for optoelectronic applications due to its bandgap tailoring property with variable mole fraction “x”. The optical properties of thin film GaAs0.99Bi<inf>0.01</inf> solar cell with a thin donor shell of P3HT and ITO on the top have been investigated. In order to boost the optical absorption, photo-generation rate in thin film HSC, plasmonic metal nanoparticles are incorporated in the donor shell. A 400nm thick layer of thin film absorbing material GaAs0.99Bi<inf>0.01</inf>, 100nm thick hole transport layer of P3HT and 100nm thick ITO layer as a top electrode, 100nm thick Al bottom electrode have been chosen for this simulation study. A comparative study of the optical properties of plasmonic MNP based HSC thin film solar cell is carried out with an array of three different materials of nanoparticles such as gold(Au), silver(Ag) and Aluminium (Al). A thorough geometry optimization study is done based on the diameter of MPNs as well as Filling Ratio (FR) of the unit cell. Photo generation rate profile upon illumination by incident solar spectrum, electric field profile at different wavelengths for different MNPs are studied and presented in this paper. The 400nm thick absorber layer produces a J<inf>sc</inf> of 31.8969 mA/cm<sup>2</sup> without the plasmonic effect whereas it produces Jsc values of 34.1957 mA/cm<sup>2</sup>, 32.0325 mA/cm<sup>2</sup> and 33.5517 mA/cm<sup>2</sup> for Au, Ag and Al MNP respectively with a FR of 0.5 of unit solar cell.
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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 | 0.000 | 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 | 0.001 |
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".