Optical Performance Enhancement of GaAsBi/P3HT Hybrid Solar CellIncorporatingMetallic Nanoparticlesinthe Absorber Layer
Bibliographic record
Abstract
GaAs1-xBixalloy 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 GaAs0.99Bi0.01solar 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 GaAs0.99Bi0.01, 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 Jscof 31.8969 mA/cm2without the plasmonic effect whereas it produces Jsc values of 34.1957 mA/cm2, 32.0325 mA/cm2and 33.5517 mA/cm2for 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 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".