Bibliographic record
Abstract
Solar cells are receiving a lot of attention due to the ongoing climate debate and attempts to implement more green energy sources to reduce the CO 2 emissions in the atmosphere.Single crystalline silicon (c-Si) solar cells with better stability over a long time period compared to other silicon based solar cells have taken a main position in the solar cell market.As new solar cell technology continually develops, the disadvantage of the relatively high cost of electricity generated related to low cell efficiency becomes one of top issues with c-Si solar cells.The purpose of this thesis is to investigate the effectiveness of nanotechnologies, in particular plasmonic metal nanoparticles (MNPs), black silicon (b-Si) nanostructures and horizontally-grown silicon nanowires (SiNWs) structures, for cell efficiency enhancement of c-Si solar cells through numerical simulation and experimental demonstrations.Due to the advantage of c-Si solar cells on the material cost, the nanotechnologies enable lower cost compared to the other types solar cell technologies with the same efficiency enhancement.We first investigate the optical and the electrical properties of MNPs for c-Si solar cell applications based on the finite difference time domain method.Simulations are performed to optimize the MNPs in terms of shape, size, surface coverage, and dielectric environment.Silver nanocubes with a silicon dioxide (SiO 2 ) sublayer are experimentally demonstrated to obtain up to an average of 7% cell efficiency enhancement.To the best of our knowledge, our group was the first group that utilizes the cubic silver MNPs on the c-Si solar cells with experimental demonstrations.Next, we investigate the cell efficiency enhancement due to the reduction of surface reflection from nanostructures.Two techniques are discussed, (i) the growth of black iii silicon and (ii) the horizontally-grown SiNWs structures.The b-Si structures are first optimized in terms of shape, size, and aspect ratio, and their effect on surface reflectivity is characterized.The needle-shaped b-Si structures are shown in experiment to obtain near-zero specular reflectivity and average 1.9% diffused reflectivity.Similarly, the horizontally-grown SiNWs structures grown by the vapor-liquid-solid (VLS) process with a 4-hour growing time have shown to reduce reflectivity from 45% to 25% at 400 nm wavelengths.The fabrication procedure and experimental measurement results of SiNWs structure on c-Si substrates are explained in detail.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".