Using plasmonics and nanoparticles to enhance the efficiency of solar cells: review of latest technologies
Bibliographic record
Abstract
With the advances in the field of plasmonics, techniques for trapping and localizing light have become more feasible at the nanoscale. Several works have shown that plasmonics-based photovoltaic devices have yielded an improved absorption capability, enabling the design of thin-layered photovoltaic absorbers. In this review, we shed light on recent advances that employ plasmonics and nano-sized structures and thin-film technologies intended to increase solar cell efficiency. In this work, we provide an overview of the challenges associated with developing high-efficiency solar cells. Despite significant efforts by numerous groups to improve the efficiency of solar cells, practical realization of these concepts has yet to materialize. The conclusions made here hope to encourage researchers to re-examine the factors and challenges that could have created barriers to full realization of all concepts proposed over the past 15 years. In fact, because of the immense impact of improving the efficiency of solar cells on the environment and economy, it is hoped that this review encourages new technology paradigms that can be translated into commercially viable products.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".