Enhanced coupling of perovskites with semiconductive properties by tuning multi-modal optically active nanostructured set-ups for photonics, photovoltaics and energy applications
Bibliographic record
Abstract
This review describes the coupling of semiconducting materials with perovskites as main optically active elements for enhancing the performance depending on the optical set-up and coupling phenomena. The various uses of semiconductor nanoparticles and related nanomaterials for energy conduction and harvesting are discussed. Thus, it was obtained different materials highlighting the properties of perovskites incorporated within heterojunctions and hybrid nanomaterials where varied materials and sources were joined. Different multi-layered substrates are reported, and different strategies for improved electron and energy transfer and harvesting are elucidated Further, enhanced coupling of semiconductive properties for the above-mentioned processes is discussed. In this regard, various nanomaterials and their properties for improving energy applications such as solar cells are demonstrated. Moreover, the incorporation of plasmonic properties from different noble metal sources and pseudo-electromagnetic properties from graphene and carbon allotropes is discussed. Since variations in electromagnetic fields affect the semiconductive properties, it leads to varying effects and potential applications within the energy research field. Hence, this review could guide the development within energy research fields as nanophotonics, photovoltaics, and energy. This review is mainly focused on the development of solar energy cells by incorporating perovskites with varied hybrid nanomaterials, photonic materials, and metamaterials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".