Inorganic and Organic Nano-Emitters for Luminescent Solar Concentrators
Bibliographic record
Abstract
Generating power directly from solar radiation represents a promising opportunity towards addressing the increasing demand for clean energy, also reducing environmental impact caused by excessive carbon emissions. Luminescent solar concentrators (LSCs) are being widely studied as fluorophore-containing waveguides to harvest solar energy in combination with photovoltaic (PV) technologies, due to their low cost and straightforward synthesis by wet chemical approaches. LSCs can play an important role in the emerging building-integrated photovoltaic (BIPV) industry as they provide a low cost alternative to transparent solar cell. 1-4 Typical LSCs consist of optical waveguides doped with highly emissive fluorophores which are required to exhibit high optical efficiency and long-term stability. Among various types of luminescent particles suitable for LSCs, inorganic quantum dots (QDs) are excellent candidates because they offer several advantages, including high good quantum yield (QY), size-tunable absorption/emission, better chemical-/photo-stability and cost-effectiveness compared to traditional organic dyes.5An efficient lumiphores for LSC should meet these requirements: (a) broad light absorption (b) high photoluminescence quantum yield (PLQY), (c) large Stokes shift (absence or low overlap between absorption and emission spectra), (d)compatibility with the host matrix material and (e) low cost. By exploiting the excellent properties of QDs, such as the size-tunable absorption and emission, herein we will introduce different strategies to achieve cost-effective large-area (100cm2) LSC based on inorganic QDs.3, 6, 7 Furthermore, in order to reduce the environmental impact of such devices, it will be explored the use of carbon dots (Cdots) as organic nano-emitters for LSC. Cdots are the latest addition to the carbon nanoallotropes family and they are exclusively composed of non-toxic elements, such as nitrogen, carbon and oxygen. They can be easily synthetized in large quantities by solvothermal method. Their main advantages, compared to conventional semiconducting quantum dots (QDs), is the non-toxicity, environmental friendliness, low-cost and simple preparation using abundant carbon based feedstock.In this work different type of Cdots with different surfaces functionalization and optical features are prepared. In particular their absorption and emission spectra are tuned in the Visibile/Near infra-red range.Carefully chosen Cdots are then employed as luminophores for metal-free large-area LSC.8, 9 REFERENCES: 1. M. G. Debije and P. P. C. Verbunt, Advanced Energy Materials, 2012, 2, 12-35. 2. F. Meinardi, A. Colombo, K. A. Velizhanin, R. Simonutti, M. Lorenzon, L. Beverina, R. Viswanatha, V. I. Klimov and S. Brovelli, Nature Photonics, 2014, 8, 392-399. 3. Zhou, Y., Benetti, D., Fan, Z., Zhao, H., Ma, D., Govorov, A.O., Vomiero, A. and Rosei, F, Advanced Energy Materials, 2016, 6, 1501913. 4. M. J. Currie, J. K. Mapel, T. D. Heidel, S. Goffri and M. A. Baldo, Science, 2008, 321, 226-228. 5. F. Meinardi, H. McDaniel, F. Carulli, A. Colombo, K. A. Velizhanin, N. S. Makarov, R. Simonutti, V. I. Klimov and S. Brovelli, Nature nanotechnology, 2015, 10, 878. 6. H. Zhao, Y. Zhou, D. Benetti, D. Ma and F. Rosei, Nano Energy, 2017, 37, 214-223. 7. H. Zhao, D. Benetti, L. Jin, Y. Zhou, F. Rosei and A. Vomiero, Small, 2016, 12, 5354-5365. 8. H. Zhao, D. Benetti, X. Tong, H. Zhang, Y. Zhou, G. Liu, D. Ma, S. Sun, Z. M. Wang and Y. Wang, Nano energy, 2018, 50, 756-765. 9. Y. Zhou, D. Benetti, X. Tong, L. Jin, Z. M. Wang, D. Ma, H. Zhao and F. Rosei, Nano Energy, 2018, 44, 378-387.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".