Developing Quantum Dots and Plasmonic Nanostructures for Solar Cell Applications
Bibliographic record
Abstract
I present our recent work on two types of nanomaterials, near-infrared (NIR) quantum dots (QDs) and plasmonic nanostructures, developed for energy-related applications (including solar cells) [1-10]. Harvesting NIR photons represents an attractive approach to improve the energy conversion efficiency of photovoltaics. Most solar cells are only able to strongly absorb "visible" photons, while leaving great amounts of solar energy in the NIR untapped. To harvest the lost NIR photons, PbS QDs and PbS@CdS core-shell QDs are very promising. Herein I present some of our most recent development in these QDs and their application in photovoltaics. In particular, we have recently developed a two-step route for the synthesis of high-quality PbS@CdS core-shell QDs with tunable shell thicknesses and absorption properties. These core-shell QDs show higher photoluminescence efficiency and significantly improved photo- and thermal-stability compared to uncoated PbS QDs only capped with ligands. The CdS shell provides protection to the PbS core and also passivates its surface defects, that may otherwise serve as charge carrier trap sites. The charge transfer behavior of these core-shell QDs has further been studied for different core sizes and shell thicknesses. As for QD-based solar cells, one example is about the controlled hybridization of NIR PbS QDs with carbon nanotubes (CNTs) and their further integration into poly(3-hexylthiophene), which is a hole-transporting polymer. The nanohybrid cells show considerably enhanced power conversion efficiency, which is attributed to the significantly extended absorption in NIR by PbS QDs and the effectively enhanced charge transportation due to CNTs. Solution processed heterojunction inorganic solar cells based on TiO2 nanorods and PbS QDs or PbS@CdS core-shell QDs will be also be briefly introduced. On the other hand, plasmonic nanostructures have recently been explored for enhancing the efficiency of solar cells. Our recent work on some interesting plasmonic nanostructures (such as Ag nanorice and nanocarrots) that have strong resonances in the NIR regime, and their application in solar cells will also be briefly highlighted. References (selected) 1. D. Wang, J. K. Baral, H. Zhao, B. A. Gonfa, V. V. Truong, M. A. El Khakani, R. Izquierdo, D. Ma, Adv. Funct. Mater , 21, 4010 (2011). 2. H. Liang, D. Rossouw, H. Zhao, S. K. Cushing, H. Shi, A. Korinek, H. Xu, F. Rosei, W. Wang, N. Wu, G. A. Botton, D. Ma, J. Am. Chem. Soc .,135, 9616 (2013). 3. H. Zhao, H. Liang, B. A. Gonfa, M. Chaker, T. Ozaki, P. Tijssen, F. Vidal, and D. Ma, Nanoscale , 2014, accepted (Back Cover). 4. I. Ka, V. Le Borgne, D. Ma, M. A. El Khakani, Adv. Mater . 24, 6289 (2012); Frontispiece Cover. 5. H. Zhao, M. Chaker, N. Wu, D. Ma, J. Mater. Chem ., 21, 8898 (2011); highlighted at http://blogs.rsc.org/jm/. 6. H. Zhao, D. Wang, T. Zhang, M. Chaker, D. Ma, Chem. Commun . 46, 5301 (2010). 7. D. Wang, H. Zhao, N. Wu, M. A. El Khakani, D. Ma, J. Phys. Chem. Lett . 1, 1030 (2010). 8. F. Ren, H. Zhao, F. Vetrone, D. Ma, Nanoscale,5, 7800 (2013). 9. H. Liang, H. Zhao, D. Rossouw, W. Wang, H. Xu, G. A. Botton, D. Ma, Chem. Mater . 24, 2339 (2012). 10. B. A. Gonfa, H. Zhao, J. Li, J. Qiu, M. Saidani, S. Zhang, R. Izquierdo, N. Wu, M. A. El Khakani, D. Ma, S olar Energy Materials and Solar Cells, 2014, In press.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".