(Invited) Designing Hybrid Nanostructures for Enhancing Photon Harvest in Photocatalysis
Bibliographic record
Abstract
Harvesting photons at longer wavelengths in the visible and near infrared (NIR) ranges represents an attractive approach to improve the power conversion efficiency of photocatalysis and photovoltaics. Plasmonic and upconverting nanostructures are promising in boosting and broadening photon harvesting and have been recently explored for enhancing the efficiency of solar cells and photocatalysis. Herein, I will present some of our most recent development in plasmonic and upconverting nanostructures and their applications in photocatalysis and solar cells and [1-5]. One example is shown in Figure 1. It illustrates the synthesis of novel, plasmonic Au nanoparticle decorated NaYF4:Yb3+, Er3+, Tm3+ (denoted as NYF) – core @ porous-TiO2-shell microspheres, which can harvest solar photons over a wide spectral range from ultraviolet to NIR for efficient photocatalysis, significantly better than the benchmark Degussa P25 [1]. The enhanced activity is attributed to synergistic effects from nanocomponents arranged into the nanostructured architecture in such a way that favours the efficient charge/energy transfer among nanocomponents and largely reduced charge recombination. Recently, an even simple method was developed for combing Au nanoparticles and NYF microspheres with two-dimensional graphitic C3N4 (g-C3N4) nanosheets (Au-NYF/g-C3N4) [2]. The simple one-step synthesis of NYF in the presence of g-C3N4, which was not previously reported in the literature, leads to both high NYF yield and high coupling efficiency between NYF and g-C3N4. The Au-NYF/g-C3N4 structure exhibits excellent stability, wide photoresponse from the ultraviolet, to visible and NIR regions, and prominently enhanced photocatalytic activities compared with the plain g-C3N4 sample in the degradation of methyl orange. Very recently, by constructing a complete comparative framework, based on the similar catalysts having alloy synergistic effect or plasmonic effect, or both, we compared the plasmonic and synergistic effects. It helps answers an important, yet not previously addressed question: synergistic and plasmonic effects, which can make more important contribution to photocatalysis? On the other hand, quantum dots are also promising for photon harvesting because of their size-tunable bandgaps, even in the NIR range. I may briefly introduce the use of quantum dots in photocatalysis and solar cells [6]. References [1] Z. Xu, M. Quintanilla, F. Vetrone, A. O. Govorov, M. Chaker and D. Ma*, Adv. Funct. Mat., 2015, 25, 2950 ( Front Cover ). [2] Q. Zhang, J. Deng, Z. Xu, M. Chaker, D. Ma, ACS Catalysis, 2017, 7, 6225. [3] Z. Xu, Md G. Kibria, B. AlOtaibi, P. N. Duchesned, L. V. Besteiro, Y. Gao, Q. Zhang, Z. Mi, P. Zhang, A. O. Govorov, L. Mai, M. Chaker, D. Ma, Applied Catalysis B: Environmental, 2017, in press. [4] H. Liang, D. Rossouw, H. Zhao, S. K. Cushing, H. Shi, A. Korinek, H. Xu, F. Rosei, W. Wang, N. Wu, G. A. Botton, and D. Ma,* J. Am. Chem. Soc., 2013, 135, 9616. [5] H. Liang, H. Zhao, D. Rossouw, W. Wang, H. Xu, G. A. Botton, D. Ma, Chem. Mater. 2012, 24, 2339. [6] I. Ka, B. Gonfa, V. Le Borgne, D. Ma*, M. A. El Khakani*, Adv. Funct. Mater., 2014, 24, 4042 ( Inside Back Cover ).
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".