The DOI matches its Crossref record.
Engineering Homogeneous Doping in Single Nanoparticle To Enhance Upconversion Efficiency
Abstract
Upconversion nanoparticles (UCNPs) have shown considerable promises in many fields; however, their low upconversion efficiency is still the most serious limitation of their applications. Herein, we report for first time that the homogeneous doping approach based on the successive layer-by-layer method can greatly improve the efficiency of the UCNPs. The quantum yield as high as 0.89 ± 0.05% is realized for the homogeneous doping NaGdF4:Yb,Er/NaYF4 UCNPs, which is nearly 2 times higher than that of the heterogeneous doping NaGdF4:Yb,Er/NaYF4 UCNPs (0.47 ± 0.05%). The influences of spatial distributions and local relative concentrations of the dopants on the optical properties of UCNPs were investigated in the single particle level. It was found that heterogeneous doping indeed existed during the spontaneous growth process of the nanoparticles. The heterogeneous doping property can further induce many negative effects on the optical properties of UCNPs, especially the luminescent efficiency. The spatial distributions and local relative concentrations of the dopants can be well controlled by the successive layer-by-layer homogeneous doping method on the monolayer level and homogeneously distributed in the single particle level. Furthermore, by using homogeneous doping NaGdF4:Yb,Tm as initial core, the multicolor emission intensity of NaGdF4:Yb,Tm/NaGdF4:A (A = Tb(3+), Eu(3+)) core/shell nanoparticles can also exhibit 20%-30% improvement. We believe that such a homogeneous doping model can open the door to improve the upconversion optical properties by engineering the local distribution of the sensitizer, activator, host, etc., in a microcosmic and provide a track for engineering the high quality UCNPs with advanced nanostructure and optical properties.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
An overview of nanoparticles commonly used in fluorescent bioimaging
Wolfbeis, O. S. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00392f · Cited by 1,631
Advances in highly doped upconversion nanoparticles
Wen, S., Zhou, J., Zheng, K., Bednarkiewicz, A., Liu, X., & Jin, D. (2018) · Nature Communications · doi:10.1038/s41467-018-04813-5 · Cited by 1,116
Temporal full-colour tuning through non-steady-state upconversion
Deng, R., Qin, F., Chen, R., Huang, W., Hong, M., & Liu, X. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2014.317 · Cited by 990
Li, X., Zhang, F., & Zhao, D. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00163j · Cited by 615
Self-citationZhou, L., Wang, R., Yao, C., Li, X., Wang, C., Zhang, X., Xu, C., Zeng, A., Zhao, D., & Zhang, F. (2015) · Nature Communications · doi:10.1038/ncomms7938 · Cited by 321
Self-citationNear-infrared (NIR) up-conversion optogenetics
Hososhima, S., Yuasa, H., Ishizuka, T., Hoque, M. R., Yamashita, T., Yamanaka, A., Sugano, E., Tomita, H., & Yawo, H. (2015) · Scientific Reports · doi:10.1038/srep16533 · Cited by 126
Metadata
- Authors
- Li, X., Wang, R., Zhang, F., & Zhao, D.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-06-02
- Volume
- 14
- Issue
- 6
- Pages
- 3634-3639
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2320519176
- Cited by
- 185
- References
- 0
- FWCI
- 13.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.