Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 188

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Efficient Infrared-to-Visible Upconversion with Subsolar Irradiance

Open access
Mahboub, Melika, Huang, Zhiyuan, Tang, Ming Lee·2016·Nano Letters

Abstract

Abstract Third generation photovoltaics are inexpensive modules that promise power conversion efficiencies exceeding the thermodynamic Shockley–Queisser limit, perhaps by using up- or down-converters, intermediate band solar cells, tandem cells, hot carrier devices, or multiexciton generation. Here, we report the efficient upconversion of infrared to visible light at excitation densities below the solar flux. Colloidally synthesized core–shell lead sulfide–cadmium sulfide nanocrystals in combination with tetracene derivatives absorb near-infrared light and emit visible light at 560 nm with an upconversion quantum yield (QY) of 8.4 ± 1.0%, which is a factor of 4 lower than the maximum upconversion QY possible. This is achieved with 808 nm cw excitation at 3.2 mW/cm2, approximately three times lower than the available solar flux. The molecular and nanocrystal engineering here paves the way toward utilizing this hybrid upconversion platform in photovoltaics, photodetectors and photocatalysis.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acs.nanolett.6b03503

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Cited by

See all 188

Metadata

Authors
Mahboub, M., Huang, Z., & Tang, M. L.
Venue
Nano Letters
Published
2016-10-31
Volume
16
Issue
11
Pages
7169-7175
ISSN
  • 1530-6984 (ISSN-L)
  • 1530-6992
OpenAlex
W2543413148
Cited by
188
References
1
FWCI
9.02
Type
journal article
Language
en
Open access
hybrid

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Efficient Infrared-to-Visible Upconversion with Subsolar Irradiance. https://app.cochat.ai/papers/10.1021/acs.nanolett.6b03503
The paper
Mahboub, M., Huang, Z., & Tang, M. L. (2016). Efficient Infrared-to-Visible Upconversion with Subsolar Irradiance. Nano Letters. https://doi.org/10.1021/acs.nanolett.6b03503

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.