Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 314

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Predictive Model for the Design of Plasmonic Metal/Semiconductor Composite Photocatalysts

Ingram, David B., Christopher, Phillip, Bauer, Jonathan L., Linic, Suljo·2011·ACS Catalysis

Abstract

We demonstrate the design of composite plasmonic metal/semiconductor photocatalysts, which show enhanced visible light photocatalytic activity compared to the semiconductor alone. We show that the overlap between the illumination source spectrum, semiconductor absorbance spectrum and metal nanoparticle surface plasmon resonance spectrum provides a useful descriptor for predicting the relative rate enhancements induced by metal surface plasmons for composite photocatalysts with similar arrangements of metal and semiconductor building blocks. We also show that optical simulations can be used to predict the value of the descriptor of photocatalytic activity for any arbitrary combination of illumination source, semiconductor, and plasmonic metal, and therefore guide the formulation of optimal composite photocatalysts. We have used optical simulations to identify optimal plasmonic nanostructures for a few model semiconductors.

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/cs200320h

Cites

2 of 2 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

Show all 2 references

Cited by

See all 314

Metadata

Authors
Ingram, D. B., Christopher, P., Bauer, J. L., & Linic, S.
Venue
ACS Catalysis
Published
2011-09-23
Volume
1
Issue
10
Pages
1441-1447
ISSN
2155-5435 (ISSN-L)
OpenAlex
W2334052068
Cited by
314
References
2
FWCI
7.36
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

How to cite

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

This page
CoChat. (2026). Citation check: Predictive Model for the Design of Plasmonic Metal/Semiconductor Composite Photocatalysts. https://app.cochat.ai/papers/10.1021/cs200320h
The paper
Ingram, D. B., Christopher, P., Bauer, J. L., & Linic, S. (2011). Predictive Model for the Design of Plasmonic Metal/Semiconductor Composite Photocatalysts. ACS Catalysis. https://doi.org/10.1021/cs200320h

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.