Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 168

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

Photocatalytically Active Superstructures of Quantum Dots and Iron Porphyrins for Reduction of CO2 to CO in Water

Open access
Lian, Shichen, Kodaimati, Mohamad S., Weiss, Emily A.·2018·ACS Nano

Abstract

This paper describes the use of electrostatic assemblies of negatively charged colloidal CuInS 2 /ZnS quantum dot (QD) sensitizers and positively charged, trimethylamino-functionalized iron tetraphenylporphyrin catalysts (FeTMA) to photoreduce CO 2 to CO in water upon illumination with 450 nm light. This system achieves a turnover number (TON) of CO (per FeTMA) of 450 after 30 h of illumination, with a selectivity of 99%. Its sensitization efficiency (TON per Joule of photons absorbed) is a factor of 11 larger than the previous record for photosensitization of an iron porphyrin catalyst for this reaction, held by a system in which both QDs and metal porphyrin were uncharged. Steady-state and time-resolved optical spectroscopy provides evidence for electrostatic assembly of QDs and FeTMA. Control of the size of the assemblies with addition of a screening counterion, K +, and a correlation between their measured size and their catalytic activity, indicates that the enhancement in performance of this system over the analogous uncharged system is due to the proximity of the FeTMA catalyst to multiple light-absorbing QDs and the selective formation of QD–FeTMA contacts (rather than QD–QD or FeTMA–FeTMA contacts). This system therefore shows the ability to funnel photoinduced electrons to a reaction center, which is crucial for carrying out reactions that require multistep redox processes under low photon flux, and thus is an important advance in developing artificial photocatalytic systems that function in natural light.

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/acsnano.7b07377

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 168

Metadata

Authors
Lian, S., Kodaimati, M. S., & Weiss, E. A.
Venue
ACS Nano
Published
2018-01-08
Volume
12
Issue
1
Pages
568-575
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2782051377
Cited by
168
References
1
FWCI
4.98
Type
journal article
Language
en
Open access
green

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.