Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 239

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

Metal–Organic Framework@Porous Organic Polymer Nanocomposite for Photodynamic Therapy

Zheng, Xiaohua, Wang, Lei, Pei, Qing, He, Shasha, Liu, Shi, Xie, Zhigang·2017·Chemistry of Materials

Abstract

The limitation for the biomedical application of porous organic polymers (POPs) is the big size and poor dispersibility in aqueous media. Herein, a nanoscale metal–organic framework (MOF)@POP composite, named UNM, has been synthesized by epitaxial growth of the photoactive porphyrin-POPs (H 2 P-POP) on the outer surface of amine containing UiO-66 (UiO-AM). After the growth of POPs, the crystallization, pore structure, and size distribution of UNM are retained well. The formed UNM possesses a small size of less than 200 nm and could be internalized by cancer cells. Such light-activated UNM exhibits efficient ability to generate 1 O 2 under various experimental conditions, which can be further applied for PDT efficacy. The present work demonstrates the great potential of nanoscale porous polymers in biomedical fields and cancer treatment.

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.chemmater.7b00228

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

Show all 1 references

Cited by

See all 239

Metadata

Authors
Zheng, X., Wang, L., Pei, Q., He, S., Liu, S., & Xie, Z.
Venue
Chemistry of Materials
Published
2017-03-03
Volume
29
Issue
5
Pages
2374-2381
ISSN
  • 0897-4756 (ISSN-L)
  • 1520-5002
OpenAlex
W2590034771
Cited by
239
References
1
FWCI
12.48
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: Metal–Organic Framework@Porous Organic Polymer Nanocomposite for Photodynamic Therapy. https://app.cochat.ai/papers/10.1021/acs.chemmater.7b00228
The paper
Zheng, X., Wang, L., Pei, Q., He, S., Liu, S., & Xie, Z. (2017). Metal–Organic Framework@Porous Organic Polymer Nanocomposite for Photodynamic Therapy. Chemistry of Materials. https://doi.org/10.1021/acs.chemmater.7b00228

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.