Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 457

The DOI matches its Crossref record.

Reliable Quantitative SERS Analysis Facilitated by Core–Shell Nanoparticles with Embedded Internal Standards

Shen, Wei, Lin, Xuan, Jiang, Chaoyang, Li, Chaoyu, Lin, Haixin, Huang, Jingtao, Wang, Shuo, Liu, Guokun, Yan, Xiaomei, Zhong, Qiling, Ren, Bin·2015·Angewandte Chemie International Edition

Abstract

Quantitative analysis is a great challenge in surface‐enhanced Raman scattering (SERS). Core‐molecule‐shell nanoparticles with two components in the molecular layer, a framework molecule to form the shell, and a probe molecule as a Raman internal standard, were rationally designed for quantitative SERS analysis. The signal of the embedded Raman probe provides effective feedback to correct the fluctuation of samples and measuring conditions. Meanwhile, target molecules with different affinities can be adsorbed onto the shell. The quantitative analysis of target molecules over a large concentration range has been demonstrated with a linear response of the relative SERS intensity versus the surface coverage, which has not been achieved by conventional SERS methods.

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 · 10.1002/anie.201502171

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.

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

Cited by

See all 457

Metadata

Authors
Shen, W., Lin, X., Jiang, C., Li, C., Lin, H., Huang, J., Wang, S., Liu, G., Yan, X., Zhong, Q., & Ren, B.
Venue
Angewandte Chemie International Edition
Publisher
Wiley
Published
2015-05-04
Volume
54
Issue
25
Pages
7308-7312
ISSN
  • 1433-7851 (ISSN-L)
  • 1521-3773
OpenAlex
W2110066199
Cited by
457
References
0
FWCI
12.37
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: Reliable Quantitative SERS Analysis Facilitated by Core–Shell Nanoparticles with Embedded Internal Standards. https://app.cochat.ai/papers/10.1002/anie.201502171
The paper
Shen, W., Lin, X., Jiang, C., Li, C., Lin, H., Huang, J., Wang, S., Liu, G., Yan, X., Zhong, Q., & Ren, B. (2015). Reliable Quantitative SERS Analysis Facilitated by Core–Shell Nanoparticles with Embedded Internal Standards. Angewandte Chemie International Edition. https://doi.org/10.1002/anie.201502171

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.