The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Preparation and characterization of uniformly sized molecularly imprinted polymers functionalized with core–shell magnetic nanoparticles for the recognition and enrichment of protein
Abstract
A general method to prepare thin, molecularly imprinted polymer (MIP) coatings on magnetic Fe3O4 nanoparticles (NPs) with a uniform core–shell structure for the recognition and enrichment of protein was developed. Four proteins (bovine serum albumin (BSA, pI = 4.9), bovine hemoglobin (BHb, pI = 6.9), bovine pancreas ribonuclease A (RNase A, pI = 9.4) and lysozyme (Lyz, pI = 11.2)) with different isoelectric points were chosen as the templates. The magnetic protein-MIPs were synthesized by combining surface imprinting and sol–gel techniques. The morphology, adsorption and recognition properties of the magnetic molecularly imprinted NPs were investigated by transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopy and through the use of a vibrating sample magnetometer (VSM). In comparison with the use of Lyz, BSA and RNase A as template proteins, BHb-imprinted Fe3O4 showed the best imprinting effect and the highest adsorption capacity among the four proteins. The as-prepared Fe3O4@BHb-MIPs NPs with a mean diameter of 230 nm were coated with an MIP shell that was 10 nm thick, which enabled the Fe3O4@BHb-MIPs to easily reach adsorption equilibrium. A high magnetic saturation value of 25.47 emu g−1 for Fe3O4@BHb-MIPs NPs was obtained, which endowed the adsorbent with the convenience of magnetic separation under an external magnetic field. The resultant Fe3O4@BHb-MIPs NPs could not only selectively extract a target protein from mixed proteins but also specifically capture the protein BHb from a real sample of bovine blood. In addition, different batches of magnetic MIPs showed good reproducibility and reusability for at least six repeated cycles.
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.
Integrity signals
Registry
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.
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
Laurent, S., Forge, D., Port, M., Roch, A., Robic, C., Vander Elst, L., & Muller, R. N. (2008) · Chemical Reviews · doi:10.1021/cr068445e · Cited by 6,852
Multifunctional Magnetic Nanoparticles: Design, Synthesis, and Biomedical Applications
Gao, J., Gu, H., & Xu, B. (2009) · Accounts of Chemical Research · doi:10.1021/ar9000026 · Cited by 1,907
Cited by
Wang, T., Zhang, L., Li, C., Yang, W., Song, T., Tang, C., Meng, Y., Dai, S., Wang, H., Chai, L., et al. (2015) · Environmental Science & Technology · doi:10.1021/es5061275 · Cited by 384
Superparamagnetic nanoparticles as versatile carriers and supporting materials for enzymes
Netto, C. G. C. M., Toma, H. E., & Andrade, L. H. (2013) · Journal of Molecular Catalysis B: Enzymatic · doi:10.1016/j.molcatb.2012.08.010 · Cited by 323
Molecular Imprinting Techniques Used for the Preparation of Biosensors
Ertürk, G., & Mattiasson, B. (2017) · Sensors · doi:10.3390/s17020288 · Cited by 292
Magnetic separation techniques in sample preparation for biological analysis: A review
He, J., Huang, M., Wang, D., Zhang, Z., & Li, G. (2014) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2014.04.017 · Cited by 275
Ansari, S. (2017) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2017.03.001 · Cited by 215
Metadata
- Authors
- Gao, R., Kong, X., Wang, X., He, X., Chen, L., & Zhang, Y.
- Venue
- Journal of Materials Chemistry
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2011
- Volume
- 21
- Issue
- 44
- Pages
- 17863
- ISSN
- 0959-9428 (ISSN-L)
- 1364-5501
- OpenAlex
- W2131852455
- Cited by
- 208
- References
- 2
- FWCI
- 18.11
- 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: Preparation and characterization of uniformly sized molecularly imprinted polymers functionalized with core–shell magnetic nanoparticles for the recognition and enrichment of protein. https://app.cochat.ai/papers/10.1039/c1jm12414e
- The paper
- Gao, R., Kong, X., Wang, X., He, X., Chen, L., & Zhang, Y. (2011). Preparation and characterization of uniformly sized molecularly imprinted polymers functionalized with core–shell magnetic nanoparticles for the recognition and enrichment of protein. Journal of Materials Chemistry. https://doi.org/10.1039/c1jm12414e
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.