Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 175

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

Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization

Open access
Chen, Ritchie, Christiansen, Michael G., Anikeeva, Polina·2013·ACS Nano

Abstract

This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe2O4 (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic parameters in the thermal decomposition of organometallic precursors that yield optimized magnetic nanoparticles over a wide range of sizes and compositions. The developed synthetic procedures allow for gram-scale production of magnetic nanoparticles stable in physiological buffer for several months. Our magnetic nanoparticles display some of the highest heat dissipation rates, which are in qualitative agreement with the trends predicted by a dynamic hysteresis model of coherent magnetization reversal in single domain magnetic particles. By combining physical simulations with robust scalable synthesis and materials characterization techniques, this work provides a pathway to a model-driven design of magnetic nanoparticles tailored to a variety of biomedical applications ranging from cancer hyperthermia to remote control of gene expression.

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/nn4035266

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 175

Metadata

Authors
Chen, R., Christiansen, M. G., & Anikeeva, P.
Venue
ACS Nano
Published
2013-09-12
Volume
7
Issue
10
Pages
8990-9000
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2112189528
Cited by
175
References
2
FWCI
4.88
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

How to cite

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

This page
CoChat. (2026). Citation check: Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization. https://app.cochat.ai/papers/10.1021/nn4035266
The paper
Chen, R., Christiansen, M. G., & Anikeeva, P. (2013). Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization. ACS Nano. https://doi.org/10.1021/nn4035266

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.