Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 483

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

Alkyl Phosphonate/Phosphate Coating on Magnetite Nanoparticles: A Comparison with Fatty Acids

Sahoo, Yudhisthira, Pizem, Hillel, Fried, Tcipi, Golodnitsky, Dina, Burstein, Larisa, Sukenik, Chaim N., Markovich, Gil·2001·Langmuir

Abstract

Coated magnetite nanoparticles with a 6−8 nm average diameter were prepared. The surfactants used to stabilize the nanoparticles and disperse them in organic solvents were oleic acid (OA), lauric acid, dodecyl phosphonate, hexadecyl phosphonate, and dihexadecyl phosphate. Transmission electron microscopy analyses of the aggregation of the coated particles suggest that carboxylate surfactants provide the particles with better isolation and dispersibility as compared with phosphonate surfactants. However, Fourier transform infrared spectra of the phosphonate and phosphate coated particles suggest that these surfactants cover the surface of the nanoparticles in islands of high packing density. The thermogravimetric and differential scanning calorimetry measurements suggest that there is a quasi-bilayer of these surfactants covering the surface of the nanoparticles, with varying amounts of surfactant in the outer layer and with the second layer weakly bound to the primary layer through hydrophobic interactions between the alkyl chains. The desorption temperatures of the alkyl phosphonates and phosphate are higher than those of the carboxylate coated particles. The enthalpy of binding of the ligands suggests strong P−O−Fe bonding on the surface. Nevertheless, regardless of binding strength, the OA coated particles are better dispersed in organic solvents. Their higher hydrophobicity is likely due to different interactions among the oleyl chains and/or a smaller tendency to form bilayer structures.

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/la010703+

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 483

Metadata

Authors
Sahoo, Y., Pizem, H., Fried, T., Golodnitsky, D., Burstein, L., Sukenik, C. N., & Markovich, G.
Venue
Langmuir
Published
2001-11-09
Volume
17
Issue
25
Pages
7907-7911
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
OpenAlex
W2009228997
Cited by
483
References
1
FWCI
4.55
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

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.