The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Effects of Molecular Weight Distribution and Chemical Properties of Natural Organic Matter on Gold Nanoparticle Aggregation
Abstract
The complexity of natural organic matter (NOM) motivates determination of how specific components in a NOM mixture interact with and affect nanoparticle (NP) behavior. The effects of two Suwannee River NOM fractions (separated by a 100,000 g/mol ultrafiltration membrane) on gold NP aggregation are compared. The weight-average molecular weight, Mw, for the unfractionated NOM was 23,300 g/mol, determined by size exclusion chromatography with multiangle light scattering. The NOM was comprised of ~1.8 wt % of >100,000 g/mol retentate (NOMr, Mw = 691,000 g/mol) and 98 wt % of filtrate (NOMf, Mw = 12,800 g/mol). Ten ppm of NOMr provided significantly better NP stability against aggregation than 10 ppm of NOMf in 100 mM NaCl due to steric effects. In the unfractionated NOM, the relative importance of the two components was concentration-dependent. For a low concentration of unfractionated NOM (10 ppm), both fractions contributed to the NOM effects; for a high concentration (560 ppm), NP stability was controlled by the small amount (10 ppm) of NOMr present, rather than the higher amount (550 ppm) of NOMf. Therefore, large humic aggregates in a heterogeneous NOM sample can have disproportionately strong effects, and characterization of Mw distributions (rather than average Mw) may be required to explain NOM effects on NP behavior.
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
Molecular structure in soil humic substances: the new view
Sutton, R., & Sposito, G. (2005) · Environmental Science & Technology · doi:10.1021/es050778q · Cited by 1,316
Cited by
Interactions of Dissolved Organic Matter with Natural and Engineered Inorganic Colloids: A Review
Philippe, A., & Schaumann, G. E. (2014) · Environmental Science & Technology · doi:10.1021/es502342r · Cited by 770
Metadata
- Authors
- Louie, S. M., Tilton, R. D., & Lowry, G. V.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-04-17
- Volume
- 47
- Issue
- 9
- Pages
- 4245-4254
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W2319559023
- Cited by
- 200
- References
- 1
- FWCI
- 5.77
- 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.