ORCID ID

https://orcid.org/0000-0002-2597-6443

Date of Award

5-2024

Degree Type

Dissertation

Degree Name

Ph.D.

Degree Program

Chemistry

Department

Chemistry

Major Professor

Zito, Phoebe

Second Advisor

Tarr, Matthew

Third Advisor

Rick, Steven

Fourth Advisor

Wiley, John

Abstract

As nanotechnology expands, the pervasive use of metal nanoparticles in consumer products and electronics, as well as their multiple industrial, agricultural, and energy applications raises the demand for precise quantification and characterization. Addressing this need, single particle inductively coupled plasma mass spectrometry (spICP-MS) emerges as a powerful analytical technique with potential for a diverse range of applications. This research focuses on optimizing methods for nanoparticle (NP) detection in complex matrices like cosmetics and asphaltenes by spICP-MS. Determining the nature of metal constituents in these types of matrices is of interest due to their varying implications. Accordingly, spICP-MS can differentiate between metal-containing NPs and dissolved metals simultaneously. Analyzing complex mixtures presents a challenge in spICP-MS research due to a higher background presence, which reduces the signal-to-noise ratio and makes particle events indistinguishable. Accordingly, method parameters are exploited and data processing techniques are evaluated to overcome these challenges. In order to analyze both aqueous and hydrocarbon complex mixtures, the instrument is operated in two different modes - aqueous and hydrocarbon. In aqueous mode, a routine spICP-MS method is presented for the screening of NPs in powder-based facial cosmetics purchased from American retailers. The presence of NPs less than 100 nm was observed in all tested facial cosmetic samples suspended in water. Qualitative and semi-quantitative results for the cosmetic samples are reported and the applicability of the technique for routine measurements is demonstrated. For hydrocarbon mode, first a blank-independent, sample-based data processing approach is evaluated specifically pertaining to spICP-MS analysis of petroleum matrices. Statistical outliers are removed from the data set to create a representative background. The background data is binned into a histogram and the fit of a Gaussian and Poisson model is applied and compared to determine the appropriate calculation for the particle detection threshold (PDT). The goal is to prevent over or underestimation of the PDT and produce the most accurate size distributions and particle counts. Second, the sample data processing approach is utilized for the characterization of NPs added as co-precipitants in asphaltenes suspended in o-xylene. Particle number concentrations, ionic concentrations, size detection limits and size distributions for 56Fe and 60Ni present in eleven asphaltene samples are reported. The challenges and limitations of the three studies are addressed and future directions are discussed.

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The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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