A New In Vitro and Machine Learning Framework for Characterizing Albumin Binding Across the Per- and Polyfluoroalkyl Substance Chemical Landscape.

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dc.contributor.advisorScott Belcher, Chair
dc.contributor.advisorJane Hoppin, Member
dc.contributor.advisorDavid Reif, Member
dc.contributor.advisorJames Bonner, Member
dc.contributor.advisorJohn Meitzen, Graduate School Representative
dc.contributor.authorStarnes, Hannah Michelle
dc.date.accepted2024-06-21
dc.date.accessioned2025-06-28T12:30:41Z
dc.date.available2025-06-28T12:30:41Z
dc.date.defense2024-06-07
dc.date.embargo2025-06-28
dc.date.issued2024-06-07
dc.date.released2025-06-28
dc.date.reviewed2024-06-17
dc.date.submitted2024-06-10
dc.degree.disciplineToxicology
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Environmental and Molecular Toxicology.
dc.formatPh.D. North Carolina State University, 2025.
dc.identifier.otherdeg38308
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/45455
dc.titleA New In Vitro and Machine Learning Framework for Characterizing Albumin Binding Across the Per- and Polyfluoroalkyl Substance Chemical Landscape.
dcterms.extent1 online resource (xv, 282 pages) : illustrations (some color)

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