Tiered Approach to Detect Nanomaterials in Food and Environmental Matrices

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Nanomaterials (NMs), implemented into a plethora of consumer products, are a potential new class of pollutants with unknown hazards to the environment. Exposure assessment is necessary for hazard assessment, life cycle analysis, and environmental monitoring. Current nanomaterial detection techniques on

Nanomaterials (NMs), implemented into a plethora of consumer products, are a potential new class of pollutants with unknown hazards to the environment. Exposure assessment is necessary for hazard assessment, life cycle analysis, and environmental monitoring. Current nanomaterial detection techniques on complex matrices are expensive and time intensive, requiring weeks of sample preparation and detection by specialized equipment, limiting the feasibility of large-scale monitoring of NMs. A need exists to develop a rapid pre-screening technique to detect, within minutes, nanomaterials in complex matrices. The goal of this dissertation is to develop a tiered process to detect and characterize nanomaterials in consumer products and environmental samples. The approach is accomplished through a two tier rapid screening process to screen likely presence/absence of elements present in common nanomaterials at environmentally relevant concentrations followed by a more intensive three tier characterization process, if nanomaterials are likely to occur. The focus is on SiO2 and TiO2 nanomaterials with additional work performed on hydroxyapatite (Ca5(PO4)3(OH)). The five step tiered process is as follows: 1) screen for elements in the sample by laser induced breakdown spectroscopy (LIBS) and X-ray fluorescence (XRF), 2) extract nanomaterials from the sample and screen for extracted elements by LIBS and XRF, 3) confirm presence and elemental composition of nanomaterials by transmission electron microscopy paired with energy dispersive X-ray spectroscopy, 4) quantify the elemental composition of the sample by inductively coupled plasma – mass spectrometry, and 5) identify mineral phase of crystalline material by X-ray diffraction. This dissertation found LIBS to be an accurate method to detect Si and Ti in food matrices (tier one approach) with strong agreement with the product label, detecting Si and Ti in 93% and 89% of the samples labeled as containing each material, respectively. In addition XRF identified Ti, Si, and Ca in 100% of food samples TEM-confirmed to contain Ti, Si, and Ca respectively. As a tier two approach, LIBS on the 0.2 micrometer filter identified nano silicon in 42% of samples confirmed by TEM to contain nano Si and 67% of TEM-confirmed samples to contain Ti. XRF identified Si, Ti, and Ca loaded on to a 0.1 µm filter and Ti in the surfactant rich phase of CPE of water and water with NOM.