The use of spectrofluorometers to examine nanomaterials is quite popular using either fluorescence or synchronous measurements. However, understanding how a material’s optical properties can influence spectral acquisition are of great importance to accurately characterize nanomaterials. This dissertation presents a series of computational and experimental studies aimed at enhancing the quantitative understanding of nanoparticle interactions with matter and photons. This allows for more reliable spectrofluorometer based acquisition of nanoparticle containing solutions. Chapter I presents a background overview of the works described in this dissertation. Correction of the gold nanoparticle (AuNP) inner filter effect (IFE) on fluorophore fluorescence using PEGylated AuNPs as an external reference method is demonstrated in Chapter II. The AuNP IFE is corrected to quantify tryptophan fluorescence for surface adsorbed proteins. We demonstrate that protein adsorption onto AuNPs will only induce ~ 20% tryptophan fluorescence reduction instead of the commonly assumed 100% reduction. Using water Raman intensities to determine the effective path lengths of a spectrofluorometer for correction of fluorophore fluorescence is discussed in Chapter III. Using Ni(NO3)2 and K2Cr2O7 as Raman IFE references, the excitation and emission path lengths are found to exhibit chromophore and fluorophore independence, however path lengths are spectrofluorometer dependent. Finally, ratiometric resonance synchronous spectroscopy (R2S2) is discussed in Chapter IV. Using a combination of UV-vis and R2S2 spectroscopy, the optical cross sections of a wide range of nanomaterials were determined. Also on-resonance fluorescence in solution is demonstrated for the first time. The nanoparticles discussed range from photon absorbers, scatterers, simultaneous photon absorbers and scatterers, all the way to simultaneous photon absorbers, scatterers, and emitters.

Complex samples containing nanoscale or larger materials exhibit light scattering, a universal property of matter. However, the influence of scattering-induced cascading optical processes on quantifying sample scattering intensity and depolarization has not been thoroughly evaluated. This study uses polystyrene nanoparticles (PSNPs) as a model analyte for systematic experimental and computational investigation. It aims to elucidate the effects of cascading optical processes on scattering cross-section, molar coefficients, depolarization, and intensities. A theoretical model is introduced to show how the Beer-Lambert law is complicated by forward-scattered light interference in UV-Vis measurements. The dependence of scattering intensity on concentration and particle size is complex due to light scattering depolarization and inner filter effects (IFEs). Scattering depolarization increases with PSNP scattering extinction but levels off before unity, influenced by light polarization. Insights from this work enhance understanding of material characterization and nanoparticle quantification and clarify light scattering effects on absorption and fluorescence measurements.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-05-12T12:35:36Z No. of bitstreams: 1 arquivo total.pdf: 5933598 bytes, checksum: f90080e0529915a4c5c37308259bee89 (MD5) / Made available in DSpace on 2016-05-12T12:35:36Z (GMT). No. of bitstreams: 1 arquivo total.pdf: 5933598 bytes, checksum: f90080e0529915a4c5c37308259bee89 (MD5) Previous issue date: 2015-06-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this work it was developed a new strategy for intervals selection using the successive projections algorithm (SPA) coupled to N-PLS and U-PLS models, both with residual bilinearização (RBL) as a post-calibration step. The new algorithm coupled to N-PLS/RBL models was evaluated in two cases of studies. The first was simulated data for quantitation of two analytes (A and B) in the presence of a single interfering. On the second study was conducted a quantitation of ofloxacin in water in the presence of interferents (ciprofloxacin and danofloxacin) by means of liquid chromatography with diode array detection (LC-DAD) data modeling. The results were compared to the N-PLS/RBL model and the variables selection with the genetic algorithm (GA-N-PLS/RBL). In the first case of study (simulated data) were observed RMSEP values (x 10-3 in arbitrary units) for the analytes A and B in the order of 6.7 to 47.6; 10.6 to 11.4; and 6.0 to 14.0 for the N-PLS/RBL, Ga-N-PLS/RBL and the proposed method, respectively. On the second case of study (HPLC-DAD data) RMSEP value (mg/L) of 0.72 (N-PLS/RBL); 0.70 (GA-N-PLS/RBL) and 0.64 (iSPA N-PLS/RBL) were obtained. When combined with the U-PLS/RBL, the new algorithm was evaluated in the EEM modeling in the presence of inner filter effect. Simulated data and quantitation of phenylephrine in the presence of acetaminophen in water sample and interferences (ibuprofen and acetylsalicylic acid) were used as a case of studies. The results were compared to the U-PLS/RBL and e twell established method PARAFAC. For simulated data was observed the following RMSEP values (in arbitrary units) 1.584; 0.077 and 0.066 for PARAFAC; U-PLS/RBL and the proposed method, respectively. In the quantitation of phenylephrine the found RMSEP (in μg/L) were of 0.164 (PARAFAC); 0.089 (U-PLS/RBL) and 0.069 (ISPA-U-PLS/RBL). In all cases it was shown that variables selection is a useful tool capable of improving accuracy when compared with the respective global models (model without variables selection) leading to more parsimonious models. It was observed in all cases, that the sensitivity loss promoted by variables selection is compensated by using more selective channels, justifying the obtained RMSEP smaller values. Finally, it was also observed that the models based on variables selection such as the proposed method were free from significant bias at 95% confidence. / Neste trabalho foi desenvolvida uma nova estratégia para seleção de intervalos empregando o algoritmo das projeções sucessivas (SPA) acoplado a modelos N-PLS e U-PLS, ambos com etapa pós-calibração de bilinearização residual (RBL). O novo algoritmo acoplado a modelos N-PLS/RBL, foi avaliado em dois estudos de casos. O primeiro envolvendo dados simulados para quantificação de dois analitos (A e B) na presença de um único interferente. No segundo foi conduzida a quantificação de ofloxacina em água na presença de interferentes (ciprofloxacina e danofloxacina) por meio da modelagem de dados cromatografia liquida com detecção por arranjo de diodos (LC-DAD). Os resultados obtidos foram comparados ao modelo N-PLS/RBL e a seleção de variáveis com o algoritmo genético (GA-N-PLS/RBL). No primeiro estudo de caso (dados simulados) foram observados valores de RMSEP (x 10-3 em unidades arbitrárias) para os analitos A e B da ordem de 6,7 e 47,6; 10,6 e 11,4; 6,0 e 14,0 para o N-PLS/RBL, GA-N-PLS/RBL e o método proposto, respectivamente. No segundo estudo de caso (dados HPLC-DAD) valores de RMSEP (em mg/L) de 0,72 (N-PLS/RBL); 0,70 (GA-N-PLS/RBL) e 0,64 (iSPA-N-PLS/RBL) foram obtidos. Quando combinado com o U-PLS/RBL o novo algoritmo foi avaliado na modelagem de EEM em presença efeito de filtro interno. Dados simulados e a quantificação de fenilefrina na presença de paracetamol em amostras de água e interferentes (Ibuprofeno e ácido acetil salicílico) foram usados como estudos de caso. Os resultados obtidos foram comparados ao modelo U-PLS/RBL e ao bem estabelecido método PARAFAC. Para dados simulados foram observado os seguintes valores de RMSEP (em unidades arbitrarias) 1,584; 0,077 e 0,066 para o PARAFAC; U-PLS/RBL e método proposto, respectivamente. Na quantificação de fenilefrina os RMSEP (em μg/L) encontrados foram de 0,164 (PARAFAC); 0,089 (U-PLS/RBL) e 0,069 (iSPA-U-PLS/RBL). Em todos os casos foi demostrado que seleção de variáveis é uma ferramenta útil capaz de melhorar a acurácia quando comparados aos respectivos modelos globais (modelo sem seleção de variáveis) e tornar os modelos mais parcimoniosos. Foi observado ainda para todos os casos, que a perda de sensibilidade promovida pela seleção de variáveis é compensada pelo uso de canais mais seletivos, justificando os menores valores de RMSEP obtidos. E por fim, foi também observado que os modelos baseados em seleção de variáveis como o método proposto foram isentos de bias significativos a 95% de confiança.