Doutorado em Química
URI Permanente para esta coleção
Nível: início
Ano de início: 2014
Conceito atual na CAPES: 5
Ato normativo: Homologação da 85ª Reunião do CTC-ES, Parecer CNE/CES nº 163/2005.
Processo nº 23001.000081/2005-56 do Ministério da Educação.
Publicado no DOU 28/07/2005, seção 1, página 11)
Periodicidade de seleção: Anual
Área(s) de concentração: Química
Url do curso: https://quimica.vitoria.ufes.br/pt-br/pos-graduacao/PPGQ/detalhes-do-curso?id=956/a>
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- ItemMetal-organic frameworks : da produção de tecidos funcionais ao Ensino de Química(Universidade Federal do Espírito Santo, 2024-08-30) Almeida, Caroline Batistin da Cruz; Moura, Paulo Rogério Garcez de ; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador2; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador3; https://orcid.org/; http://lattes.cnpq.br/; Co-orientador4; ID do co-orientador4; Lattes do co-orientador4; Luz, Priscilla Paiva ; https://orcid.org/; http://lattes.cnpq.br/; Orientador2; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; Alves Junior, Severino; https://orcid.org/; http://lattes.cnpq.br/; Pimenta, Patrícia Figueiredo Santos ; https://orcid.org/; http://lattes.cnpq.br/; Tavares, Mari Inez ; https://orcid.org/; http://lattes.cnpq.br/; Dalmaschio, Cleocir José ; http://lattes.cnpq.br/; 5º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 6º membro da banca; https://orcid.org/; http://lattes.cnpq.br/; 7º membro da banca; https://orcid.org/; http://lattes.cnpq.br/Glyphosate (GLY) is an organophosphate pesticide widely used in agriculture as a non selective, broad-spectrum herbicide. It is the most widely sold pesticide in Brazil and worldwide and is considered potentially carcinogenic to humans according to the World Health Organization. Porous materials have been tested for the adsorption of this compound in aqueous media, with zirconium-based MOFs (ZrMOFs) being the materials with the best reported performance. This research evaluated the adsorption performance of GLY by ZrMOFs not yet investigated and produced cellulose composites functionalized with MOFs for the same application. The MOFs MOF-808, UiO-66 and UiO-66(NH2) were synthesized and characterized by XRD, TGA, DTA, N2 adsorption, DLS and pcz. Adsorption tests were performed in batch mode and PPO, PSO and Weber-Moris kinetic models were applied to the results. The adsorption equilibrium was investigated by the Langmuir and Freundlich models. MOF-808 synthesized in water reached the maximum GLY adsorption capacity of 277.0 mg g-1 and MOF-808 synthesized in DMF obtained 273.2 mg g-1, however, with better kinetic performance removing approximately 70% of GLY in solution in 10 minutes of contact and 99.5% in 5h. The PSO model best fitted the kinetic results indicating great availability of active sites for adsorption in the materials and the chemical nature of GLY adsorption in the material. The Langmuir model confirms this character indicating monolayer adsorption and the same interaction energy in the active sites. Cellulosic fibers functionalized with MOF-808 via in situ and ex situ methods were successfully obtained and showed good GLY adsorption performance, removing 99% of GLY in 3 hours of contact with a 0.1 mM solution of the herbicide. These fibers have potential applications for filters and personal protective clothing. Based on the theoretical framework involving the results obtained, a teaching plan on “MOFs for water decontamination” was developed, aimed at teaching Chemistry at the high school level. Problem-based experimental activities were applied to develop skills for the New High School Training Itinerary through the theme. It was observed that skills in the areas of scientific investigation, creative processes and sociocultural mediation were developed throughout the activities, and the theme proved to be an important strategy for teaching various objects of knowledge in the Chemistry curriculum, such as bonding theories, mixture separation techniques, acid-base theories, formulas, properties and reactions of organic compounds
- ItemSíntese e caracterização de MOFs e seus compósitos utilizados na modificação de eletrodos de carbono aplicados como sensores eletroquímicos(Universidade Federal do Espírito Santo, 2024-08-08) Fonseca, Ramon Raoni Ferreira da; Ferreira, Rafael de Queiroz; https://orcid.org/0000-0002-5190-8508; Luz, Priscilla Paiva; https://orcid.org/0000-0002-9460-546X; https://orcid.org/0000-0002-0603-9999; Rosa, Thalles Ramon; Caliman, Cristiano; Ribeiro, Josimar; Ribeiro, Marcos AntônioThe metal-organic frameworks (MOFs) MIL-53(Fe), MIL-88b-NH2(Fe), MIL-100(Fe) and MIL-82(Fe) were synthesized from the solvothermal reaction between a Fe salt and na acid ligand (benzene-1,4-dicarboxylic acid, 2-amino-benzene-1,4-dicarboxylic acid, benzene-1,3,5-tricarboxylic acid, or benzene-1,2,4,5-tetracarboxylic acid). All the materials obtained were characterized using physicochemical techniques. The electrochemical techniques showed that MIL-88b-NH2(Fe) had the most promising results for developing an electroanalytical methodology to detect and quantify Cu2+ in ethanol fuel. The presence of Cu2+ in ethanol fuel represents a problem in terms of quality, leading to malfunctioning automotive engines. Therefore, this study uses a FeIII-organic network to develop an innovative electrochemical sensor. Subsequently, the modified carbon paste selected to analyze Cu2+ in ethanol fuel was MIL-88b NH2(Fe) based on the results obtained from cyclic voltammetry and electrochemical impedance spectroscopy. Subsequently, square-wave anodic stripping voltammetry parameters were optimized using a design of experiments approach. The analytical signal of the electrode exhibited good stability (relative standard deviation = 5.2%). Besides, the correlation coefficient (r) and coefficient of determination (R²) calculated in the calibration curve were 0.9993 and 99.85%, respectively, indicating a good fit of the linear model to the experimental data. The limit of detection obtained from the linear equation was 2.0 × 10−8 mol L−1. Finally, in the recovery test using spiked samples of ethanol fuel, the obtained values were 83 and 96%, indicating the absence of matrix effects. It was shown that the MIL-88b-NH2(Fe) modified carbon paste electrode is a suitable sensor for assessing Cu2+ contamination in ethanol fuel under and above the limit permitted by Brazilian legislation.
- ItemIdentificação de alcaloides em extratos de espécies de Amaryllidaceae através da técnica de CL-EM suportada por estratégia de rede molecular e avaliação da atividade antiparasitária(Universidade Federal do Espírito Santo, 2024-04-17) Feu, Amanda Eiriz; Romão, Wanderson; https://orcid.org/; http://lattes.cnpq.br/; Borges, Warley de Souza; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; Endringer, Denise Coutinho ; https://orcid.org/; http://lattes.cnpq.br/; Bauermeister, Anelize ; https://orcid.org/; http://lattes.cnpq.br/; Andrade, Jean Paulo de ; https://orcid.org/; http://lattes.cnpq.br/; Kuster, Ricardo Machado ; http://lattes.cnpq.br/This work begins with a scientometric review covering the alkaloids of Amaryllidaceae through three databases. It presented the evolution of research areas since the earliest publications and an analysis of the main authors, institutions, keywords, and trends. Subsequently, a targeted analysis was conducted to identify and annotate alkaloids present in extracts of wild and commercial (hybrid) Amaryllidaceae bulbs. The extraction method employed was exclusively methanol maceration, without using other processes like acid-base extraction. The analysis was carried out using the UHPLC-ESI(+)-LTQ MS technique, with support from molecular networking networks on the Global Natural Products Social Molecular Networking (GNPS) platform. Initially, 19 previously isolated alkaloids were submitted to the GNPS compound library at the bronze level to generate annotations in the molecular network. The extracts were analyzed using the same methodology and sequence as the standards. The classical molecular network analysis had some limitations, leading to the adoption of the feature-based mode as more effective for network analysis. The resulting network had 10 families and 216 unique nodes. Among these families, 7 referred to various Amaryllidaceae alkaloid structures, including homolycorine, haemanthamine, pretazettine, lycorine, and galantamine types. This resulted in the annotation of 28 alkaloids, with an additional 15 alkaloids annotated among the unique nodes. Another aspect addressed was the evaluation of these extracts' activity against the promastigote form of Leishmania amazonensis. Three wild species and eight hybrid species were identified as active, with the raw extract of Ismene amancaes bulbs being the most active (IC50 = 1.29 µg/mL) and selective for the parasite. Molecular network analysis revealed the presence of N-oxide of lycoramine, lycoramine, lycorine, haemanthidine, 11-hydroxyvitattine N-oxide, goleptine, and 9-norpluviine in this species. An exclusive minor alkaloid, with an m/z of 350, was detected, although there is no spectral correspondence in the literature. Additionally, an acid-base extraction of Ismene amancaes bulbs and flowers was performed, resulting in four alkaloid-enriched extracts. GC-MS analysis detected 14 alkaloids, with hippeastrine, isolated from the ethyl acetate fraction, being noteworthy. Evaluation of pharmacokinetic parameters and druglikeness indicated the presence of ten alkaloids with favorable results, with seven of them considered promising compounds. Among these compounds, those with higher concentrations in the enriched extracts were evaluated by molecular docking using the CYP51 cysteine of L. infantum. 8-O-demethylhomolycorine and hippeastrine exhibited the lowest binding energies, suggesting potential for inhibition of Leishmania species. Therefore, the results indicate that Ismene amancaes shows a promising profile in antiparasitic activities, highlighting its efficacy and selectivity against L. amazonensis
- ItemExplorando métodos de seleção de variáveis e fusão de dados em regressão por vetores de suporte : uma aplicação em petroleômica(Universidade Federal do Espírito Santo, 2024-03-28) Cunha, Pedro Henrique Pereira da; Filgueiras, Paulo Roberto ; https://orcid.org/; http://lattes.cnpq.br/; https://orcid.org/; http://lattes.cnpq.br/; Souza, Murilo de Oliveira ; https://orcid.org/; http://lattes.cnpq.br/; Duarte, Lucas Mattos; https://orcid.org/; http://lattes.cnpq.br/; Almeida, Mariana Ramos de ; https://orcid.org/; http://lattes.cnpq.br/; Romão, Wanderson ; http://lattes.cnpq.br/Support Vector Regression (SVR) is considered a black-box machine learning method and has stood out in chemometrics over the past decades, achieving results superior or equal to methods already established in academia. As a black-box method, it is challenging to understand the cause/effect relationship. To address this, variable selection can be applied, a strategy that aims to identify the most influential variables in building the model. This work proposes the development of two variable selection methods - Permutation Subwindow Analysis (SPA) and Noise-Incorporated Permutation Subwindow Analysis (NISPA) - to apply in SVR combined with infrared. SPA and NISPA provided the most accurate models for kinematic viscosity, saturates, and aromatic content. The root mean square error of prediction (RMSEP) for SPA and NISPA were, respectively, 14.3% and 14.6% for kinematic viscosity, 4.7% and 4.4% for saturates content, and 3.4% and 3.1% for aromatic content. Therefore, SPA and NISPA, in addition to generally obtaining faster, more accurate, and more parsimonious models, revealed the most important variables for building SVR models. Another way to improve a model is data fusion, but this strategy has been little studied in SVR. Thus, data fusion was studied using NIR, MIR, and NMR of ¹H and ¹³C combined using low, medium, and high-level fusion. The models generated by data fusion were superior to the models without fusion for most tests. In API density, the application of medium-level fusion using PCA combining MIR and NIR developed a model with better parameters than the model without data fusion. By applying medium level fusion with GA to predict pour point, combining NIR and NMR of ¹H, it was possible to surpass models without fusion, as well as models found in the literature. In total nitrogen, high-level fusion with MIR and NMR of ¹H proved to be statistically better than models without data fusion. This demonstrates that it is possible to extract new information for SVR modeling using data fusion and obtain statistically better models than those derived from isolated analytical sources
- ItemBiofluidos e espectrometria de massas para triagem de pacientes para COVID-19(Universidade Federal do Espírito Santo, 2024-02-28) Almeida, Camila Medeiros de; Mill, José Geraldo; https://orcid.org/0000-0002-0987-368X; http://lattes.cnpq.br/2497419234600362; Romão, Wanderson ; https://orcid.org/0000-0002-2254-6683; http://lattes.cnpq.br/9121022613112821; https://orcid.org/0000-0003-3318-8583; http://lattes.cnpq.br/4627760102080131; Chaves, Andrea Rodrigues ; https://orcid.org/0000-0002-1600-1660; http://lattes.cnpq.br/6064014965252121; Campos, Luciene Cristina Gastalho ; https://orcid.org/0000-0002-5962-661X; http://lattes.cnpq.br/6872591263471658; Cunha Neto, Alvaro ; https://orcid.org/0000-0002-1814-6214; http://lattes.cnpq.br/7448379486432052; Filgueiras, Paulo Roberto ; https://orcid.org/0000-0003-2617-1601; http://lattes.cnpq.br/1907915547207861The COVID-19 disease has been and continues to be a global health concern. The identification of infected patients through rapid and efficient screenings remains necessary to contain its spread. Biological fluids, such as serum and saliva, offer ease of collection and provide rich information about molecular changes in the body during illness. The use of mass spectrometry (MS) combined with machine learning (ML) has been applied to biofluids from patients with diseases and controls to identify biomarkers and conduct rapid and effective screenings. Therefore, this thesis aims to present advancements in the search for disease biomarkers, particularly for COVID-19, using technologies based on Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI MS) and Electrospray Ionization Mass Spectrometry (ESI MS), along with chemometric data treatments. To achieve this, a methodology was developed for screening patients suspected of having COVID-19 based on saliva samples, using MALDI MS with the assistance of Support Vector Machine (SVM) learning. This involved optimizing sample preparation and analysis parameters. The most efficient results in a shorter analysis time were obtained by digesting saliva with 10 μL of trypsin for 2 hours. Optimization of the parameters at 1M resolution was ideal for the analyses. SVM models were created using data from the analysis of 149 samples, 97 positive and 52 negative for COVID-19. Two models yielded the best results. SVM1 selected 780 variables with a false negative rate (FNR) of 0%, while SVM2 selected only 2 variables (525.4 Da and 1410.8 Da) with a 3% FNR. Another application of MS in biofluids was the development of a multiomic method for screening patients infected with SARS-CoV-2 based on serum lipid and proteomic profiles. ESI MS was used to investigate the lipid profile of 239 serum samples (119 positive and 120 negative for COVID-19). MALDI MS was used to analyze the proteomic profile of 300 serum samples (150 positive and 150 negative for COVID-19). After processing MS data and variable selection, statistical analyses such as Volcano plot, Heatmap, principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and SVM were performed to distinguish the most relevant variables for classifying positive and negative samples for COVID-19. In lipidomic analyses using ESI(±)-Orbitrap MS and SVM models, sensitivities of 96.67% and 100%, specificities of 82.14% and 96.88%, and accuracies of 89.66% and 98.44% were observed for positive and negative ion mode analyses, respectively. In proteomic analyses using MALDI(+) MS, the linear PLS-DA model demonstrated an accuracy of 99.10%. Thus, the combination of MS techniques with chemometric data treatments has shown promising alternatives with high sensitivity and specificity to discriminate infected and non-infected biological samples by SARS-CoV-2