Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Optimization of synthesis conditions and physicochemical characterization of silver nanoparticles by cell lysate of lactobacillus rhamnosus(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06-13) Flores Otero, Jesús Fernando; Aguilar Jiménez, Oscar Alejandro; emipsanchez; Santacruz López, Yolanda Arlette; Salas Villalobos, Ulises Andrés; School of Engineering and Sciences; Campus Monterrey; Ramos de la Peña, Ana MayelaNanomaterials have been present throughout history and, in 1951, Turkevich prepared metallic nanoparticles through a chemical mechanism for the first time. Many procedures for the synthesis of metallic nanomaterials exist, using both top-down and bottom-up approaches. However, as resources become scarcer and pollution increases, green methods have become more prominent, one such being biogenic synthesis as it takes advantage of biomolecules to replace chemical substances. In this work, silver nanoparticles were synthesized using Lactobacillus rhamnosus lysate to reduce silver nitrate, obtaining silver nanoparticles with a diameter close to 70 nm. The nanoparticles were characterized by Dynamic Light Scattering (DLS), X Ray Diffraction (XRD), UV-Vis Spectroscopy and Scanning Electron Microscopy (SEM). First, X-Ray Diffraction was used to determine the crystal structure and composition of AgNPs for comparison with previously reported studies. Henceforth, UV-Vis Spectroscopy showed an absorption peak at approximately 418 nm, which is within the UV absorption range for silver (400-460 nm). With these results, the nanoparticles were identified as AgNPs. Dynamic Light Scattering was used to optimize both the AgNPs size and size distribution (PDI) using a Factorial Design with three independent variables: reaction temperature, time and precursor (AgNO3) concentration. The optimal conditions for the formation of AgNPs were 60 °C, 60 minutes and 50 x 10-4 M AgNO3 concentration, obtaining an average particle size of 75.64 ± 3.04 nm and a PDI of 0.268 ± 0.014. In addition, zeta potential was measured to define the stability of AgNPs, with the previously mentioned optimized result having a Z-potential value of -36.07 ± 0.74. Scanning Electron Microscopy was used to confirm both the size and morphology of the AgNPs, observing particles of approximately the same sizes as the ones reported by DLS. Finally, the catalytic activity of AgNPs was tested with the degradation of methylene blue with NaBH4.
- Optimization of kinetic and operating parameters in bioreactors using evolutionary algorithms(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Barrera Hernández, Gonzalo Irving; Sosa Hernández, Víctor Adrián; emipsanchez; Alfaro Ponce, Mariel; Aranda Barradas, Juan Silvestre; Corrales Muñoz, David Camilo; School of Engineering and Sciences; Campus Estado de México; Gómez Acata, Rigel ValentínBioreactors play a role in creating biological products such as medicines and biofuels by care fully controlling factors such as substrate levels and temperature within them to obtain optimal production results, bioreactor production process poses a challenge that poses a challenge to engineers due to the intricate setup involved. In the field of microbiology and biotechnology, conventional approaches such as the Monod model, logistic growth models, and fed-batch techniques have been employed to predict and improve the growth conditions of microor ganisms and the production of proteins of interest in fermenters. However, these approaches could face challenges when they encounter nonlinear systems and conflicting objectives. To address these challenges, our suggestion is to approach the configuration of factors in bioreactors as an optimization problem using an evolutionary algorithm that can improve the effectiveness and quality of the operating process. The objective of this study is to in vestigate and create a pipeline that integrates evolutionary algorithms to solve multi-objective and scalar optimization problems, aimed at identifying kinetic and critical parameters within a bioreactor system. The optimization process involves, in the first stage, a least squares ap proach that considers product, biomass, dissolved oxygen, and substrate concentrations as objectives, with the kinetic parameters (e.g., maximum specific growth rate and substrate affinity) serving as decision variables. The second stage focuses solely on maximizing the amount of produced product, specifically biomass, using critical operational variables, such as feed rate and aeration, as decision variables. The research employs Escherichia coli as a microorganism that has been genetically al tered to produce orange fluorescent protein (OFP) to test the validity of improvement frame works. Initially, in the simulation and process tuning phase, experimental information, from batch cultures, is used to accurately determine the factors. Later, in the fed-batch phase, the application of an algorithm is used to optimize biomass yield while considering operational constraints such as oxygen levels and maximum reactor volume. The findings show that this method accurately calculates factors during the fed-batch phase and efficiently increases biomass production in the continuous fed phase. The use of algorithms such as multiple NSGA-III and single-objective genetic algorithms provides valuable benefits when dealing with intricate bioreactor configurations that have conflicting objectives such as managing substrate consumption and improving production yield. This approach has promising prospects for improving the accuracy and efficiency of bioprocess optimization, while increasing its scalability, in the field of biotechnology in the future.