Ciencias Exactas y Ciencias de la Salud

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Environmental assessment of urban rivers through a dual lens approach: machine learning based water quality analysis and metagenomic characterization of contamination effects
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Fernández del Castillo Barrón, Alberto; Gradilla Hernández, Misael Sebastián; emipsanchez; García González, Alejandro; Pacheco Moscoa, Adriana; Brown, Lee; Oscar Alejandro Aguilar Jiménez; School of Engineering and Sciences; Campus Monterrey; Senés Guerrero, Carolina
Urban rivers are critical ecosystems increasingly threatened by pollution. Effective water quality monitoring and contamination assessment are essential for informed management decisions. The Santiago River, a key hydrologic system in Mexico, has become one of the country’s most polluted rivers, posing significant ecological risks and public health concerns for nearby communities. This study underscores the urgent need for comprehensive environmental evaluation and enhanced monitoring approaches. Chapter one introduces the motivation behind monitoring water quality in highly polluted rivers, presenting the problem statement and contextual background of the Santiago River basin. It outlines the research question and provides an overview of the proposed dual-lens approach: combining water quality analysis via machine learning algorithms with metagenomic characterization of contamination effects. Key contributions of this work to the field are also highlighted. Chapter two reviews global monitoring strategies from highly polluted rivers, focusing on nine rivers across developed and developing countries to offer a comparative perspective on water quality management needs. In Chapter three, regression and classification machine learning models are developed to predict the Santiago River Water Quality Index (SR-WQI), designed as complementary tools to strengthen the current monitoring program. Chapter four analyzes the historical water quality patterns of the Santiago River to identify the most variable and representative data for training machine learning models. This chapter also reveals that redundant data can hinder model performance by leading to overfitting. Chapter five investigates spatial variations in the microbial composition of Santiago River sediments and examines correlations with water quality. Using high-throughput sequencing, potential microbial biomarkers were identified and impacts of physicochemical parameters and heavy metals on microbial communities were assessed. Finally, chapter five highlight the main findings of this thesis and covers some limitations, perspectives for future research and final remarks.
Multiscale-based computer algorithm for predicting fracture toughness enhancement in carbon fiber-reinforced epoxy due to nanoclay addition.
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-11-29) Rivera Santana, Juan Andrés; Guevara Morales, Andrea; puemcuervo, emipsanchez; Otero Hernández, José Antonio; Cárdenas Fuentes, Diego Ernesto; Gómez Vargas, Óscar Armando; School of Engineering and Sciences; Campus Estado de México; Figueroa López, Ulises
As global energy consumption increases at the same time humanity faces the ever-increasing threat of climate change, among other things, increasingly longer wind turbine blades are required, which translates into a need for structurally more resistant materials. To achieve this purpose, a light material capable to withstand short- and long-term loading is required. Therefore, in this work the use of nanoclays is proposed to reinforce a high-modulus and high-strength material, such as carbon fiber reinforced epoxy. The main idea behind this proposal is that nanoclays are capable of increasing the material stiffness while retarding the propagation of cracks, ultimate responsible for material failure, all this without significantly increasing the overall material weight. Now, as a first step to evaluate the effectiveness of the proposed material, a computational algorithm capable of evaluating the stiffness and fracture toughness improvement in carbon fiber reinforced epoxy materials due to nanoclay addition, is proposed in this work. For the estimation of the improvements in the material engineering constants, the unit cell homogenization technique is employed at two levels: nano- and microscopic. Subsequently, these results are used for the evaluation of fracture toughness enhancement, which also requires the individual numerical evaluation of each mechanism: debonding, plastic yielding and shear banding. Starting with the evaluation of the debonding effects on fracture toughness, an algorithm based on the bisection method is used first to find the critical debonding radius and then the corresponding mechanism. Likewise, this critical debonding radius is used to numerically evaluate plastic yielding, together with the corresponding material stress-strain curves. Subsequently, the bisection method is used once again to find the shear banding critical radius and thereby quantify the corresponding mechanism. Finally, the contributions of the three mechanisms are added to obtain the total fracture toughness enhancement. In addition, the algorithm proposed in this work proves that nanoparticles play an important role in stiffening the carbon-reinforced epoxy material, but always playing a secondary role with respect to carbon fibers. However, the most important role of nanoclays is found in the fracture toughness enhancement of CFRP. Said enhancement is generated by energy mechanisms related to the interactions between the clay and its surrounding matrix, effectively hindering the crack propagation process, which in the long run translates into increased durability for the material. For its part, the resin holds all the material together and serves as the primary transmission medium for the fracture toughness enhancement mechanisms. Finally, the results obtained by the algorithm are consistent with the experiments and analytical models available in the literature.

Ciencias Exactas y Ciencias de la Salud

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