Se encontraron 3 investigaciones
Xanthan gum (XG), a polysaccharide produced by Xanthomonas campestris, exhibits remarkable rheological properties and finds diverse applications, ranging from food and oil drilling to medicine. XG's performance is influenced by its conformational changes between helical and random coil states, which are responsive to changes in pH, ionic strength, and temperature. This research proposal aims to elucidate the underlying mechanisms of these supramolecular assemblies through rheological and Nuclear Magnetic Resonance spectroscopic studies. 1H-NMR relaxometry, a powerful technique for studying molecular dynamics, will be employed to investigate the conformational changes of XG. By measuring the longitudinal (T1) and transverse (T2) relaxation times, we can elucidate the structural details of XG in solution. Additionally, diffusion-ordered spectroscopy (DOSY) will provide direct insights into molecular mobility and aggregation processes. To gain a deeper understanding of the cross-linking mechanisms, NOESY experiments will be performed to identify the crucial residues involved in intra- and intermolecular interactions. By combining relaxometric and rheological data, we anticipate developing predictive theoretical models that will significantly advance our understanding of polymer behavior and facilitate the design of novel materials. Therefore, this research will bridge the knowledge gap between supramolecular conformation and cross-linking in macromolecules, paving the way for the design of advanced materials for applications in the health sciences, ultimately leading to breakthroughs in fields like drug delivery and 3D-printing for tissue engineering. Ultimately, by unlocking critical chemical information, this proposal will pave the way for groundbreaking polymer research in Peru. This will elevate the local capacity in the field of macromolecular dynamics, with a focus on future in vivo applications.
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Este proyecto tiene como objetivo evaluar el potencial de valorización de relaves mineros generados en las operacionesde Nexa Resources (Unidad Minera de Atacocha) como insumos para la producción de materiales cementicios y geopoliméricos destinados a aplicaciones en infraestructura minera. La investigación incluirá una caracterización integral de los residuos en sus dimensiones física, química y mineralógica. Se analizará la reactividad de los relaves en medios alcalinos y en mezclas con cemento Portland, identificando las condiciones óptimas para su acondicionamiento, activación alcalina y sustitución parcial de clínker. Con base en estos resultados, se formularán y fabricarán mezclas piloto tanto de cementos con reemplazo como de sistemas geopoliméricos, que serán sometidas a ensayos mecánicos (resistencia a compresión, tiempos de fraguado) y de durabilidad (ataque por sulfatos y hielo-deshielo) para validar su desempeño. Adicionalmente, se realizarán estudios de relajación por Resonancia Magnética Nuclear en el Dominio del Tiempo (RMN-DT), que permitirán comprender mejor la movilidad del agua y los cambios microestructurales de los materiales con relaves. Asimismo, se ejecutarán ensayos de lixiviación en morteros hidráulicos y geopoliméricos para verificar el cumplimiento de la normativa vigente y asegurar la inocuidad ambiental de los productos. Finalmente, se explorarán aplicaciones industriales de interés para las operaciones mineras, como concreto lanzado (shotcrete), relleno en pasta (backfill) y adoquines, evaluando su viabilidad técnica y su potencial de implementación a escala piloto. El proyecto busca generar conocimiento técnico que permita establecer rutas de valorización específicas para cada tipo de relave, contribuyendo a la reducción de pasivos ambientales, la disminución de emisiones de CO asociadas a la producción de clínker y la promoción de soluciones constructivas sostenibles, bajo un enfoque de economía circular aplicado al sector minero.
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Steady-State Free Precession (SSFP) in Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful technique for acquiring high-resolution spectra. However, accurately determining longitudinal (T1) and transverse (T2) spin relaxation times from SSFP data presents a significant challenge due to the complex interplay between signal intensity and numerous experimental parameters. To address this challenge, this research proposes a novel deep learning (DL)-driven framework for directly mapping simultaneous T1 and T2 values from SSFP datasets. This approach overcomes the limitations of traditional methods, such as inversion recovery and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence, by employing deep learning routines to model spin dynamics and accurately map relaxation times. These architectures will be trained to learn the complex relationships between experimental parameters (flip angle (θ), RF pulse duration, repetition time (TR), echo time (TE)), SSFP signal intensities, and the underlying relaxation times (T1 and T2). Furthermore, this DL-based approach offers several key advantages, including enhanced accuracy and efficiency, improved robustness against experimental noise and artifacts, and increased flexibility for adapting to diverse SSFP pulse sequences and experimental conditions. Consequently, this research has the potential to significantly advance NMR spectroscopy by enabling more accurate and efficient relaxation time measurements using SSFP, thereby accelerating research across diverse fields, including biomolecular NMR, materials science, and metabolomics.
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