Se encontraron 2 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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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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