STEADY STATE FREE PRECESSION RELAXATION MAPPING WITH DEEP LEARNING IN HIGH-RESOLUTION NMR

SIN RESUMEN

This research project aims to develop a novel deep learning (DL)-driven framework for accurate and efficient relaxation time mapping using Steady-State Free Precession (SSFP) techniques in high-resolution NMR spectroscopy. Recognizing the limitations of traditional methods, such as sensitivity to experimental parameters and the complexity of data analysis, this project seeks to leverage the power of DL to overcome these challenges.

OE1:Develop and train a robust deep learning model for estimating NMR relaxation times. OE2:Evaluate and optimize the performance of the deep learning model using theoretical models. OE3:Demonstrate the applicability of the deep learning framework to a diverse range of NMR applications, including biomolecular NMR (carbohydrate and amino acids) and materials sciences (polymers) matrices.

Investigacion basica

Disciplinario

CONCURSO ANUAL DE INVESTIGACIÓN

• 11 — Ciencias analíticas

Ciencias naturales - Química - Química analítica

PONTIFICIA UNIVERSIDAD CATÓLICA DEL PERÚ