H2020Индивидуална стипендия2016–2018

QUNS · Quantum-Statistical Methods for Nuclear Singlet States in Complex Fluids

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2016-03-14 → 2018-03-13
Финансиране от ЕС: 179 326 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Квантово-статистически методи се използват за анализ на молекули с дълготрайни спинови състояния в сложни среди като биомембрани. Това помага за подобряване на качеството на изображенията при магнитен резонанс (MRI) и точността на химичните анализи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Quantum-Statistical Methods for Nuclear Singlet States in Complex Fluids

Nuclear magnetic resonance (NMR) and the related method of magnetic resonance imaging (MRI) has important applications in chemistry and medicine respectively. In chemistry NMR provides molecular structure information and information of dynamics. From MRI, images of soft matter are obtained thus playing an important role in medical investigations. However, NMR/MRI are insensitive methods with weak signal at physiological temperatures, limiting studies to molecules present at high concentration (for instance water if we want an MRI image). Thus there is an interest in delivering molecules that can provide enhances signal, so called hyperpolarized molecules. Within this topic two main computational challenges are identified that are required for in depth understanding and development of applications, namely (i): how can quantum chemistry assist in providing detailed information of NMR relaxation processes and (ii): development of molecular dynamics models that with sufficient accuracy can model the slow processes, required to understand NMR relaxation in complex media. These are the challenges addressed in the proposal. The first paper provide simulation-methods and the prediction of the time constant for a long lived spin state (LLS) and thus contributes to challenge (i). With this theoretical understanding of LLS, we can learn how to design molecules with LLS and thereby obtained a delivery vehicle for hyperpolarized molecules that in turn gives us improved MRI imaging or NMR results. A second paper address challenge (ii), and provides a simulation technique to, at sufficiently long lengths and timescales, compute the relaxation in biomembrane model systems. With these tools at hand the long-lived spin state can in future work be developed to play an important role in MRI imaging as well as materials research and thus benefit the society as a whole.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) are supremely important techniques with widespread applications in chemistry, physics and medicine. NMR methodology has until recently been limited by the time constant T1 for the decay of nuclear spin magnetization back to thermal equilibrium. Long-lived nuclear singlet states (LLS) have been shown to overcome this limit with a decay constant TLLS that may be two orders of magnitude longer than T1. However, so far mostly ideal systems have been studied in the LLS context, involving simple solvents and oxygen and other paramagnetic molecules removed. This is far from the conditions in many potential applications of MRI and/or materials research, and it is not clear how LLS performs in environments such as complex fluids or biological matter. To overcome this limitation, the proposed project is to develop state-of-the-art quantum-statistical simulation methodology toolbox to model TLLS in complex fluids (lipid/water phases). The project builds on the experience of the research fellow in LLS and computational engineering combined with quantum-chemical, molecular simulation, and experimental expertise of the host institution. Methodology for the essential but challenging quadrupole and paramagnetic spin relaxation enhancement will be developed for LLS. Machine learning techniques will overcome the excessive computational burden of very many quantum-chemical calculations needed in conventional computational relaxation studies.The simulated TLLS will provide a general understanding of the applicability of LLS at the microscopic level, for colloidal systems. The theoretical understanding will guide the development of LLS in materials research and MRI. Machine learning development will feed into the quantum chemistry studies of NMR and other molecular properties in complex systems, as well as computational engineering.

Оригинален текст от CORDIS (на английски).

Участници

OULUN YLIOPISTO · OuluКоординаторФинландия