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The NMR spectra of molecular species in solution mixtures can be separated with diffusion-ordered NMR spectroscopy (DOSY), a ‘virtual chromatography’ approach based on the measurement of translational diffusion coefficients. Classic DOSY experiments, however, require several minutes are not applicable to many important time-evolving mixtures.
Taking advantage of the concept of spatial encoding, we show here that DOSY data can be collected in a single scan of less than one second for several types of out-of-equilibrium mixtures. SPEN provides an acceleration of DOSY experiments by several orders of magnitude. SPEN DOSY pulse sequences are developed, that compensate for convection effects and are suitable for measurements in low-viscosity organic solvents, a requirement to monitor organic chemical reactions. We also show how to collect multiple consecutive scans from short-lived, non-renewable signals produced by dissolution dynamic nuclear polarisation (D-DNP), which is a versatile and powerful hyperpolarisation method. These methodological developments are supported by advanced numerical simulations, based on a Fokker-Plank formalism to describe simultaneously the spin and spatial dynamics. An exemple of hyperpolarised sample is given with a model mixture of small molecules, while the ability to monitor a reacting mixture is illustrated with a diamination reaction in dichloromethane.
The proposed UF DOSY methodology may contribute towards a real-time diffusion NMR analysis of mixtures, to help in the identification of a sample’s components and in the analysis of molecular interactions.

The mechanical properties of dense tissues control many biological processes, from wound healing to embryonic development to cancer progression. In this talk I will discuss recent theoretical work that combines developmental models with active matter physics to describe dense tissue as active materials that exhibit a jamming-unjamming transition tuned by cell shape and cell motility. Cell division and death, as well as mechanical feedback that coordinates cell migration, can modify the transition resulting in novel tissue ``materials’’ properties. These findings may have implications for cell sorting and patterning in wound healing and development.

Data-driven methods, and Machine-Learning in particular, became very popular in many diverse fields due to their unprecedented ability to identify recurring features, causal relations and complex correlation structures. For the same reasons, the application of these methods to the physical sciences has also attracted much attention, though the field is still very much in its infancy. In this talk I will discuss two applications of Machine-Learning to the study of complex systems: First, I will show how data-driven discretization of nonlinear PDEs can produce accurate low-resolution models, effectively providing a coarse-grained equation which accounts for sub-gridscale physics. Second, I will discuss crumpling of thin sheets and how Machine-Learning can be insightful in studying the emergent patterns, by augmenting the dataset with in-silico calculations of a related system - rigid origami. This also suggests a general strategy of applying data-driven methods to experimental systems where data is scarce or expensive.