Multiphasic systems have broad applications in pharmaceutical, coating, food, and cosmetic industries among others. The possibility to tailor the structure of such systems by controlling the interfacial properties expands their potential to many other fields, including, for example, tissue engineering and energy storage. The proposed project is dedicated to the study of interfacial polymer complexation as a promising way for fine-tuning and super-stabilization of the structure in liquid dispersions such as emulsions. The research strategy encompasses the investigation of the interfacial behaviour of polymers carrying complementary complexing moieties, a systematic study of the stability and properties of common emulsions stabilized by these polymers, and the development of materials with bicontinuous liquid architecture. Such dual-functional systems have a great potential in acting as cross-flow microreactors and simultaneously, as hydroformylation catalyst of higher olefins. Moreover, the findings of the project will bring an important contribution to the insight into polymer interfaces and soft materials science.

The physical understanding of particle transport within liquid-saturated porous media is a prerequisite in many environmental and industrial fields, such as transport in soil, aquifers, petroleum rocks, building materials and catalysts. Currently the leading field for tracking particle dynamics in model solid porous media is based on optical microscopy techniques, which require optically transparent samples. Our objective is to follow the movement of particles, down to the colloidal size, inside a non-transparent porous matrix, akin to natural porous matrices such as soil. We intend to use the latest developments in X-ray based techniques, both in the real and reciprocal space, X-ray imaging and X-ray photon correlation spectroscopy. Complexity of the system is developed along two lines: (a) nature of the mobile particles (size, density, passive/active particle), which decides the balance between directional motion (such as gravitation-induced sedimentation) and stochastic motion of the particles (Brownian motion) and (b) nature of the confining matrix, especially in terms of varying structural anisotropy of the confining matrix introduced via a clay component (model soils). The first part of the project consists of methodological developments, especially those related to a novel analysis of image sequences yielding the intermediate scattering function (ISF). We regard ISF as a central physical quantity, where real and reciprocal space techniques can meet. The second part of the project explores particle mobility in non-transparent matrices going in the direction of model soils. A third, parallel exploratory part deals with active particles, for which there are several similarities with sedimenting particles, in terms of ISF analysis.

Quantum hardware has been steadily progressing in the last years. Amongst the highlights, a few of the most advanced approaches: superconducting qubits, cold atoms, ions and photonic qubits, have recently overcome the symbolic 100 qubit mark. Yet, the search for new physical and material platforms which could revolutionize the field by providing new functionalities, or solving some of the remaining technological blockages, remains very active. In MolEQuBe we propose to explore a novel quantum material platform combining the promising optical and spin properties of rare earth ions with the unique ability of synthetic molecular chemistry for creating tailored atomic arrangements and functional supramolecular architectures compatible with SC resonators and photonic devices. This fine control will be exploited at different levels, to limit decoherence mechanisms from molecular vibrations and magnetic noise, to engineer qubit-qubit interactions and qubit-host interactions, and to accurately tune the qubit’s optical and spin transition energies. To achieve these ambitious goals, MolEQuBe gathers a consortium of experts with unique complementary competences and experimental capabilities including rare-earth-molecule chemistry, coherent and high-resolution optical spectroscopy, coherent optical storage, superconducting technologies, and microwave single-spin measurements. During the project, we will target three main objectives: (i) We will synthesize RE molecular materials that combine the unmatched narrow optical and spin linewidths of RE ions with the highly deterministic design capabilities provided by molecular chemistry to design scalable optically addressable multi-qubit platforms. (ii) We will demonstrate single molecular spin detection by depositing optimized RE molecular materials onto high-quality-factor superconducting (SC) microwave (MW) resonators and using MW photon counting. We will then harness this single molecule detection capability to carry out two-qubit gates using a small nuclear spin register within the molecule. (iii) We will create novel integrable quantum memory platforms, fabricated in low-loss polymer waveguides featuring high bandwidth and containing optimized 171Yb molecules. We will demonstrate optical-to-spin coherence transfer, and single photon level storage on RE ensembles using the atomic frequency comb memory protocol. The strong potential and feasibility of MolEQuBe’s approach is supported by recent pioneering work by the partners. On the one hand, the IRCP and ISIS teams recently reported 30 kHz optical linewidth in an isotopically pure highly concentrated Eu3+ molecular crystal (optical T2 of 10 µs). This breakthrough represents three to four orders of magnitude line narrowing over molecular centers reported so far. At the same time, the achievement of single Er3+ spin detection using a scheelite crystal by the SPEC partner opens the way for quantum demonstrations with molecular qubits.

Biotherapeutics play a critical role in the treatment of human diseases and their market is expanding rapidly. Most biotherapeutics are expressed as recombinant glycoproteins where glycosylation controls properties such as immunogenicity, stability and bioactivity. Thus, glycosylation can directly affect the drug quality, safety and efficacy and must be adequately analyzed and controlled throughout the R&D and manufacturing processes of the drug. Lectin microarrays are very promising as high throughput strategy for assessing glycan patterns of biotherapeutics since lectins can recognize selectively glycan epitopes. We want to develop with GLYCODIAG, a company specialized in glycoprofiling, an array to detect the major undesirable glycanic structures in biotherapeutics and to allow real-time monitoring and control. A panel of solely recombinant lectins, novel strategies for biomolecules conjugation and array immobilization will be elaborated prior validation on model glycoproteins.

Materials science contributes to facing the challenges of our society and particularly the climate change by delivering highly optimized structural and functional materials. The relevant material classes range from structural materials like superalloys and light-weight steels for increased efficiency in aviation, automobiles and power generation to functional materials like solar cell and battery materials for electric-power generation and storage respectively. The key challenge for materials design and optimization is the prediction of the thermodynamic stability of a compound, i.e. the prediction of the energetically stable crystal structure from only its chemical composition. The enormous numerical effort involved in this prediction sets a fundamental limit on the computational exploration of materials. While density-functional theory calculation (DFT) revolutionized materials science and provides highly reliable predictions of thermodynamic stability, it is still too computationally expensive to explore the chemical and structural complexity that is needed for many technologically relevant materials. With the advent of data-driven scientific discovery, we are witnessing the change to the fourth paradigm in science. The enormous potential of data exploitation with artificial intelligence (AI) will change many fields of our society ranging from health to environment and technology. One of the fields that are changing gears with AI already now is materials science. In this project, we apply modern AI techniques to the prediction of the thermodynamic stability of intermetallic phases that play a central role in superalloys and light-weight steels. As a novel and unique approach, we will develop and apply a hybrid-AI ecosystem with broader applicability, in which descriptors will be designed to include (i) physical properties like atomic radius and valence electrons number, (ii) local geometric information, (iii) qualitative domain knowledge of interatomic interactions in terms of physical models, (iv) quantitative domain-knowledge of chemical bond-formation from DFT, and (v) quantitative domain knowledge on the structure-energy relation. These descriptors will be used with different regression models in combination with dimensionality reduction, hyper-optimization and importance analysis. This enables us to construct hybrid-AI models that are sufficiently robust to explore chemically and structurally complex topologically close-packed phases that are not accessible otherwise. The predictions of the hybrid-AI on structural stability and sublattice occupancy of these intermetallic materials will be confirmed by experimental measurements within this project. We expect that this hybrid-AI for materials will initiate new promising directions of research for the benefit of materials science in France and Germany.