Functionalized amines are high-value materials with numerous applications as therapeutic agents, agrochemicals and organic materials. The development of strategies for enabling the fast and divergent synthesis of these material can have a positive impact to our ability to discover, manufacture and evolve molecules of high-relevance to our society. In this proposal we present an innovative approach for the functionalization of homoallylic amines based on their coordination borane (BH3). This will provide access to novel boryl radical species that we will exploit in cyclization-functionalization cascade. This novel reactivity mode will convert the homoallylic amine into a cyclic borylated and functionalized building block that can be further diversified across the sp3 C–B bond by oxidation or Suzuki-Miyaura cross-coupling. Overall, this strategy will constitute a divergent platform for the diversification of high-value amines and also a novel retrosynthetic tactic for the preparation of high-value and structurally complex drug analogues. This research squarely fits within the expertise of the Leonori group in the generation and use of boryl radicals in synthesis and catalysis. The completion of such an innovative and ambitious project at RWTH Aachen University will be facilitated by generating, transferring, sharing and disseminating knowledge, and will enhance the Researcher future career following the training plan envisioned.

Ionic conducting materials form the basis of solid oxide fuel cells, solid oxide electrolyser cells, and batteries, which form a key component of the EU Energy 2050 long-term strategy. There is a need to develop faster ionic conductors, however progress has been slow. Recently, several studies have demonstrated photo-enhanced iodine-ion diffusion as well as suggested interactions between photons and oxygen-ion defects in oxide materials may be possible. The proposed research plan, OPTICS, aims to address the question: Can changes in ion transport be enhanced in technologically relevant ionically conducting oxides (O-ion, H-ion, and Li-ion) by light illumination? There are challenges in studying these effects using conventional methods. Namely, absorption only occurring at the surface in thick samples, artefacts in conductivity measurements stemming from photocurrents and electrode effects, and difficulties understanding the mechanisms due to the indirect nature of photon-ion interactions. In OPTICS, these challenges will be overcome employing isotopic tracer diffusion measurements on epitaxial thin films carried out in tandem with atomistic and continuum simulations to identify the underlying mechanisms. Combining the Host’s (Prof. Roger De Souza) expertise in tracer diffusion and atomistic modelling with the Applicants experience with optical measurements on epitaxial films, photo-enhanced ionic diffusion will be studied experimentally and computationally in the bulk, at surfaces, and at interfaces of nanostructured materials. Light-enhanced ionic transport has the potential, though OPTICS, to lead to substantial improvements in technologically relevant ionic conductors leading to a new class of photo-ionic fuel cells, electrolysers, and batteries.

This project will develop a unifying framework of novel methods for sequence classification and thus make a major break-through in automatic speech recognition and machine translation, advancing these areas of human language technology (HLT) beyond state-of-the-art. Despite the huge progress made in the field, the specific aspect of sequence classification has not been addressed adequately in the past research in these disciplines and remains a big challenge. The proposed project will provide a novel framework under consistent consideration of the leading aspect of sequence classification. It will break the ground for a deeper, more comprehensive foundation for sequence classification and pave the way for a new generation of algorithms that will put human language technology on a more solid basis and that will accelerate progress in the field across several disciplines. The leading research objectives are: 1. A novel theoretical framework for sequence classification. 2. Consistent sequence modeling across training and testing, which is specifically lacking in machine translation. 3. Adequate sequence-level performance-aware training criteria to learn the free parameters of the models. 4. Investigation of (true) unsupervised training for HLT sequence classification: its principles, its prerequisites, its limitations and its practical usage. The study of these four problems will provide key enabling techniques for HLT sequence classification in general that will carry over to and create high impact on the areas of speech recognition, machine translation and handwritten text recognition. Using our top-ranking research prototype systems, we will verify the validity and effectiveness or our research on public international benchmarks.