Phenoxyimines (FI) and Salen are ubiquitous ligands that are widely used, particularly in organometallic catalysis, with various industrial applications. However, one inherent drawback of their structure is the presence of electrophilic imine(s), which can react and cause a deleterious drop in catalytic performance. MORFAL aims at developing a related class of FI and Salen ligands, where the imine function will be replaced by a trisubstituted amidine giving rise to new phenoxy-amidine ligands (FA). The amidine moiety should provide higher stability and electron-donating ability making FA ligands particularly well suited for stabilising highly reactive cationic or neutral metal species. Considering that the hard character of FA ligands (N,O donor atoms) is best suited for early metals, the coordinating ability of FA ligands will be studied with group 2-4 elements. FA group 4 metal complexes will be targeted for the production of UHMWPE and for the stereoselective polymerisation of propylene. The use of FA ligands more tolerant to alkylating or reducing agent than FI should facilitate the access to well define active cationic species for olefin polymerisation and prevent alkylation and/or reduction of the ligand backbone during the polymerisation reaction. The sigma and ?-donor ability of FA ligands should also contribute to enhance the thermal stability of the active cationic species and thus allow to maintain high catalytic performance when more drastic reaction conditions (temperature, pressure) are required. MORFAL aims also at developing FA-based catalysts for hydrophosphination reaction from early transition metals or even the large alkaline earths Ca-Ba. Special attention will be paid to promote asymmetric catalytic HP using chiral FA early metal complexes. FA ligands are expected to be more suitable than FI for this reaction, which otherwise can undergo intramolecular hydrophosphination.

Innovations in food packaging mostly concern food shelf-life and consumer safety by the inhibition or prevention of microbial growth onto food, thanks to the development of antimicrobial active packaging. In particular, bio-based biodegradable polymers and antimicrobial natural compounds generate a growing interest in the sustainability of packaged food. The aim of NanoBAP project is to investigate the potential of electrospun nanofibers in the field of active packaging, through the development of an antimicrobial and antioxidant coating based on biosourced materials for the combined release of multiple natural active compounds from a PLA film. Two strategies based on electrospinning will be fully investigated: from the design and characterisation of physico-chemical properties of the coated films and the release/transfer mechanisms of active compounds up to the evaluation of in vitro and model/simplified food antimicrobial activity. The final innovative proposed packaging solution would be of key importance for the packing of sliced or textured fresh foods. In conclusion, the outcome of this project will generate fully bio-based and biodegradable active films with the potential to substantially mitigate plastic pollution and to reduce food waste. This will make a both scientific and economical step forward to “zero waste” concept.

Intelligent vision systems are more and more important in a lot of applications nowadays, ranging from security at home, in cars, navigation of drones and robots, … but their practical applicability is still limited by the large computing system they require. The IRIS project exhibits three facets. The first facet opens a new way by proposing new bio-inspired algorithms for the realization of a stand-alone vision chip for both optic flow (retinal slip speed) measurement and contrasting target localization over a wide luminance range. New algorithms are required because classical algorithms are efficient, but require a large amount of computation (and power) that limit their usability in such applications. Insect-based vision is not used in mainstream applications (pattern recognition, image recording) because it has comparatively lower resolution than vertebrate vision but it evolved to support accurate control of navigation in complex, 3D, dynamic environments. More than fifty years of insect research indicate that it is extremely efficient to detect motion-related events that occur when the animal moves in 3D space. In this project, we propose to design and realize innovative algorithms adapted to the new class of 3D vision chip with 3 stacked layers: perceptive, pre-processing and computing layers. Many studies in robotic field and insect behavior have highlighted the key role of optic flow measurement for visual navigation. Recent studies carried out at ISM in Marseille (Biorobotic department) have shown that robust optic flow measurement can be achieved by merging the output signal of a few number of insect-based motion detectors. This result combined with a technology of vertically integrated retina using 3D-stacking technology (developed by CEA), opens a promising avenue toward the implementation of fast and smart retina featuring a high fill factor, low power consumption, high flexibility and large computational resource without sacrificing the size of the overall chip. This first IRIS facet will involve ISM, LEAD, CEA-LIST and the industrial partner Novadem to determine the specifications of a new vision chip with unique features in terms of size, programmability and computational resources. The second facet will be focused on the development of new visual processing algorithms for motion detection and object localization. These last ten years, many studies on primate visual cortex yielded novel bio-inspired models concerning rapid object categorization and recognition. The second objective of the IRIS project will aim at combining bio-inspired object categorization algorithms (extension of HMAX that will work robustly in the presence of complex scenes and developed by LEAD) with optic flow processing (developed by ISM) in order to improve the performance of the visual processing to detect and track a moving object of interest. This second facet will include a crucial benchmarking of the selected visual processing algorithms that will be implemented into the IRIS processors. The third facet takes its place at the crossroad between the design of a novel vision chip and the use of novel control laws for the visual guidance of an aerial vehicle. Among an increasing number of flying robotic platforms, achieving collision-free vision-based piloting in cluttered indoor and outdoor environments is still a tricky challenge, even with relative large computational resources such as those offered by the IRIS sensor. Much research efforts must be deployed for the use of vision to tackle the problems involved in vital tasks and swift decisional tasks such as obstacle avoidance, odometry and path planning. It is precisely the main objective of this third facet to combine innovative visual sensors with insect-based control laws to improve the ability of future aerial vehicles (provided by Novadem) to avoid obstacles or track moving target in a natural world indoors or outdoors.

The formulation of healthy food acceptable by the consumer is penalized because flavor perception, which is built in our brain through integratory processes and which mainly drives food acceptability, is so far still unpredictable. In AROMA project I will investigate one strategy of sugar/salt reduction using aroma (OITE) -with a focus on a specific population (obese vs normal-weight), to unravel the key brain mechanisms of mental representation of food (flavor perception). This system will be investigated with a set of complementary brain imaging technics and a multidisciplinary approach: from food model formulation to brain imaging, including sensory evaluation. AROMA will provide valuable insights and new results: (1) on the key mechanisms of flavor perception through the prism of obesity and (2) on the validation of OITE as a strategy to reduce salt and sugar in food for a variety of consumers and (3) on proofed method in Neurosciences to study OITE.