Recent studies of self-assembly in binary systems of bio-surfactants, either of microbial origin or saponins extracted from plants, are reviewed. Saponins in water reported in the first section include aescin, glycyrrhizin, and quillaja saponins, while rhamnolipids are discussed in the second section on microbial surfactants. Studies of surface activities are a natural starting point of the characterization of surfactants, but here we focus mainly on physico-chemical and structural properties of self-assembled bulk structures in solution, often characterized by scattering techniques. When quantitative modelling is performed, self-assembly parameters like aggregation numbers, head group areas, and resulting shapes can be followed as a function of physical-chemical parameters like concentration, composition, temperature, or pH. Morphologies include micelles and their structural evolution with addition of other bio- or synthetic surfactants, co-surfactants, proteins or phospholipids.

Le fractionnement du grain de blé, qui permet la séparation de l’albumen amylacé (au cœur) de ses parties périphériques pour produire de la farine, dépend de ses propriétés mécaniques. On distingue, chez le blé tendre, des blés dits « soft », dont l’albumen est friable, et « hard » plus résistant. Ils différent par la présence de gènes codant deux protéines spécifiques, les Puroindolines (PINs) A et B, qui sont de forme sauvage (cas des soft) ou mutés (cas des hard). Ces protéines sont impliquées dans les interactions entre les constituants majeurs du grain (amidon et protéines de réserve qui entourent les granules, capables de former un réseau aux propriétés uniques appelé gluten). Nous avons pu disposer de lignées quasi-isogéniques pour la dureté qui ne différent que par la présence de ces gènes sauvages ou mutées, alors que le fond génétique est identique, et montré que ces seuls gènes affectent les caractéristiques physiques et le comportement au broyage des grains [1,2]. Nous avons pu caractériser in situ les propriétés mécaniques respectives de l’amidon et du gluten et montré leurs différences, contrairement à ce qui est établi dans la littérature, grâce au développement d’une méthode originale où la pointe d’un Microscope à Force Atomique (AFM) est utilisée pour abraser la surface de l’échantillon à analyser [3]. Nous avons également mis en évidence des différences d’interfaces entre les polymères en fonction de la dureté du grain [4]. Enfin, nous avons utilisé la méthode de caractérisation mécanique par AFM par résonance de contact (CR-AFM) pour mesurer le module d’indentation (élastique) au sein de granules d’amidon (échelle de la centaine de nanomètres), moyennant une correction originale prenant en compte la topographie locale [5]. Cette méthode a été utilisée sur des sections de grains (céréales) et graines (légumineuses) de différentes origines botaniques pour comparer les propriétés de leurs amidons.

Soft materials are usually defined as materials made of mesoscopic entities, often self-organized, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterize them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g., tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This roadmap paper intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterization, instrumental, simulation and theoretical methods as well as general concepts.

Small-angle scattering is a commonly used tool to analyze the dispersion of nanoparticles in all kinds of matrices. Besides some obvious cases, the associated structure factor is often complex and cannot be reduced to a simple interparticle interaction, like excluded volume only. In recent experiments, we have encountered a surprising absence of structure factors (S(q) = 1) in scattering from rather concentrated polymer nanocomposites [A.-C. Genix et al, ACS Appl. Mater. Interfaces 11 (2019) 17863]. In this case, quite pure form factor scattering is observed. This somewhat “ideal” structure is further investigated here making use of reverse Monte Carlo simulations in order to shed light on the corresponding nanoparticle structure in space. By fixing the target “experimental” apparent structure factor to one over a given q-range in these simulations, we show that it is possible to find dispersions with this property. The influence of nanoparticle volume fraction and polydispersity has been investigated, and it was found that for high concentrations only a high polydispersity allows reaching a state of S = 1. The underlying structure in real space is discussed in terms of the pair-correlation function, which evidences the importance of attractive interactions between polydisperse nanoparticles. The calculation of partial structure factors shows that there is no specific ordering of large or small particles, but that the presence of attractive interactions together with polydispersity allows reaching an almost “structureless” state.