Publications by authors named "V Gerbaud"

Phase diagrams are powerful tools to understand the multi-scale behaviour of complex systems. Yet, their determination requires in practice both experiments and computations, which quickly becomes a daunting task. Here, we propose a geometrical approach to simplify the numerical computation of liquid-liquid ternary phase diagrams.

View Article and Find Full Text PDF

The field encompassing biomimetics, bioinspiration and nature inspiration in engineering science is growing steadily, pushed by exogenous factors like the search for potentially sustainable engineering solutions that might already exist in nature. With the help of information provided by a bibliometric database and further processed with a dynamic network and semantic analysis tool, we provide insight at two scales into the corpus of nature-inspired engineering field and its dynamics. At the macroscale, the Web of Science(WoS) categories, countries and institutions are ranked and ordered by thematic clusters and country networks, highlighting the leading countries and institutions and how they focus on specific topics.

View Article and Find Full Text PDF

We show that the solvent behaviour in both diffusio-osmosis and Marangoni flow can be derived from a simple model of colloid-interface interactions. We demonstrate that the direction of the flow is regulated by a single value of the attractive parameter covering the purely repulsive and attractive-repulsive interaction cases. The proposed universality between diffusio-osmosis and Marangoni flow is extended further to include diffusio-phoresis.

View Article and Find Full Text PDF

Cryogenic transmission electron microscopy of high-pressure freezing (HPF) samples is a well-established technique for the analysis of liquid containing specimens. This technique enables observation without removing water or other volatile components. The HPF technique is less used in scanning electron microscopy (SEM) due to the lack of a suitable HPF specimen carrier adapter.

View Article and Find Full Text PDF

The efficiency of four modeling approaches, namely, group contributions, corresponding-states principle, σ-moment-based neural networks, and graph machines, are compared for the estimation of the surface tension (ST) of 269 pure liquid compounds at 25 °C from their molecular structure. This study focuses on liquids containing only carbon, oxygen, hydrogen, or silicon atoms since our purpose is to predict the surface tension of cosmetic oils. Neural network estimations are performed from σ-moment descriptors as defined in the COSMO-RS model, while methods based on group contributions, corresponding-states principle, and graph machines use 2D molecular information (SMILES codes).

View Article and Find Full Text PDF