Shear stress computation in a millimeter thin flat panel photobioreactor: Numerical design validated by experiments.

Flat panels are the most spread type of photobioreactors for studying light effects on a microalgae culture. Their low thickness, usually between 1 and 3 cm, aims at ensuring light homogeneity across the culture. Yet because optical density has to remain very low, studies are still limited to low cell density cultures. To alleviate this problem, even thinner photobioreactors can be designed. Nevertheless, thin flat panel reactors are very prone to induce high shear stress. This work aimed at designing a new millimeter thin panel photobioreactor to study light effects on Chlorella and Scenedesmus genera. We proposed a numerical workflow that is capable of assessing the shear stress intensity in such a reactor. The numerical predictions were validated at three different levels: 2D preliminary simulations were able to reproduce bubble commonly known behaviors; close to the nozzle, the predictions were successfully confronted to shadowgraphy experimental reference; at the mini bioreactor scale, experimental and numerical mixing were found to be close. After these throughout validations, shear stress intensity in the photobioreactor was calculated over 1000 Lagrangian tracers. The experienced shear stress was agglomerated at the population level. From the computed shear stress, it was possible to choose the minimal reactor thickness that would not hinder cell growth.

Water vapor transport properties of bio-based multilayer materials determined by original and complementary methods.

To enhance PLA gas barrier properties, multilayer designs with highly polar barrier layers, such as nanocelluloses, have shown promising results. However, the properties of these polar layers change with humidity. As a result, we investigated water transport phenomena in PLA films coated with nanometric layers of chitosan and nanocelluloses, utilizing a combination of techniques including dynamic vapor sorption (DVS) and long-term water vapor adsorption-diffusion experiments (back-face measurements) to understand the influence of each layer on the behavior of multilayer films. Surprisingly, nanometric coatings impacted PLA water vapor transport. Chitosan/nanocelluloses layers, representing less than 1 wt.% of the multilayer film, increased the water vapor uptake of the film by 14.6%. The nanometric chitosan coating appeared to have localized effects on PLA structure. Moreover, nanocelluloses coatings displayed varying impacts on sample properties depending on their interactions (hydrogen, ionic bonds) with chitosan. The negatively charged CNF TEMPO coating formed a dense network that demonstrated higher resistance to water sorption and diffusion compared to CNF and CNC coatings. This work also highlights the limitations of conventional water vapor permeability measurements, especially when dealing with materials containing ultrathin nanocelluloses layers. It shows the necessity of considering the synergistic effects between layers to accurately evaluate the transport properties.

A validated Distributed Activation Energy Model (DAEM) to predict the chemical degradation of biomass as a function of hydrothermal treatment conditions.

This study proposes a DAEM (Distributed Activation Energy Model) approach to predict the chemical alterations of lignocellulosic biomass as a function of hydrothermal treatment conditions. The model is first tuned by an original device allowing the sample shrinkage to be continuously assessed during hydrothermal treatment in saturated water vapor up to 190 °C. The shrinkage dynamic is supplied in the DAEM model as an indicator of the degree of biomass conversion. A set of chemical analyses was performed at selected residence times and treatment temperatures to correlate this degree of conversion with the resulting chemical molecules. A set of functions was then derived from this database to correlate the degree of conversion with the components concentrations. Finally, a validation database was built with different combinations of temperature levels and residence times. The model was proved to be predictive on this new dataset.