Real-Time Microfluidic Deconstruction and Predictive Modeling of Lignocellulosic Biomass

Context:

Lignocellulosic biomass (LB) is a strategic renewable carbon feedstock mainly composed of cellulose, hemicelluloses and lignin. While rich in fermentable polysaccharides, these carbohydrates are tightly embedded in a lignin matrix that makes LB highly recalcitrant to enzymatic deconstruction. Current industrial valorization routes rely on harsh physicochemical pretreatments that are energy-intensive, may degrade sugars, and generate inhibitory by-products. Developing mild, low-energy, enzyme-compatible alternatives therefore represents a major scientific and technological challenge for sustainable biorefineries.

Microfluidics offers unprecedented control over temperature, pH, residence time and hydrodynamics while drastically reducing reagent consumption. It also enables high-throughput screening of enzymatic conditions and time-resolved monitoring of structural and kinetic phenomena. However, a major bottleneck remains: the lack of quantitative, time-resolved correlations between LB micro-architecture evolution, chemical modifications and hydrolysis kinetics at the cell and tissue scales.

This thesis aims to overcome this limitation by developing, for the first time, an integrated microfluidic platform enabling sequential pretreatment → neutralization → enzymatic hydrolysis on the same micro-structured biomass sample within a single microfluidic chip. The approach will be applied to a representative diversity of resources, including softwoods (spruce), hardwoods (poplar), and agricultural residues (wheat straw), allowing generic insights into biomass-dependent reactivity under mild conditions.

Objectives:

This PhD thesis is part of the µLB-Predict project at the interface between biochemical engineering and fluid mechanics funded by the Graduate School SIS of Université Paris-Saclay.

The objectives of the study are as follows:

• Develop an integrated microfluidic platform for mild pretreatment and enzymatic hydrolysis of lignocellulosic biomass, including optimization of pretreatment strategies and enzyme cocktails;
• Characterize biomass deconstruction mechanisms by coupling confocal imaging with saccharification kinetics and establishing correlations between structural evolution and hydrolysis performance;
• Build predictive digital twin integrating structural and kinetic descriptors to model conversion dynamics and identify rate-limiting steps using MATLAB/Python tools.

To carry out these tasks, the successful candidate will benefit from the complementary expertise of the LGPM (Chair of Biotechnology, CentraleSupélec) and UMR SayFood (INRAE, AgroParisTech) laboratories. The project, which will take place on both sites, will combine microfluidics, advanced microscopy, biomass chemistry and numerical modelling. Written and oral communications in English will be required throughout the thesis. The PhD student will enroll in the INTERFACES doctoral school of Université Paris-Saclay and will receive both scientific and transferable skills training during the PhD.

The Ph.D. thesis will be carried out within a collaboration between two laboratories of the Paris-Saclay research ecosystem, the Chaire de Biotechnologie, Laboratoire de Génie des Procédés et Matériaux (LGPM), CentraleSupélec, and UMR SayFood, AgroParisTech, both members of the Graduate School Sustainable Innovation and Systems (GS-SIS). These laboratories bring complementary expertise covering process engineering, biotechnology, and the transformation of bio-based materials.

The Chaire de Biotechnologie, Laboratoire de Génie des Procédés et Matériaux (LGPM), CentraleSupélec, located in Pomacle (20 km from Reims) conducts research at the interface of biotechnology, process engineering, and digital twinning, with the objective of linking experimental observations to mechanistic models for process understanding, optimization, and scale-up. 

UMR SayFood, AgroParisTech is located in Agro Paris-Saclay campus, Palaiseau, and focuses on the science and engineering of products derived from agricultural resources, studying the relationships between transformation processes, composition, structure, and functional properties. 

The PhD student will carry out the project within the two partner research units.

We are looking for a student with a master’s degree (or equivalent, five years of higher education) in Chemical Engineering, Biochemical Engineering, Process Engineering, Biotechnology, Fluid Mechanics, or any related discipline.

The successful candidate must be autonomous, scientifically curious and motivated to work at the interface between experimentation and modelling. Strong interest in biomass valorization and sustainable processes is expected. Basic programming skills in Python or MATLAB, lab work experience, and interest in image analysis and modelling would be highly appreciated. The candidate must be able to read and write fluently in English.