Optimisation of enzymatic degradation of PLA for the production of biohydrogen (BioH2) through photofermentation.
| ABG-136118 | Thesis topic | |
| 2026-03-01 | No particular funding (self-financing, teaching hours, teaching fellowship) |
- Energy
- Biochemistry
- Biotechnology
Topic description
This thesis project proposes an innovative approach to producing biohydrogen (BioH₂) from the enzymatic degradation of polylactic acid (PLA), a bioplastic that is difficult to recycle. The aim is to optimise the hydrolysis of PLA into lactic acid, which can be metabolised directly by purple non-sulfur bacteria (PNSB) to produce BioH2 under anaerobic conditions. The work will consist of selecting high-performance esterases in collaboration with Génoscope CEA, expressing them in soluble form in model hosts such as E. coli, yeasts and PNSB, and optimising the reaction conditions (pH, temperature and concentration) to maximise lactic acid production. A second phase will aim to improve photofermentation in a photobioreactor (PBR) equipped with advanced control systems (LED, AI and CFD). Funded by the CEA and PUI Grenoble Alpes, this project is part of a circular economy approach, aiming to develop a scalable process for converting PLA waste into renewable energy in line with the challenges of the energy transition.
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Funding further details
Presentation of host institution and host laboratory
Le laboratoire Chimie et Biologie des Métaux (CBM) mène des travaux à l'interface de la chimie et de la biologie. Ces travaux portent sur l'étude de la structure, de l'activité et de la régulation des systèmes biologiques complexes transportant et utilisant des ions métalliques, ainsi que sur l’exploitation de ces connaissances pour élaborer des systèmes bio-inspirés possédant des structures et/ou des fonctions similaires.
https://www.cbm-lab.fr/Pages/BEE/Presentation.aspx
Website :
PhD title
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Graduate school
Candidate's profile
Master II: biochemistry, microbiology or molecular biology.
Skills in microbial culture and biochemical analysis.
Knowledge of modelling (CFD) and process control (AI) appreciated.
Motivation for teamwork and interdisciplinarity.
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