Stage Development of self-optimized process for nanocrystals continuous synthesis in milli-fluidics
Stage Development of self-optimized process for nanocrystals continuous synthesis in milli-fluidics.
Automation of continuous reactors has been subject to an increasing interest with the key aim of developing self-optimizing processes and thus making more efficient the development of new products.
At LOF, we have strong a background in the use of continuous reactors based on micro or millifluidics technology for synthesis development. These tools allow a very good control of operating conditions while keeping low the amount of reactant needed. Nevertheless, exploring the experimental space (temperature, flow rates…and so on) to find the best operating conditions can take lot of time that can be greatly reduced through the use of self-optimized processes. In the particular case of inorganic synthesis, the quality of generated crystals is of tremendous importance for their good application and requires a clever methodology to explore operating conditions. In this perspective, self-optimization is believed to be much more efficient in the development of bespoke and high quality nanomaterials than more traditional approaches.
Therefore, the aim of this internship is to develop a fully automated platform and to implement self-optimizing algorithms to perform nanocrystal synthesis in a milli reactor.
Both experimental and theoretical approaches are foreseen:
· As a first step, a review of most used algorithms is expected (simplex, genetic algorithm…and so on)
· On the basis of the work already done in the lab, the student will have to automate the setup to control pumps and sensors. The nanomaterials will be analyzed by fluorescence spectroscopy. The collected signals will have to be interpreted in order to define parameters to optimize.
· Then, self-optimizing algorithm will be implemented and tested on a nanocrystal synthesis.
Master level - Chemical engineering, Physics – Fluid flows, Automation engineering
Strong IT skills are required. Knowledge of Matlab or Python is strongly recommended. Good hands-on capabilities are also expected. Some knowledge on physics of flow is desired but not compulsory.
3-6 months - From February-March to August-September 2017