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INtelligent System for Processing Intensive Reaction Electrochemistry

Project description

Automating green chemistry for cyclopropane synthesis

Cyclopropane rings are crucial intermediates in organic synthesis, yet scalable and sustainable production methods remain underdeveloped. Traditional electrochemical (EC) approaches face challenges like electrode passivation, limiting efficiency and reproducibility. Supported by the Marie Skłodowska-Curie Actions programme, the INSPIRE project is addressing these challenges by integrating EC synthesis into an automated continuous-flow microreactor, enhanced by machine learning. This system optimises reaction conditions, minimises electrode fouling, and improves scalability. Using Bayesian optimisation, INSPIRE refines parameters for higher yields and selectivity while generating real-time data for systematic reaction screening. By merging EC synthesis with automation and AI-driven optimisation, INSPIRE establishes a high-throughput approach for producing cyclopropane rings and other valuable molecules in a greener, more efficient manner.

Objective

This project focuses on developing and implementing an automated organic electrosynthesis process of cyclopropane rings. Electrochemical (EC) methods, which often suffering from passivation phenomena, will be applied in a continuous-flow microreactor integrated into a global chemical robot, addressing critical challenges in the field of synthetic chemistry. Cyclopropane rings are vital intermediates in organic synthesis, yet their scalable production via green and efficient methods remains underdeveloped.

This study aims to optimize EC cyclopropanation with the aid of machine learning algorithms (e.g. Bayesian optimization) to refine reaction conditions and minimize electrode passivation. The key objectives include:
(i) optimizing reaction parameters to maximize yields, conversion rates, and selectivity,
(ii) implementing advanced machine learning tools to address and mitigate foulinga major barrier to EC process scalabilityand,
(iii) developing robust numerical models to enable the scaling up of EC reactions.

The integration of machine learning with EC flow synthesis is expected to enhance reaction efficiency and reproducibility, creating a framework for the systematic screening of reactions and mechanisms, thus reducing the manual experimental time required by traditional optimization methods. The EC microreactor, implemented within automated continuous-flow platforms, will facilitate dataset generation, real-time control over reaction dynamics, and material production, pushing the boundaries of electrosynthetic methodologies.

The ultimate goal is to establish a versatile, high-throughput approach for synthesizing cyclopropane rings and other high-value molecules, paving the way for significant advancements in sustainable synthetic strategies and expanding the potential of EC processes in organic chemistry.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITEIT VAN AMSTERDAM
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 232 916,16
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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