Nuada's novel optimised MOF reactors for CO2 capture

Project Information

NUACO2

Grant agreement ID: 101218725

DOI

10.3030/101218725

EC signature date 6 May 2025

Start date 1 June 2025

End date 31 May 2027

Funded under

• The European Innovation Council (EIC)

Total cost

No data

EU contribution

€ 2 493 418,85

Coordinated by

MOF TECHNOLOGIES LIMITED
United Kingdom

Periodic Reporting for period 1 - NUACO2 (Nuada's novel optimised MOF reactors for CO2 capture)

Reporting period: 2025-06-01 to 2026-05-31

Summary of the context and overall objectives of the project

Hard-to-abate industries remain major contributors to global carbon emissions, posing a significant challenge to climate goals. Traditional carbon capture methods often struggle with efficiency and deployability at scale in these sectors. The EIC-funded NUACO2 project aims to change this. Led by Belfast-based technology provider Nuada, the project is pioneering their next-generation point-source carbon capture solution tailored for foundational industries. Central to NUACO2 is the development and validation of innovative metal-organic framework reactor designs for faster and more efficient carbon capture while paving the way for upscaling to full industrial deployment. By advancing this cutting-edge technology, NUACO2 will make a crucial step forward towards significantly reducing emissions where it is most difficult to do so.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the first periodic report NUADA has made great progress and is on track to deliver all project deliverables and milestones within the planned schedule.
The key activities performed include:
- Reactor design and optimisation.
- Systematic lab scale testing and validation.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

NUADA has completed the design of new MOF reactors and validated them at lab scale.
These modelling and empirical results confirm the improved performance KPIs and set the basis for scale-up tasks.