News Picture Generic

Closed-Loop Bayesian Optimization for the Synthesis of Amine-Grafted Resins for Direct Air Capture

March 24, 2026

ChemRxiv

Direct air capture (DAC) of CO2 remains limited by adsorbent performance. Adsorption kinetics are a particularly underexplored lever to improve adsorbents, despite their significance in realistic processes. Today, the profile of 'the' optimal DAC adsorbent displaying high CO2 uptake and selectivity as well as fast sorption kinetics remains unknown. Here, we present a closed-loop approach combining high-throughput synthesis and characterisation with Bayesian optimization to understand how the material synthesis space impacts adsorbent properties and how it can ultimately be optimized to enhance adsorption-based DAC performance. To carry out this study, we chose amine-functionalized porous adsorbents as our model system. By jointly optimizing adsorption kinetics and equilibrium CO2 uptake at 400 ppm, the framework identifies high-performing materials while experimentally mapping a Pareto front. Sorbents with the highest CO2 uptakes exhibit the slowest kinetics, and vice versa. We link this trade-off to the pore structure of the materials and how the pore geometry imposes diffusion constraints. The optimization study uncovers adsorbents with order-of-magnitude higher adsorption kinetics than benchmark adsorbents at half the uptakes. Beyond this model system, our Bayesian optimization framework is a blueprint for multi-objective materials optimization in fields beyond DAC adsorbents.

For details: 

Closed-Loop Bayesian Optimization for the Synthesis of Amine-Grafted Resins for Direct Air Capture

Tristan L Spreng 1, Paul Schweng 2, David Danaci 1,3,4, Ronny Pini 1,3, and Camille Petit 1

1) Department of Chemical Engineering, Imperial College London, London, United Kingdom
2) Institute of Materials Chemistry and Research, University of Vienna, Vienna, Austria,
3) The Sargent Centre for Process Systems Engineering, Imperial College London, London, United Kingdom,
4) I-X Centre for AI in Science, Imperial College London, London, United Kingdom
5) National University of Singapore (Suzhou) Research Institute, Suzhou, Jiangsu, China

ChemRxiv
https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000640/v1

Contact us to learn more about this exciting article:https://www.chemspeed.com/contact/

Other Recent News

Discover more news articles you might be interested in

Read more about Identifying critical powder properties for high-throughput dispensing of alumina and organic templates
News Picture 1 1 V2
Jun
16

Identifying critical powder properties for high-throughput dispensing of alumina and organic templates

Screening powder properties such as flowability, compressibility, and particle geometry is crucial for controlling ceramic processing, particularly in automated workflows that demand high reproducibility. Sacrificial templating for porous ceramics is well suited to automation because it is prone to variability arising from manual handling.

Read more about Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research
News Picture 1 1 V2
Jun
2

Integration of Machine Learning and Automated Synthesis for Accelerated Drug and Material Research

The challenges posed by global climate change and disease risks have intensified the demand for efficient and practical materials and molecules. Traditional trial-and-error approaches are becoming increasingly inefficient and resource-intensive. The rapid advancement of artificial intelligence (AI) has opened new avenues to accelerate research and shorten development cycles.

© Chemspeed Technologies 2026