CO2 conversion and e-fuels
Turning captured CO2 into something useful. Here the hard constraint is selectivity, not activity.
Early packs get chosen by who asks for them. If your laboratory would use this, saying so genuinely moves it up the list — and costs you nothing.
The problem
CO2 can be converted into fuels and chemicals, and the reaction is not usually the problem. The problem is that it produces a mixture — and separating products you did not want destroys the economics faster than a slow catalyst does.
That makes this pack structurally different from the others: the objective vector has to include selectivity towards a specific product, not just conversion rate.
How the pack would work
- Interatomic potentials extended with selectivity descriptors, across both electrocatalysts and thermocatalysts.
- Multi-objective ranking where product distribution is an explicit axis.
- Closure to cost per tonne of the target product, including separation.
- Candidate space
- Electrocatalysts and thermocatalysts, with product selectivity
- What one experiment costs
- Electrochemical and reactor testing
- Predictor
- Interatomic potentials with selectivity descriptors
- Decides on
- Cost per tonne of product
What it would take to build
Stated plainly, because these are the things we do not have yet.
A selectivity descriptor that holds up across the chemistries of interest.
Separation cost models, because they often decide the answer.
Questions
Why is selectivity harder than activity?
Because activity is a single number and selectivity is a distribution. Screening for it means predicting which of several competing pathways wins, which is a genuinely harder modelling problem and a reason this pack is later rather than earlier.