CLEAN AIR Q

Carbon dioxide (CO2) from the atmosphere can be combined with hydrogen (H2) to produce methanol (CH3OH), which, as a liquid, can be easily stored and has many commercial uses.

Hydrogen can be made and sourced renewably, but it’s difficult to turn hydrogen and carbon dioxide into methanol, because carbon dioxide is a very stable molecule and resists change. It requires specific conditions and some sort of catalyst to encourage the reaction.

Commercial metal-oxide catalysts are inefficient and energy intensive, needing temperatures above 300°C , and they make a lot of carbon monoxide (CO) as a by-product.

This is where quantum computations come in. The qubits (denoted by a probability density rather than rigid 0 or 1) in quantum computers can fundamentally simulate electrons in ways classical computers can only approximate. Using quantum machine learning, we can quickly and accurately calculate the intermediate energy levels in a reaction.

These energy levels model how efficiently molecules interact with each other on an atomic scale. By comparing the energy profiles in different types of catalysts, a quantum ML algorithm will iteratively generate a super molecule specifically designed for carbon capture.

Direct air capture relies on finding a more abundant and efficient catalyst to scale its impact. Currently, it is the only tech on the market which is economically incentivized (by-product is sold as an ultra-low-carbon synthetic fuel), prevents emissions at its source— adding a buffer to our global carbon budget, and has a clearly scalable path to negative emissions.