Research note · 6 min read
What oxygen tolerance buys you in polymer chemistry
By Eman Ahmed, PhD candidate, Gormley Lab, Rutgers University
If you have only ever seen controlled radical polymerisation described in a textbook, the constraint that shapes everything in practice is easy to miss: oxygen kills it. Propagating radicals react with molecular oxygen far faster than they react with monomer, so a conventional atom transfer radical polymerisation needs sealed glassware and a freeze-pump-thaw cycle or a nitrogen sparge before anything useful happens.
That requirement quietly sets the ceiling on how many experiments you can run. Degassing is manual, it is slow, and it does not parallelise. A skilled person might set up a dozen carefully controlled reactions in a day. Twelve points is not enough to map a reaction space with four or five interacting variables.
Open labware is the whole point
Oxygen-tolerant reversible-deactivation radical polymerisation changes the arithmetic. By building oxygen consumption into the system itself, photocatalytically, enzymatically, or through the reducing environment of the photo-ATRP mechanism, the reaction becomes something you can run in an uncapped well on an open bench.
Once that is true, a 96-well plate stops being a container and becomes an experimental design. Each well is an independent condition. A liquid handler can lay down ninety-six different ligand, initiator, monomer and catalyst combinations in the time it would take to degas one flask, and every one of them is measured the same way, which matters more than it sounds like it should. Consistency in how data is collected is what makes it usable for modelling later.
Where it gets interesting: the slow monomers
Acrylates propagate quickly and are relatively forgiving. Methacrylates are not. Methyl methacrylate has a substantially smaller propagation rate constant, which means the balance between activation and deactivation that ATRP depends on has to be tuned more carefully, and the right ligand and initiator pairing is not something you can reliably reason your way to from first principles.
This is precisely the situation where throughput earns its keep. Rather than arguing about which ligand should work, you screen the ligands. In our ACS Polymers Au paper we used an automated photo-ATRP platform to do exactly that, extending a workflow the lab had previously built for PET-RAFT and enzyme-assisted RAFT to a third major polymerisation chemistry.
The unglamorous part that makes it reproducible
A high-throughput platform that only works in the lab that built it is a demo, not a method. The practical obstacle is rarely the robot. It is translating an intended experimental design into the deck layout, volumes and transfer sequence the instrument needs, without arithmetic errors propagating silently across ninety-six wells.
We released a Python package to handle that planning step. It is not the scientifically exciting part of the work, and it is probably the part that determines whether anyone else can run it.
What this unlocks
The reason to care about throughput here is not throughput. It is that data-driven optimisation of polymerisation conditions needs datasets that are large, consistent and include the conditions that did not work. Hand-run chemistry produces small datasets biased towards conditions the chemist already expected to succeed. Plate-based chemistry produces the other kind.
Source
This note summarises Automation-Assisted Photoinduced Atom Transfer Radical Polymerization (Ramirez, Ahmed, Mare et al., ACS Polymers Au, 2026; doi:10.1021/acspolymersau.5c00067). These notes are plain-language companions to peer-reviewed work. Every factual claim traces to the paper linked at the end of the note.