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. 2020 Feb;2(2):1900126.
doi: 10.1002/aisy.201900126. Epub 2019 Dec 3.

Automation of Controlled/Living Radical Polymerization

Matthew Tamasi  1 , Shashank Kosuri  1 , Jason DiStefano  1 , Robert Chapman  2 , Adam J Gormley  1
Affiliations

Automation of Controlled/Living Radical Polymerization

Matthew Tamasi et al. Adv Intell Syst. 2020 Feb.

Abstract

Controlled/living radical polymerization (CLRP) techniques are widely utilized to synthesize advanced and controlled synthetic polymers for chemical and biological applications. While automation has long stood as a high-throughput (HTP) research tool to increase productivity as well as synthetic/analytical reliability and precision, oxygen intolerance of CLRP has limited the widespread adoption of these systems. Recently, however, oxygen-tolerant CLRP techniques, such as oxygen-tolerant photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer (PET-RAFT), enzyme degassing of RAFT (Enz-RAFT), and atom-transfer radical polymerization (ATRP), have emerged. Herein, the use of a Hamilton MLSTARlet liquid handling robot for automating CLRP reactions is demonstrated. Synthesis processes are developed using Python and used to automate reagent handling, dispensing sequences, and synthesis steps required to create homopolymers, random heteropolymers, and block copolymers in 96-well plates, as well as postpolymerization modifications. Using this approach, the synergy between highly customizable liquid handling robotics and oxygen-tolerant CLRP to automate advanced polymer synthesis for HTP and combinatorial polymer research is demonstrated.

Keywords: automation; high throughput; oxygen tolerant; polymers; reversible addition–fragmentation chain transfer.

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Conflict of interest statement

Conflict of Interest The authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.
Schematic of automated process for PET–RAFT and Enz-RAFT. “Python” and the Python logos are trademarks or registered trademarks of the Python Software Foundation, used with permission from the Foundation.
Figure 2.
Figure 2.
Robotic PET–RAFT. A,C) GPC-measured Mn and Đ of NAM and DMA polymerizations prepared by liquid handling robotics in 96-well plates. Polymerizations were irradiated under 560 nm LED light (5 mW cm−2) for 5 h and DP was varied from 100 to 400. B,D) Corresponding molecular weight distributions as measured by GPC.
Figure 3.
Figure 3.
Molecular weight distributions as measured by GPC for five synthesized NAM 200 samples. Polymerizations were irradiated under 560 nm LED light (5 mW cm−2) for 5 h and DP was held at a constant 200/1 Mon/CTA ratio.
Figure 4.
Figure 4.
Automated synthesis of block copolymers. A) GPC-measured Mn and Đ of DMA tri-block copolymer prepared by liquid handling robotics in 96-well plates. DMA tri-block copolymer was irradiated under 560 nm LED light (5 mW cm−2) for 3 h for block 1 and 2 h for subsequent blocks. B) Corresponding molecular weight distributions as measured by GPC.
Figure 5.
Figure 5.
Automated synthesis and postpolymerization functionalization process.
Figure 6.
Figure 6.
Automated synthesis and postpolymerization functionalization. A) UV–vis spectra of polymer samples showing loss of characteristic DBCO absorption peak after addition of 2 kDa PEG-N3. B) Molecular weight distributions for DMA/NHS polymers before and after PEG functionalization.
Figure 7.
Figure 7.
Automated synthesis by Enz-RAFT: A) GPC-measured Mn and Đ of Enz-RAFT prepared NAM by liquid handling robotics in 96-well plates. Polymer DP was varied from 100 to 350. B) Corresponding molecular weight distributions as measured by GPC.
Figure 8.
Figure 8.
Unique transfer requirements for increased complexity and scale using PET–RAFT.
See this image and copyright information in PMC

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