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Here we report the modelling of thermally initiated RAFT step-growth polymerization kinetics of maleimide and acrylate monomers with bifunctional RAFT agents bearing tertiary carboxyalkyl-stabilized fragmentable R groups. 

Here, we report the modelling of photo-mediated RAFT step-growth polymerization kinetics of maleimide and acrylate monomers with bifunctional RAFT agents bearing tertiary carboxyalkyl stabilized fragementable R groups. 

Abstract Photomediated reversible addition fragmentation chain transfer (RAFT) step‐growth polymerization is performed using a trithiocarbonate‐based chain transfer agent (CTA) and acrylate‐based monomers both with and without a photocatalyst. The versatility of photo‐mediated RAFT step‐growth is demonstrated by one‐pot synthesis of a graft copolymer via sequential monomer addition. Furthermore, oxygen‐tolerant photo‐mediated RAFT step‐growth is demonstrated, facilitated by the appropriate selection of photocatalyst and solvent pair (zinc tetraphenyl porphyrin [ZnTPP] and dimethyl sulfoxide [DMSO]), enabling ultralow volume polymerization under open‐air conditions. 

Here we report one-pot stimuli-responsive tandem degradation of a graft copolymer with alternating backbone functionalities. 

RAFT step-growth polymerizationviathe Z-group approach was developed, offering a facile method to prepare deconstructable (multiblock) polymers by combining RAFT chain-growth polymerization and RAFT interchange. 

Abstract Breath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain‐responsive mesostructure. The miniaturized construction and module‐based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine‐learning algorithm based on K‐means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on‐body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers.