Continuous Flow Chemistry and Bayesian Optimization for Polymer-Functionalized Carbon Nanotube-Based Chemiresistive Methane Sensors

Abstract

We report the preparation of poly(ionic) polymer-wrapped single-walled carbon nanotube dispersions for chemiresistive methane (CH4) sensors with improved humidity tolerance. Single-walled CNTs (SWCNTs) were noncovalently functionalized by poly(4-vinylpyridine) (P4VP) with varied amounts of a poly(ethylene glycol) (PEG) moiety bearing a Br and terminal azide group (Br-R1). The quaternization of P4VP with Br-R1 was performed using continuous flow chemistry and Bayesian optimization-guided reaction selection. Polymers (PyBrR1) with different degrees of functionalization were used to disperse SWCNTs and subsequently incorporated into sensors containing a platinum complex as an aerobic oxidative catalyst with a polyoxometalate (POM) redox mediator to facilitate room-temperature CH4 sensing. As the degree of quaternization in the PyBrR1-CNT composites increased, improvements in response magnitude were observed, with nominally 10% quaternized PyBrR1 giving the largest response. Incorporation of PEG improved sensor stability at relative humidities between 57-90% versus sensors fabricated from CNT dispersions with unfunctionalized P4VP. Devices fabricated with these dispersions outperformed those prepared in situ under dry conditions, and exhibited greater stability at elevated humidities. The influence of Keggin-type POM character was also evaluated to identify alternative POMs for enhanced sensor performance at high humidity. In an effort to identify areas for further improvement in algorithm performance for polymer functionalization, a kinetically informed machine learning model was explored as a route to predict reactivity of pyridine units and alkyl bromides under flow conditions.