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

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.