Polymeric Gas Sensing Materials for Detection of Toxic Analytes

Abstract

Toxic volatile organic compounds (VOCs), including formaldehyde, are pervasive in both industrial and indoor environments and pose serious health risks at even low chronic exposure levels, yet the tools available for real-time monitoring remain bulky, expensive, and ill-suited for portable or wearable deployment. At its core, this is majorly a materials problem and no potential transducer architecture in a micro gas sensor can compensate enough for a sensing layer that lacks the chemical specificity to distinguish formaldehyde from structurally similar interferents like ethanol, acetone, and benzene. This thesis approaches this materials challenge directly, tracing a systematic path from backbone chemistry through composite engineering and regeneration characterization to device-level integration, with the goal of understanding not just which materials work but why they work and how that understanding can guide rational design of gas sensing polymeric materials for any gas analyte. Four pristine conducting polymer backbones were first screened under single-gas and multi-component gas exposures. Polyaniline (PANI) and poly(2,5-dimethylaniline) (P25DMA) emerged as the most promising candidates. Under quaternary mixture conditions (formaldehyde, ethanol, acetone, and benzene at 2 ppm each), P25DMA was the only pristine material to exceed a mixture selectivity index F/(E+A+B) value of ~1.02 compared to ~0.59 for PANI, attributable to the steric and electronic influence of the dimethyl ring substitution reducing non-selective hydrogen-bonding with competing oxygenates. However, even P25DMA's selectivity advantage has limits and neither backbone alone provides the sensitivity-selectivity combination required for robust real-world deployment. This established the case for composite modification: not as an incremental improvement, but as a necessary step to access adsorption environments that pristine polymer chemistry alone cannot deliver. To further improve performance, metal oxides (In2O3, NiO, SnO2, TiO2) were incorporated into both backbones. The central finding is that metal oxide identity governs the mode of incorporation, which in turn dictates pore architecture and ultimately drives sensitivity and selectivity outcomes. Because APS-initiated polymerization occurs in a strongly acidic medium, each metal oxide's resistance to dissolution during synthesis determines whether it survives as a surface-accessible particle or becomes buried within the polymer matrix. In2O3 and SnO2, being acid-stable, remain particulate and expose hydroxylated, defect-rich surfaces whose polar adsorption sites interact preferentially with formaldehyde's carbonyl group, simultaneously enhancing sensitivity and maintaining selectivity. NiO, being acid-labile, becomes polymer-encased during synthesis and contributes no chemically differentiated surface, instead blocking active amine sites at higher loadings and degrading selectivity precisely where sensitivity appears to peak. This acid stability explains why metal oxide identity matters relatively more than metal oxide loading as a first design variable. Backbone architecture then determines how effectively that oxide can be dispersed: P25DMA's lamellar structure anchors 3.6 times more In2O3 than PANI's fibrillar network under the same recipe, generating a denser polymer-metal oxide interfacial perimeter where selective adsorption events occur. Pore accessibility completes the picture by controlling whether analyte molecules can physically reach those interfacial sites and whether the pore environment favors selective or non-selective uptake. At the optimal 5% In2O3 loading in P25DMA, confinement-dominated transport gives way to open, externally accessible surface where formaldehyde's smaller kinetic diameter and stronger affinity for oxide sites gives it a systematic competitive advantage over interferents, driving sensitivity and selectivity simultaneously. This hierarchy, from metal oxide acid stability to embedding mode to backbone morphology to pore accessibility to competitive adsorption selectivity, is not specific to formaldehyde and provides a transferable framework for designing polymer-oxide composites toward any analyte where backbone-analyte affinity and metal oxide surface chemistry can be deliberately matched. Finally, both pristine P25DMA and P25DMA with 5% In2O3 composite were deposited onto a MEMS resonant mass sensor. Formaldehyde exposure of the functionalized MEMS sensor displayed device-level transduction of polymer sorption, with P25DMA with 5% In2O3 composite exhibiting larger and faster frequency shifts than the pristine P25DMA. This is in agreement with and reinforces the observation of improved sensing performance of the composite material. For a sensing material to be practically deployable, sensitivity and selectivity alone are not enough: it must also recover reliably after exposure and survive repeated use without structural degradation. PANI regenerates through mild thermal assistance up to 80°C enhances desorption kinetics without compromising backbone integrity. Beyond this point, the onset of glass transition induces segmental mobility that shortens analyte residence time and reduces retained analyte in polymer matrix leading to desorption. The post-regeneration characterization confirms the backbone remains chemically intact throughout, establishing that the material can be returned to baseline under mild conditions without sacrificing the chemical properties that drive its sensing performance. Additional parametric studies on synthesis temperature, ageing, sensing layer mass, physical form, and carbon-based fillers collectively confirmed that composition-driven effects from oxide identity and loading are the dominant levers for tuning selectivity, while processing variables impose secondary but measurable influences. Underpinning all of this materials work is a detailed mechanistic and kinetic study of PANI polymerization itself, tracing the initiation, chain growth, and termination steps and developing a numerical kinetic model that predicts monomer conversion and molecular weight evolution, ensuring that the sensing materials produced in this thesis are understood at the level of their synthesis chemistry rather than treated as empirical outputs. Taken together, this thesis demonstrates that the path to high-performance polymer-based formaldehyde sensing runs through a hierarchy of materials decisions, from backbone chemistry through oxide acid stability and embedding mode to pore architecture, and that mechanistic understanding of each step is what enables co-optimization of sensitivity, selectivity, and renderability rather than trading one against another.