Microgravimetric Thermodynamic Modeling for Optimization of Chemical Sensing Nanomaterials

Abstract

On the basis of microgravimetric sensing data, an analytical modeling method is proposed for comprehensive evaluation and optimization of gas sensing or adsorbing related functional materials. Resonant microcantilever is loaded with the material to be evaluated for a gravimetric sensing experiment. With sensing isotherm curves obtained at different temperatures, key thermodynamic and kinetic parameters of the material, such as enthalpy ΔH°, Gibbs free energy, adsorption rate constant Ka, and coverage θ, etc., can be quantitatively extracted for optimal selection and design. On the basis of the gravimetric experiment, the modeling method is used on three sorts of trimethylamine sensing nanomaterials of mesoporous silica nanoparticles (MSNs). The COOH-functionalized material is clearly identified as the best sensing material among the three similar ones, thereby validating high accuracy of the proposed model. Broad applicability of the modeling method to other sensing materials and/or target gases is also experimentally confirmed, where sensing properties of a functionalized hyper-branched polymer to organophorous simulant of dimethyl methylphosphonate (DMMP) are still evaluated well. In addition to sensing materials, the gravimetric experiment-based modeling method can be expanded to other functional materials like moisture absorbents or detoxification agents. Water adsorbing experiment on KIT-5 mesoporous-silica is modeled, with the low -ΔH° value (i.e., low adsorption heat) result, indicating that the KIT-5 is a good adsorbent to humidity. Alternatively, the modeled high -ΔH° value (i.e., high reaction heat) shows promising usage of SBA-15 mesoporous-silica as detoxification material to hazardous organophorous chemicals. Therefore, the analytical modeling technology can be used for developing and evaluating new adsorbing materials for gas sensing, fixing, and detoxification applications.