An Aminopyridine Bearing Pillar[5]arene-Based QCM Sensor for Chemical Sensing Applications: Design, Experimental Characterization, Data Modeling, and Prediction

This study presented that deca pyridin-2-amine bearing Pillar[5]arene (P5-PA) was designed in an appropriate cavity, which acts a part significant role in host-guest interactions of the macrocyclic molecules. P5-PA monolayer was deposited onto suitable substrates as an active layer by using Langmuir-Blodgett (LB) coating technique to examine its vapor sensing capabilities against vapor of some aliphatic hydrocarbons through Quartz Crystal Microbalance (QCM) technique. The kinetic vapor studies were occurred by exposing the P5-PA/LB thin film to different percentage of organic VOCs vapors such as dichloromethane, chloroform and carbon tetrachloride in air-VOCs mixture. The early-time Fick's diffusion law was handled to extract the diffusion coefficients by utilizing QCM data depending real time. It is observed that there were two different regions with two slopes indicating that one belongs to slow surface diffusion and another fast for bulk diffusion into LB thin film. The collected experimental data with 1 Hz sampling frequency was modelled with deep learning models (NARNET and LSTM) which could get satisfactory results on small datasets. The models were trained with 83% samples of data; the remaining 17% data is used to evaluate the developed models prediction performance. The predicted values of models were compared with the original (measured) data in the results section. It is observed from the results; the developed deep learning models have higher than 0.98 correlation coefficient for each vapor, which is satisfactory for prediction applications.