Sensing Volatile Pollutants with Spin-Coated Films Made of Pillar[5]arene Derivatives and Data Validation via Artificial Neural Networks.

Different types of solvents, aromatic and aliphatic, are used in many industrial sectors, and long-term exposure to these solvents can lead to many occupational diseases. Therefore, it is of great importance to detect volatile organic compounds (VOCs) using economic and ergonomic techniques. In this study, two macromolecules based on pillar[5]arene, named and , were synthesized and applied to the detection of six different environmentally volatile pollutants in industry and laboratories.

Influence of atomic force microscopy acquisition parameters on thin film roughness analysis.

A reliable procedure for measuring parameters connected to surface roughness is needed to compare the gas sensing properties of various thin films or the effect of different fabrication procedures on the surface roughness and the sensing properties. In this article, we propose to investigate how the acquisition parameters specific to atomic force microscopy investigations such as pixel size, scan area and scan speed influence the roughness parameters, namely root mean square and surface area ratio, commonly used for characterizing the gas sensing properties of porphyrins and other materials.