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Title: | Preparation and characterization of the phthalocyanine–zinc(II) complex-based nanothin films: optical and gas-sensing properties | Authors: | Acikbas Y. Erdogan M. Capan R. Ozkaya Erdogan C. Baygu Y. Kabay N. Gök Y. |
Keywords: | Nanothin film Neural networks Optical sensor SPR Swelling dynamics Zinc(II)–phthalocyanine Chemical detection Chemical sensors Chemical stability Film preparation Ketones Optical films Optical properties Optical sensors Surface plasmon resonance Swelling Thin film transistors Thin films Zinc compounds Gas sensing properties Learning models Macrocyclic structure Nano-thin films Neural-networks Optical sensing properties Sensing material Surface-plasmon resonance Swelling dynamic Zinc(II)–phthalocyanine Deep learning |
Publisher: | Springer Science and Business Media Deutschland GmbH | Abstract: | Phthalocyanines (Pcs), among synthetic macrocyclic structures, have recently been preferred sensing materials for harmful vapor detection owing to their attractive properties, such as chemical stability and highly thermal, and good production as nano-thin film layers and their ?-electron conjugated system. Herein, phthalocyanine–zinc(II)-based (ZnPc) Langmuir–Blodgett (LB) nanothin films were produced and characterized by UV–Visible spectrophotometer and Surface Plasmon Resonance (SPR) technique. The gas-sensing and optical properties of these ZnPc nanothin films were also investigated by SPR method. The optical properties of ZnPc LB films with varied numbers of layers were also expressed with this study. The refracting index values of ZnPc LB film layers were identified between 1.43 and 1.73 for the thicknesses of 3.7 and 12.6 nm with linear regression of 0.9926 by fitting SPR experimental data. The basic host–guest interaction principle was used to investigate the response of phthalocyanine–zinc(II)-based optical sensors against to the selected alcohol and ketone vapors. These kinetic data were performed by employing Fick’s diffusion equation to study the swelling dynamics’ ZnPC nanothin films. To prove the efficiency of the experiments, the experimental data were modeled with the well-known deep learning models. The experimental data set is split into two sections: the training and test part. The 83% amount of data is operated to train the deep learning models and the remaining 17% samples of data are operated to observe the developed models prediction performance. © 2022, Springer Nature Switzerland AG. | Description: | Article; Early Access | URI: | https://doi.org/10.1007/s13204-022-02749-3 https://hdl.handle.net/11499/47399 |
ISSN: | 2190-5509 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection Tavas Meslek Yüksekokulu Koleksiyonu Teknoloji Fakültesi Koleksiyonu WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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