Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/47399
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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