Chemical Sensor Properties and Mathematical Modeling of Graphene Oxide Langmuir-Blodgett Thin Films

Büyükkabasakal, Kemal; Acikbas, Sibel Celik; Deniz, A.; Açıkbaş, Yaser; Çapan, Rıfat; Erdoğan, Matem

doi:10.1109/jsen.2019.2926367

Cited by 14 publications

(5 citation statements)

References 30 publications

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“…The studies of QCM sensor application are good agreement with our present work which has a bigger molecular weight of organic vapor causes to larger sensitivity (Acikbas et al, 2011;Buyukkabasakal et al, 2019). It is sensible to guess that if the number of adsorbed vapor molecules on N-MPMA QCM sensor (adsorbent) is identical and limited for various adsorbents, a greater molar mass of adsorbent would absolutely cause to a greater frequency change.…”

Section: Qcm Kinetic Resultssupporting

confidence: 89%

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Developing ofN‐(4‐methylpyrimidine‐2‐yl)methacrylamideLangmuir–Blodgettthin film chemical sensor via quartz crystal microbalance technique

Açıkbaş

Tetik

Özkaya

et al. 2020

Microscopy Res & Technique

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In the present work, the characterization and gas sensing properties of newly synthesized N‐(4‐methylpyrimidine‐2‐yl)methacrylamide (N‐MPMA) monomer Langmuir–Blodgett (LB) thin films were investigated. The UV–visible spectroscopy, quartz crystal microbalance (QCM), and atomic force microscopy were utilized to characterize N‐MPMA LB thin films. The surface behavior of N‐MPMA monolayer was stable and allowed an effective transfer at a surface pressure of 14 mN/m. The mass change/unit area value of the N‐MPMA LB thin film deposited quartz crystal surfaces was investigated. The amount of N‐MPMA LB thin film deposited on the substrate for bilayer was calculated as 228.72 ng (86.31 ng/mm2) and 12.5 Hz frequency shift was observed for each layer of the N‐MPMA film. The kinetic responses of N‐MPMA LB film against chloroform, dichloromethane, benzene, and toluene were measured via QCM system at room temperature. N‐MPMA QCM sensor results displayed that chloroform has the largest frequency shifts compared with the other vapors used in the present work and these results can be illuminating in terms of physical properties of organic vapors.

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Section: Qcm Kinetic Resultssupporting

confidence: 89%

“…The kinetic response values of N-(4-methylpyrimidine-2-yl)methacrylamide quartz crystal microbalance chemical sensor and molecular weight) of organic vapors (Buyukkabasakal et al, 2019).…”

Section: Qcm Kinetic Resultsmentioning

confidence: 99%

Developing ofN‐(4‐methylpyrimidine‐2‐yl)methacrylamideLangmuir–Blodgettthin film chemical sensor via quartz crystal microbalance technique

Açıkbaş

Tetik

Özkaya

et al. 2020

Microscopy Res & Technique

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“…These interactions are not only dependent on physical properties such as vapor pressure, molar volume, viscosity, etc. [7,15], but also chemical interactions such as host-guest, hydrogen bonding, Van der Waals, etc. [38].…”

Section: Fig 4 a Schematic Representation Of The Interaction Mechanis...mentioning

confidence: 99%

“…Many sensors with different operating mechanisms, fast and sensitive detection of organic volatile compounds in the indoor or outdoor environment can be carried out [5,6]. A thin film layer is usually preferred as the basic gas sensor element in the detection of volatile organic compounds in this field [7][8][9]. These thin films are designed to have a controlled, homogeneous structure and effective detection capability; researchers are making great efforts to develop gas sensor elements with increased sensitivity, low cost and reversibility [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Surface plasmon resonance chemical sensor modified with α-naphthylmethacrylate nano thin film for chloroform detection

Acikbas

2023

Res. Eng. Struct. Mat.

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The research on the rapid and sensitive detection of pollution caused by the products and technologies used has recently attracted attention in the literature. In the detection of volatile organic compounds (VOCs), polymers/monomers are used in the production of sensitive, fast-responding, reversible thin-film gas sensor elements. In this work, the monomer α-Naphthylmethacrylate was selected as thin film sensor element. The α-Naphthylmethacrylate-based nano thin films were prepared with technique of LB. Monomer LB thin films fabricated onto gold-coated glass substrate to investigate their vapour sensing properties by using Surface Plasmon Resonance (SPR) optical technique. This prepared monomer-based thin film sensor exposed to five different concentrations of chloroform vapors varying between 13.98x103-69.9x103 ppm and the sensor response values were recorded. Swelling dynamics’ of this monomer thin film sensor was also illuminated by using Fick's early-time diffusion law. Diffusion coefficients of α-Naphthylmethacrylate LB thin film sensor materials exposed to the five different concentrations of chloroform vapor were calculated with the help of reflected light intensity graph data and Fick's Law as a function of time. It was determined that the diffusion coefficient values of the first and second slope regions as varying between 5.72x10-17-21.97x10-17 cm2s-1 and 3.06x10-17-6.25x10-17 cm2s-1, respectively. SPR kinetic measurement results showed that α-Naphthylmethacrylate material is promising for the detection of chloroform vapor.

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“…Amani et al [10] performed multi-criteria modeling and optimization of the rheological and thermophysical properties of an environmentally-friendly covalently functionalized nanofluid containing graphene nanoplatelets (CGNPs). The Narx-ANN mathematical model was developed to shift the quartz resonator's frequency shift on GO langmuir bladgett thin-films [11]. The application of ANN to the classification and prediction of graphene nanomaterial is very minimal, but it is extensive for others [12].…”

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confidence: 99%

Optimization of learning algorithms in multilayer perceptron (MLP) for sheet resistance of reduced graphene oxide thin-film

Aminuddin

Ismail

Masrie

et al. 2021

IJEECS

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<div>Multilayer perceptron (MLP) optimization is carried out to investigate the classifier's performance in discriminating the uniformity of reduced Graphene Oxide(rGO) thin-film sheet resistance. This study used three learning algorithms: resilient back propagation (RP), scaled conjugate gradient (SCG) and levenberg-marquardt (LM). The dataset used in this study is the sheet resistance of rGO thin films obtained from MIMOS Bhd. This work involved samples selection from a uniform and non-uniform rGO thin-film sheet resistance. The input and output data were under going data pre-processing: data normalization, data randomization and data splitting. The data were dividedin to three groups; training, validation and testing with a ratio of 70%: 15%: 15%, respectively. A varying number of hidden neurons optimized the learning algorithms in MLP from 1 to 10. Their behavior helped establish the best learning algorithms in discriminating MLP for rGO sheet resistance uniformity. The performances measured were the accuracy of training, validation and testing dataset, mean squared errors (MSE) andepochs. All the analytical work in this study was achieved automatically via MATLAB software version R2018a. It was found that the LM is dominant inthe optimization of a learning algorithm in MLP forrGO sheet resistance.The MSE for LM is the most reduced amid SCG and RP.</div><div> </div>

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Chemical Sensor Properties and Mathematical Modeling of Graphene Oxide Langmuir-Blodgett Thin Films

Cited by 14 publications

References 30 publications

Developing ofN‐(4‐methylpyrimidine‐2‐yl)methacrylamideLangmuir–Blodgettthin film chemical sensor via quartz crystal microbalance technique

Developing ofN‐(4‐methylpyrimidine‐2‐yl)methacrylamideLangmuir–Blodgettthin film chemical sensor via quartz crystal microbalance technique

Surface plasmon resonance chemical sensor modified with α-naphthylmethacrylate nano thin film for chloroform detection

Optimization of learning algorithms in multilayer perceptron (MLP) for sheet resistance of reduced graphene oxide thin-film

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