Sensitivity prediction and analysis of nanofiber-based gas sensors using solubility and vapor pressure parameters

Rianjanu, Aditya; Hidayat, Shidiq Nur; Yulianto, Nursidik; Majid, Nurhalis; Triyana, Kuwat; Wasisto, Hutomo Suryo

doi:10.35848/1347-4065/ac1a8e

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…The NF CA0 sample, which is the sample without CA doping, shows a typical PVAc nanober morphology in agreement with previous studies. 68,69 When CA has been introduced into the electrospun NF CA solution, the produced nanobers show signicant morphological changes. The bers appear to be cross-linked with each other at their junction resulting in nanobrous mats with lumpy surfaces.…”

Section: Morphological and Deposition Analysismentioning

confidence: 99%

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

et al. 2022

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Section: Morphological and Deposition Analysismentioning

confidence: 99%

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

et al. 2022

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“…To create the prediction model, chemometrics technology including KNN machine learning algorithm was used in the analysis. This method not only accurately predicted the sensitivity of the sensor, but also provided a way for selecting appropriate sensing materials [11] .…”

Section: Related Workmentioning

confidence: 99%

“…By using equation (11), the distance between missing samples and other samples in the dataset can be determined. the "Manhattan distance" is shown in equation (12).…”

Section: Iot Data and Devices Optimization Based On Knnmentioning

confidence: 99%

IoT devices and data availability optimization by ANN and KNN

Chen,

Song,

Zhang

et al. 2023

Preprint

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To improve the availability of IoT devices and data, research has been conducted on rapid prediction of instantaneous fault rates and temperatures. An IoT device and data availability optimization scheme based on artificial neural networks and K-nearest Neighbo drivers is proposed, using artificial neural network algorithms and K-nearest Neighbo driven neural network algorithms. The preliminary algorithm for achieving availability optimization is selected, and the objectives are divided into data optimization and device optimization. Applicable models are constructed separately, and the proposed optimization model is solved using the K-neighborhood driven neural network algorithm. The validation results showed that the proposed scheme reduced the maximum temperature to 2.0750 ℃ compared to the benchmark method, availability forward fault-tolerant method, and heuristic optimization algorithm. Compared with the first three methods, the improved method can improve the average availability of IoT devices by 27.03%, 15.76%, and 10.85%; The instantaneous fault rates of the three algorithms reached 100%, 87.89%, and 84.4%. This optimization algorithm has high efficiency in eliminating fault signals and optimizing the prediction of time limited satisfaction, and has strategic foresight in the decision plans of decision implementers.

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“…A further enhancement of pattern recognition models is necessary in e-nose for data analysis besides fabrication and optimization of highly sensitive low-power gas sensors 34 – 40 . To obtain high accuracy and precision, e-nose requires appropriate learning algorithms to extract and to learn complex patterns detected by its sensor components.…”

Section: Introductionmentioning

confidence: 99%

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

et al. 2023

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Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

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Sensitivity prediction and analysis of nanofiber-based gas sensors using solubility and vapor pressure parameters

Cited by 9 publications

References 37 publications

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

Influence of dopant concentration on the ammonia sensing performance of citric acid-doped polyvinyl acetate nanofibers

IoT devices and data availability optimization by ANN and KNN

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

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