A Novel X-Ray Radiation Sensor Based on Networked SnO2 Nanowires

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

Section: Introductionmentioning

confidence: 99%

Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Zarra

Galang

Ballesteros

et al. 2020

“…Although there have been great benefits to humans as a result of the continuous developments in technology and industry, there have been serious effects on human health due to the release of hazardous radiation [23][24][25] and substances such as toxic gases [26,27] and volatile organic compounds [28,29]. The presence of some of these gases, even at low concentrations, is very dangerous to human life [30].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Sensing Performance of Zigzag Graphene Nanoribbon to Detect NO, NO2, and NH3 Gases

Salih

Ayesh

2020

In this article, a zigzag graphene nanoribbon (ZGNR)-based sensor was built utilizing the Atomistic ToolKit Virtual NanoLab (ATK-VNL), and used to detect nitric oxide (NO), nitrogen dioxide (NO2), and ammonia (NH3). The successful adsorption of these gases on the surface of the ZGNR was investigated using adsorption energy (Eads), adsorption distance (D), charge transfer (∆Q), density of states (DOS), and band structure. Among the three gases, the ZGNR showed the highest adsorption energy for NO with −0.273 eV, the smallest adsorption distance with 2.88 Å, and the highest charge transfer with −0.104 e. Moreover, the DOS results reflected a significant increase of the density at the Fermi level due to the improvement of ZGNR conductivity as a result of gas adsorption. The surface of ZGNR was then modified with an epoxy group (-O-) once, then with a hydroxyl group (-OH), and finally with both (-O-) and (-OH) groups in order to improve the adsorption capacity of ZGNR. The adsorption parameters of ZGNR were improved significantly after the modification. The highest adsorption energy was found for the case of ZGNR-O-OH-NO2 with −0.953 eV, while the highest charge transfer was found for the case of ZGNR-OH-NO with −0.146 e. Consequently, ZGNR-OH and ZGNR-O-OH can be considered as promising gas sensors for NO and NO2, respectively.

“…They easily detect the presence of various gases and are able to estimate their concentration in the range 1–50 ppm. They are also successfully used to detect X-ray radiation [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Varroosis Based on Bee Brood Samples Testing with Use of Semiconductor Gas Sensors

Bąk

Wilk

Artiemjew

et al. 2020

Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class—empty chamber (13 objects), 2nd class—fragments of combs containing brood sick with varroosis (19 objects), and 3rd class—fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)—with default settings in RSES tool—was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments—in multiple Monte Carlo Cross Validation model—using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.