scholarly journals Nanoporous Gold as a VOC Sensor, Based on Nanoscale Electrical Phenomena and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2851
Author(s):  
Timothy S.B. Wong ◽  
Roger Newman
Keyword(s):  
Neural Network ◽  
Capillary Condensation ◽  
Daily Life ◽  
Industrial Applications ◽  
Nanoporous Gold ◽  
Volatile Organic ◽  
Sensitivity And Selectivity ◽  
Classification And Regression ◽  
Voc Sensor ◽  
Significant Health

Volatile organic compounds (VOCs) are prevalent in daily life, from the lab environment to industrial applications, providing tremendous functionality but also posing significant health risk. Moreover, individual VOCs have individual risks associated with them, making classification and sensing of a broad range of VOCs important. This work details the application of electrochemically dealloyed nanoporous gold (NPG) as a VOC sensor through measurements of the complex electrical frequency response of NPG. By leveraging the effects of adsorption and capillary condensation on the electrical properties of NPG itself, classification and regression is possible. Due to the complex nonlinearities, classification and regression are done through the use of a convolutional neural network. This work also establishes key strategies for improving the performance of NPG, both in sensitivity and selectivity. This is achieved by tuning the electrochemical dealloying process through manipulations of the starting alloy and through functionalization with 1-dodecanethiol.

scholarly journals Distant temperature and humidity monitoring: prediction and measurement

2021 ◽  
Vol 24 (3) ◽  
pp. 1405
Author(s):  
Farrukh Hafeez ◽  
Usman Ullah Sheikh ◽  
Attaullah Khidrani ◽  
Muhammad Akram Bhayo ◽  
Saleh Masoud Abdallah Altbawi ◽  
...  
Keyword(s):  
Neural Network ◽  
Daily Life ◽  
Ventilation Rate ◽  
Industrial Applications ◽  
Outdoor Environment ◽  
Accurate Information ◽  
Energy Prediction ◽  
Daily Life Activities ◽  
Rate Calculation ◽  
Humidity Monitoring

Sensing environmental measuring parameters has a pivotal role in our everyday lives. Most of our daily life activities depend upon environmental conditions. Accurate information about these parameters also helps in several industrial applications like ventilation rate calculation, energy prediction, stock maintenance in warehouses, and saving from harmful conditions. The emergence of machine learning can make it easy to predict such time series problems. This paper describes the design of a remotely controlled robotic car for measuring and predicting humidity and temperature. A customized app for accessing the robotic car is designed to indicate predicted and realtime measured values of humidity and temperature. A sensor installed builtin helps in the measurement. The recurrent neural network (RNN) model is used to predict humidity and temperature. For this purpose, experiments are carried out in both outdoor and indoor settings. Accuracy of 85% and 90% is achieved in an outdoor environment and indoor settings.

scholarly journals Use of a Novel Polymer-Coated Steel as an Alternative to Traditional Can Manufacturing in the Food Industry

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 222
Author(s):  
Miguel A. Selles ◽  
Steven R. Schmid ◽  
Samuel Sanchez-Caballero ◽  
Maziar Ramezani ◽  
Elena Perez-Bernabeu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Friction Coefficient ◽  
Organic Compounds ◽  
Upper Bound ◽  
Food Industry ◽  
Manufacturing Processes ◽  
Coated Steel ◽  
Volatile Organic ◽  
Food And Beverage

Metal containers (both food and beverage cans) are made from huge steel or aluminum coils that are transformed into two- or three-piece products. During the manufacturing process, the metal is sprayed on both sides and the aerosol acts as insulation, but unfortunately produces volatile organic compounds (VOCs). The present work presents a different way to manufacture these containers using a novel prelaminated two-layer polymer steel. It was experimentally possible to verify that the material survives all the involved manufacturing processes. Thus tests were carried out in an ironing simulator to measure roughness, friction coefficient and surface quality. In addition, two theoretical ironing models were developed: upper bound model and artificial neural network. These models are useful for packaging designers and manufacturers.

scholarly journals Volatile Organic Compounds (VOCs) Emitted from Coated Furniture Units

Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 806
Author(s):  
Ozge Cemiloglu Ulker ◽  
Onur Ulker ◽  
Salim Hiziroglu
Keyword(s):  
Air Quality ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Indoor Air ◽  
Exposure Period ◽  
Point Of View ◽  
Health Concerns ◽  
Volatile Organic ◽  
Significant Health

Volatile organic compounds (VOCs) are the main source influencing the overall air quality of an environment. It is a well-known fact that coated furniture units, in the form of paints and varnishes, emit VOCs, reducing the air quality and resulting in significant health problems. Exposure time to such compounds is also an important parameter regarding their possible health effects. Such issues also have a greater influence when the exposure period is extended. The main objective of this study was to review some of the important factors for the emission of VOCs from coated furniture, from the perspective of material characteristics, as well as health concerns. Some methods for controlling VOC emissions to improve indoor air quality, from the point of view recent regulations and suggestions, are also presented in this work.

scholarly journals Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients

2008 ◽  
Vol 15 (7) ◽  
pp. 1089-1094 ◽  
Author(s):  
R. A. Lukaszewski ◽  
A. M. Yates ◽  
M. C. Jackson ◽  
K. Swingler ◽  
J. M. Scherer ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care ◽  
Clinical Diagnosis ◽  
Predictive Accuracy ◽  
High Sensitivity ◽  
Icu Patients ◽  
The Neural Network ◽  
Sensitivity And Selectivity ◽  
Ccl2 Mrna ◽  
Factor Α

ABSTRACT Postoperative or posttraumatic sepsis remains one of the leading causes of morbidity and mortality in hospital populations, especially in populations in intensive care units (ICUs). Central to the successful control of sepsis-associated infections is the ability to rapidly diagnose and treat disease. The ability to identify sepsis patients before they show any symptoms would have major benefits for the health care of ICU patients. For this study, 92 ICU patients who had undergone procedures that increased the risk of developing sepsis were recruited upon admission. Blood samples were taken daily until either a clinical diagnosis of sepsis was made or until the patient was discharged from the ICU. In addition to standard clinical and laboratory parameter testing, the levels of expression of interleukin-1β (IL-1β), IL-6, IL-8, and IL-10, tumor necrosis factor-α, FasL, and CCL2 mRNA were also measured by real-time reverse transcriptase PCR. The results of the analysis of the data using a nonlinear technique (neural network analysis) demonstrated discernible differences prior to the onset of overt sepsis. Neural networks using cytokine and chemokine data were able to correctly predict patient outcomes in an average of 83.09% of patient cases between 4 and 1 days before clinical diagnosis with high sensitivity and selectivity (91.43% and 80.20%, respectively). The neural network also had a predictive accuracy of 94.55% when data from 22 healthy volunteers was analyzed in conjunction with the ICU patient data. Our observations from this pilot study indicate that it may be possible to predict the onset of sepsis in a mixed patient population by using a panel of just seven biomarkers.

scholarly journals Co-Treatment with Single and Ternary Mixture Gas of Dimethyl Sulfide, Propanethiol, and Toluene by a Macrokinetic Analysis in a Biotrickling Filter Seeded with Alcaligenes sp. SY1 and Pseudomonas Putida S1

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 309
Author(s):  
Yiming Sun ◽  
Xiaowei Lin ◽  
Shaodong Zhu ◽  
Jianmeng Chen ◽  
Yi He ◽  
...  
Keyword(s):  
Ternary Mixture ◽  
Dimethyl Sulfide ◽  
Illumina Miseq ◽  
Industrial Applications ◽  
Biotrickling Filter ◽  
Sequencing Analysis ◽  
Organic Sulfur Compounds ◽  
Volatile Organic ◽  
Bacterial Groups ◽  
Maximum Removal

The biotrickling filter (BTF) treatment is an effective way of dealing with air pollution caused by volatile organic compounds (VOCs). However, this approach is typically used for single VOCs treatment but not for the mixtures of VOC and volatile organic sulfur compounds (VOSCs), even if they are often encountered in industrial applications. Therefore, we investigated the performance of BTF for single and ternary mixture gas of dimethyl sulfide (DMS), propanethiol, and toluene, respectively. Results showed that the co-treatment enhanced the removal efficiency of toluene, but not of dimethyl sulfide or propanethiol. Maximum removal rates (rmax) of DMS, propanethiol and toluene were calculated to be 256.41 g·m−3·h−1, 204.08 g·m−3·h−1 and 90.91 g·m−3·h−1, respectively. For a gas mixture of these three constituents, rmax was measured to be 114.94 g·m−3·h−1, 104.17 g·m−3·h−1 and 99.01 g·m−3·h−1, separately. Illumina MiSeq sequencing analysis further indicated that Proteobacteria and Bacteroidetes were the major bacterial groups in BTF packing materials. A shift of bacterial community structure was observed during the biodegradation process.

scholarly journals Prediction of Volatile Organic Compounds (VOCs) From Decomposition of Local Household Food Waste Using the Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5773-5779
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Volatile Organic Compounds ◽  
Organic Compounds ◽  
Food Waste ◽  
Back Propagation ◽  
Ann Model ◽  
Neuron Network ◽  
Volatile Organic ◽  
Artificial Neural

This study examines the potential of artificial neural network (ANN) to predict Total Volatile Organic Compounds (TVOCs) released via decomposition of local food wastes. To mimic the decomposition process, a bioreactor was designed to stimulate the food waste storage condition. The food waste was modeled based on the waste composition from a residential area. A feed forward multilayer back propagation (Levenberg – Marquardt training algorithm) was then developed to predict the TVOCs. The findings indicate that a two-layer artificial neuron network (ANN) with six input variables and these include (outside and inside temperature, pH, moisture content, oxygen level, relative humidity) with a total of eighty eight (88) data are used for the modeling purpose. The network with the highest regression coefficient (R) is 0.9967 and the lowest Mean Square Error (MSE) is 0.00012 (nearest to the value of zero) has been selected as the Optimum ANN model. The findings of this study suggest the most suitable ANN model that befits the research objective is ANN model with one (1) hidden layer with fifteen (15) hidden neurons. Additionally, it is critical to note that the results from the experiment and predicted model are in good agreement.

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