scholarly journals Corrigendum to “Aging of tire and road wear particles in terrestrial and freshwater environments – A review on processes, testing, analysis and impact” [Chemosphere 288 (2022) 132467]

Chemosphere ◽  
2021 ◽  
pp. 133150
Author(s):  
Stephan Wagner ◽  
Philipp Klöckner ◽  
Thorsten Reemtsma
Keyword(s):  
Wear Particles ◽  
Freshwater Environments

Stabilizing pyritic material

1989 ◽  
Vol 4 ◽  
pp. 244-248 ◽  
Author(s):  
Donald L. Wolberg
Keyword(s):  
Significant Contribution ◽  
Organic Materials ◽  
Sedimentary Rocks ◽  
Iron Sulfides ◽  
Decomposition Products ◽  
Iron Minerals ◽  
Pyrite Formation ◽  
Organic Sediments ◽  
Freshwater Environments

The minerals pyrite and marcasite (broadly termed pyritic minerals) are iron sulfides that are common if not ubiquitous in sedimentary rocks, especially in association with organic materials (Berner, 1970). In most marine sedimentary associations, pyrite and marcasite are associated with organic sediments rich in dissolved sulfate and iron minerals. Because of the rapid consumption of sulfate in freshwater environments, however, pyrite formation is more restricted in nonmarine sediments (Berner, 1983). The origin of the sulfur in nonmarine environments must lie within pre-existing rocks or volcanic detritus; a relatively small, but significant contribution may derive from plant and animal decomposition products.

ANALYSIS OF AUTOMATIC TRANSMISSION USING FOURIER TRANSFORM INFRARED (FTIR) SPECTROCOPY

2019 ◽  
Vol 7 (SI-TeMIC18) ◽  
Author(s):  
Norhanifah Abdul Rahman ◽  
Matzaini Katon Katon ◽  
Nurina Alya Zulkifli Zulkifli
Keyword(s):  
Fourier Transform ◽  
Automatic Transmission ◽  
Fourier Transform Infrared ◽  
Oil Analysis ◽  
Wear Particles ◽  
Reliable Technique ◽  
Fluid Analysis ◽  
Analysis Technique ◽  
Machine Elements ◽  
Transmission Fluid

Automatic Transmission (AT) system is efficient in the aspects of vehicle safety, comfort, reliability and driving performance. The objectives of this paper are to collect the oil samples from AT systems of engine bus according to manufacturer's recommendations and analyse collected oil samples using oil analysis technique. The sample transmission fluid which was taken from the AT gearbox has been experimentally analyzed. The oil samples were taken with an interval of 5,000km, 30,000km, 50,000km, 80,000km, 180,000km and 300,000km for AT bus operation. These samples then have been analyzed by comparing between new and used transmission fluid using Fourier Transform Infrared (FTIR) spectroscopy. Oil analysis by FTIR is a form of Predictive Maintenance (PdM) to avoid major failure in machine elements. Most machine elements are not easily accessible in the transmission system. Having a reliable technique would avoid the needs to open the components unnecessarily, hence, help to prevent catastrophic failure which are very costly, and ease of regular monitoring. In order to identify the major failures of automatic gearbox, forecasts can be made regarding the lube transmission fluid analysis test. By using this test, the minor problems can be determined before they become major failures. At the end of this research, the wear particles profile for interval mileage of AT system was obtained. Keywords: Wear, Automatic Transmission (AT), Transmission fluid, Fourier Transform Infrared (FTIR), Oil analysis.

TIRE-WEAR PARTICLES AS A MAJOR COMPONENT OF MICROPLASTICS IN THE ENVIRONMENT

2018 ◽  
Author(s):  
Reto Gieré ◽  
◽  
Frank Sommer ◽  
Volker Dietze ◽  
Anja Baum ◽  
...  
Keyword(s):  
Wear Particles ◽  
Tire Wear

scholarly journals Tyre wear particles: an abundant yet widely unreported microplastic?

2020 ◽  
Vol 27 (15) ◽  
pp. 18345-18354 ◽  
Author(s):  
Lydia J. Knight ◽  
Florence N. F. Parker-Jurd ◽  
Maya Al-Sid-Cheikh ◽  
Richard C. Thompson
Keyword(s):  
Wear Particles ◽  
Tyre Wear

Microplastics in Freshwater Environments: Sources, Fates and Toxicity

2021 ◽  
Vol 232 (5) ◽  
Author(s):  
Runrun Ding ◽  
Ling Tong ◽  
Weicheng Zhang
Keyword(s):  
Freshwater Environments

scholarly journals Morphological residual convolutional neural network (M-RCNN) for intelligent recognition of wear particles from artificial joints

Friction ◽  
2021 ◽  
Author(s):  
Xiaobin Hu ◽  
Jian Song ◽  
Zhenhua Liao ◽  
Yuhong Liu ◽  
Jian Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Support Vector ◽  
Wear Particles ◽  
Artificial Joints ◽  
Performance Deterioration ◽  
Overall Performance ◽  
Correct Category ◽  
Intelligent Recognition

AbstractFinding the correct category of wear particles is important to understand the tribological behavior. However, manual identification is tedious and time-consuming. We here propose an automatic morphological residual convolutional neural network (M-RCNN), exploiting the residual knowledge and morphological priors between various particle types. We also employ data augmentation to prevent performance deterioration caused by the extremely imbalanced problem of class distribution. Experimental results indicate that our morphological priors are distinguishable and beneficial to largely boosting overall performance. M-RCNN demonstrates a much higher accuracy (0.940) than the deep residual network (0.845) and support vector machine (0.821). This work provides an effective solution for automatically identifying wear particles and can be a powerful tool to further analyze the failure mechanisms of artificial joints.

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