Ultra-Fine Particles. Exploratory Science and Technology

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Because particle generation during braking is a stochastic process, five replicate tests were performed under each temperature condition to obtain more generalized results [ 18 ].

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Equipment operation and experimental condition changes were conducted remotely from a control room, and pollutants generated during the braking test process were purified with a ventilation unit. A fast mobility particle sizer FMPS, TSI was used to measure fine and ultrafine particles generated during the braking test, and the sampling probe was installed approximately 5 cm from the braking area. The FMPS determined the particle size by controlling the charging state of nanoparticles and measuring electrical mobility with current capacity. It also provided the particle size distribution data of 32 channels ranging in size from 5.

Details of the channels are shown in Table S1. Data Analysis 2. Data preprocessing A dataset was established from the braking tests and was subject to preprocessing for more efficient interdependence analysis. To account solely for particle concentrations that result from braking, the mean particle concentration during the 5 s prior to braking was subtracted from the measured particle concentration. In addition, the effects due to differences in the particle concentration ranges from each test were removed by normalization, whereby the data were adjusted to range from 0 to 1.

Note that the class 1 corresponded to the size channels 1—20, and class 2 corresponded to the channels 21— Clustered heatmaps A clustered heatmap uses a color scale to shade values in a data matrix and displays the hierarchical cluster dendrograms of each row and column on the margins. Clustered heatmaps show data patterns by integrating information into a single figure and have been used recently in the biological sciences [ 20 ].

Four factors must be determined to generate a clustered heatmap.

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First, a preprocessing algorithm which can minimize noise is needed; in this study, a normalization algorithm was applied in the data preprocessing section. Second, a clustered heatmap requires a hierarchical clustering method. Third, a distance metric is needed to measure the space between clusters. Finally, an appropriate color scheme for the heatmap is required [ 22 ]. The current study used the pheatmap function [ 23 ] in the R programming language [ 24 ] to generate clustered heatmaps.

This robust method featured by relative insensitivity to noise and outliers tends to cluster groups of similar sizes [ 25 , 26 ]. The Euclidean metric was used as the distance metric, and the color scheme was RGB: red indicating higher values, and blue indicating lower values. The number of clusters was set at two to identify if the generated clusters generally coincide with the size classes Table S2. Data analysis procedure To characterize the differences in particle emission patterns under low and high temperature conditions, particle size distributions, time series maps, and correlation heatmaps were analyzed.

The analysis process was largely divided into exploratory and clustered heatmap analyses.

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In the exploratory analysis, particle size distributions were analyzed to determine the peak channel of each temperature condition and test. A particle size distribution was plotted using the mean particle concentration that occurred during the braking period across the size channels, and the total particle concentrations were compared prior to plotting.

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The dataset that was not normalized was used to compare particle concentrations among the tests. In addition, to examine the interdependence between particle generation characteristics with braking time in each temperature condition and size channel, time series maps and correlation heatmaps were created.

In the clustered heatmap analysis, the hierarchical clustering approach was applied to the heatmap to generate the clustered time series map and the clustered correlation heatmap. Results and Discussion Different patterns among replicate tests may be due to the stochastic nature of brake wear particles.

The stochasticity may arise from a combination of multiple factors, such as abrasion, adhesion, fatigue, delamination, and thermal decomposition, the effects of which all vary depending on brake wear mechanism conditions [ 18 , 27 — 29 ]. Despite the differences, a common trend was obtained for each temperature condition, suggesting that temperature affects the particle generation patterns during the braking of railway vehicles.

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Ultra-Fine Particles - 1st Edition - ISBN: , View on ScienceDirect . Ultra-Fine Particles - Exploratory Science and Technology. ykoketomel.ml: Ultra-Fine Particles: Exploratory Science and Technology ( Materials Science and Process Technology) (): Tyozi Uyeda, Chikara.

Particle Size Distributions Total particle number concentration generated from five tests in each of low and high temperature condition is listed in Table 2. The lowest particle concentration generated under low temperature was about an order of magnitude smaller than the lowest concentration generated under high temperature.

The number of peaks for the distributions differed between two temperature conditions. Except for one case test 2 , low temperature conditions exhibited unimodal distributions, with the peak located within size channel 24, which belongs to the size class 2. The bimodal shape was clearly seen in tests 1—4 under high temperature but was less evident in test 5. One peak of the bimodal distributions under high temperature was found within size channels 23—24, similar to the peak location under low temperature. The other peak for the bimodal distributions was located at the smaller particle sizes, size channels 17—18, except for test 1 that exhibits a peak at size channel 12, which all belong to size class 1.

Temporal Patterns of Wear Particles Fig. The heatmap illustrates that under low temperature, particles with high concentration generally ranged between size channels 21—28, and occurred most frequently at 25—36 s, the latter end of the braking period Fig. In contrast to the low temperature, at high temperature, the two peaks in terms of particle size occurred dispersedly along the braking period.

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Also, the peak timing was inconsistent among the replicate tests Fig. The clustered heatmap illustrates the hierarchical clustering results of the particle size channels for each time step along the braking time Fig. Clustering results in low temperature conditions demonstrate that there was one cluster cluster 1 of channels with high particle concentrations, while the other cluster cluster 2 contained channels with low particle concentrations. In the majority of the tests, the particle sizes with high occurrence frequency were, on average, larger than the particle sizes with low occurrence frequency throughout the braking period.

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The indoor sampling heights have been varying between Current evidence does, however, support a chain of events involving pollution-induced pulmonary and systemic oxidative stress and inflammation, translocation of particle constituents and an associated risk of vascular dysfunction, atherosclerosis, altered cardiac autonomic function and ischaemic cardiovascular and obstructive pulmonary diseases Kelly and Fussell ; Fig. The direction of the observation is along the 5fold symmetry axis. Electron and laser beams were also examined as heat sources. Obtaining a bright beam without losing the resolution was a technical challenge [14]. Advance article alerts. When participants were split into groups based on pressure point sensitivity — high PPHigh and low PPLow acuity — there was a significant relationship between chocolate discrimination and pressure point sensitivity for the PPHigh group on the center tongue.

The test 2 was an exception, in which small particles occurred more frequently than large particles did Fig. As in the low temperature condition, under high temperature two clusters were generated in terms of occurrence frequency: cluster 1 with high particle concentrations and cluster 2 with low particle concentrations.

In contrast to the results from low temperature, however, across the tests under high temperature, particle sizes in one cluster were not consistently larger or smaller than those in the other cluster Fig. Correlations among Wear Particles Fig. Regardless of the temperature, particles tended to covary with the similar sized particles. Given the low temperature, in particular, particles larger than approximately 80 nm showed a consistently high correlation to each other Fig. Consistent with previous results Fig. All Pages Books Journals. View on ScienceDirect. Hardcover ISBN: Imprint: William Andrew.

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