scholarly journals Deep Regression Prediction of Rheological Properties of SIS-Modified Asphalt Binders

Materials ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 5738
Author(s):  
Bongjun Ji ◽  
Soon-Jae Lee ◽  
Mithil Mazumder ◽  
Moon-Sup Lee ◽  
Hyun Hwan Kim
Keyword(s):  
Regression Model ◽  
Asphalt Binder ◽  
Engineering Properties ◽  
Asphalt Binders ◽  
Percentage Error ◽  
Engineering Property ◽  
Force Microscopy ◽  
Atomic Force ◽  
Specialized Equipment ◽  
Selection Of

The engineering properties of asphalt binders depend on the types and amounts of additives. However, measuring engineering properties is time-consuming, requires technical expertise, specialized equipment, and effort. This study develops a deep regression model for predicting the engineering property of asphalt binders based on analysis of atomic force microscopy (AFM) image analysis to test the feasibility of replacing traditional measuring estimate techniques. The base asphalt binder PG 64-22 and styrene–isoprene–styrene (SIS) modifier were blended with four different polymer additive contents (0%, 5%, 10%, and 15%) and then tested with a dynamic shear rheometer (DSR) to evaluate the rheological data, which indicate the rutting properties of the asphalt binders. Different deep regression models are trained for predicting engineering property using AFM images of SIS binders. The mean absolute percentage error is decisive for the selection of the best deep regression architecture. This study’s results indicate the deep regression architecture is found to be effective in predicting the G*/sin δ value after the training and validation process. The deep regression model can be an alternative way to measure the asphalt binder’s engineering property quickly. This study would encourage applying a deep regression model for predicting the engineering properties of the asphalt binder.

scholarly journals Deposition Of Oxide And Intermetallic Thin Films By Pulsed Laser (PLD) And Electron Beam (PED) Methods

2015 ◽  
Vol 60 (3) ◽  
pp. 2173-2182 ◽  
Author(s):  
J. Kusiński ◽  
A. Kopia ◽  
Ł. Cieniek ◽  
S. Kąc ◽  
A. Radziszewska
Keyword(s):  
Thin Films ◽  
Electron Beam ◽  
Pulsed Laser ◽  
Nanocrystalline Structure ◽  
X Ray Diffraction ◽  
Pulsed Electron Beam ◽  
Force Microscopy ◽  
Atomic Force ◽  
Basic Parameters ◽  
Selection Of

Abstract In this work the pulsed laser deposition (PLD) and the pulsed electron beam deposition (PED) techniques were used for fabrication of Mo-Bi2O3, La1−xSrxCoO3, La1−xCaxCoO3 and Al-Mg thin films. An influence of ablation process basic parameters on the coatings structure and properties was discussed. Two types of laser ablation systems were applied: one equipped with a KrF excimer and second with a Q-switched Nd:YAG. Films were deposited on Si and MgO substrates. Scanning (SEM) and transmission (TEM) electron microscopy, atomic force microscopy (AFM) as well as X-ray diffraction (XRD) were used for structural analysis. Investigations focused on structure and chemical composition showed that smooth and dense thin films with nanocrystalline structure, preserving the composition of the bulk target, could be obtained by the both PLD and PED techniques. Research study showed that by a proper selection of PLD and PED process parameters it was possible to deposit films with significantly decreased amount and size of undesirably nanoparticulates.

scholarly journals Machine Learning-Based Multidomain Processing for Texture-Based Image Segmentation and Analysis

2020 ◽  
Author(s):  
Nikolay Borodinov ◽  
Wan-Yu Tsai ◽  
Vladimir V. Korolkov ◽  
Nina Balke ◽  
Sergei Kalinin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Topological Defects ◽  
Physical Parameters ◽  
General Strategy ◽  
Nanoscale Systems ◽  
Force Microscopy ◽  
Atomic Force ◽  
Periodical Structure ◽  
Physical And Chemical ◽  
Selection Of

<a>Atomic and molecular resolved atomic force microscopy (AFM) images </a>offer unique insights into materials properties such as local ordering, molecular orientation and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. Usage of supervised learning and feature extraction allows to retain the consideration of specific problem-dependent features (such as types of periodical structure observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for segmentation of molecular resolved AFM images based on crystal orientation of observed domains, automated selection of boundaries and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique.

scholarly journals First person – Shigetaka Nishiguchi

2021 ◽  
Vol 134 (14) ◽  
Keyword(s):  
Natural Sciences ◽  
Early Career ◽  
First Person ◽  
Exploratory Research ◽  
Force Microscopy ◽  
Early Career Researchers ◽  
Atomic Force ◽  
Afm Imaging ◽  
Selection Of ◽  
Ancestral Type

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Shigetaka Nishiguchi is first author on ‘ Structural variability and dynamics in the ectodomain of an ancestral-type classical cadherin revealed by AFM imaging’, published in JCS. Shigetaka conducted the research described in this article while an assistant manager at Olympus Corporation and a graduate student in Hiroki Oda's lab at the JT Biohistory Research Hall and Osaka University, Osaka, Japan. He is now a postdoc in the lab of Takayuki Uchihashi at the Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan, investigating cadherin using atomic force microscopy.

Engineering Properties of Nuclear Waste Slurries

2009 ◽  
Author(s):  
Simon Biggs ◽  
Michael Fairweather ◽  
Timothy Hunter ◽  
Qanitalillahi Omokanye ◽  
Jeffrey Peakall
Keyword(s):  
Nuclear Waste ◽  
Engineering Properties ◽  
Particle Interactions ◽  
Force Microscopy ◽  
Particle Properties ◽  
Atomic Force ◽  
Nuclear Systems ◽  
Ph Conditions ◽  
Particulate Systems

The type of particulate systems encountered in legacy nuclear waste slurries is highly complicated, with the aggregation and flow behaviour being at times very variable. However, deconstructing the complex overall slurry activity to singular particle-particle interactions can lead to a greater understanding of the mechanisms involved with particle aggregation, and so to predictions of their settling and flow in nuclear systems. Of particular importance to legacy waste is the role of salts in controlling the attraction of particles (and so in dictating the rheological properties of the system) as sludge may contain a variety of specific ions and generally have high ionic conductivity [1]. In this paper, particle-particle interactions are characterised using a number of complimentary methods, and their influence on resulting flow and bed compression is measured. The methods used to characterise the particle-particle interactions under various salt and pH conditions were electroacoustic analysis (zeta potential) and atomic force microscopy (AFM). Following on from the analysis of particle-particle properties, bulk sediment behaviour was investigated using shear and compressive yield stress measurements, vital parameters in dictating flow and dewatering performance, respectively. Together, these techniques enable the characterisation of a range of particulate systems that may be encountered in legacy wastes, and results point to a number of important factors that can help explain the observed variability in industrial slurry behaviour.

scholarly journals Micromechanism of the Dispersion Behavior of Polymer-Modified Rejuvenators in Aged Asphalt Material

2018 ◽  
Vol 8 (9) ◽  
pp. 1591 ◽  
Author(s):  
Mingyu Zhao ◽  
Fan Shen ◽  
Qingjun Ding
Keyword(s):  
Asphalt Binder ◽  
Gel Permeation Chromatography ◽  
Force Microscopy ◽  
Atomic Force ◽  
Dispersion Behavior ◽  
Dispersion Agent ◽  
Resistance To Deformation ◽  
Aged Asphalt ◽  
Dispersal Behavior ◽  
Pavement Engineering

Polymer-modified rejuvenator has a different composition and dispersion behavior to traditional rejuvenators. The objective of this study was to investigate the micromechanism of polymer-modified rejuvenators on the behavior of aged asphalt binder. Firstly, gel permeation chromatography (GPC) analysis was conducted to determine the dispersion effectiveness. Secondly, the dispersal behavior of polymer-modified rejuvenators was studied by means of atomic force microscopy (AFM) and scanning electron microscopy (SEM). Rheological, toughness-tenacity, and force–ductility analyses of the rejuvenated asphalt binder were additionally performed. The results indicate that the contacted asphaltenic micelles in aged asphalt binder were dispersed by dispersion agent in the polymer-modified rejuvenator, and that the dispersion ability of the polymer-modified rejuvenator was promoted to the commercial rejuvenator level. Additionally, the polymer-modified rejuvenator was found to improve the rejuvenated asphalt binder’s resistance to deformation, through the formation of polymeric network structures in the asphalt binder. The results may be used to improve the performance of rejuvenated asphalt binder in recycled-pavement engineering.

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