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Low-frequency laboratory measurements of the elastic properties of solids using a distributed acoustic sensing system

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Journal of Rock Mechanics and Geotechnical Engineering 2023年第9期15卷 2330-2338页

作者： Vassily Mikhaltsevitch maxim Lebedev Roman Pevzner Alexey Yurikov Konstantin Tertyshnikov Curtin University GPO Box U1987PerthWA 6845Australia Centre for Sustainable Energy and Resources Edith Cowan University270 Joondalup DrJoondalupWA 6027Australia

In recent decades,low-frequency(LF)experiments based on the forced-oscillation(FO)method have become common practice in many rock physics laboratories for measuring the elastic and anelastic properties of ***,the use of the electronic displacement sensors in traditional acquisition systems of FO devices such as conventional capacitive transducers or strain gauges seriously limits both the efficiency and productivity of LF measurements,and,due to the limited contact area of the displacement sensors with a sample under test,increases the requirements for sample *** this paper,we present the first results obtained in the development of a new laboratory method elaborated to measure the elastic properties of *** method is a further development of the FO method where traditional data acquisition is replaced by acquisition based on fiber-optic distributed acoustic sensing(DAS)*** new method was tested in a laboratory study using two FO setups designed for measurements under uniaxial and confining *** study was carried out on a sample made from polymethyl methacrylate(PMMA)and an aluminium standard,first under uniaxial pressure at FO frequencies of 1,10,30,60 and 100 Hz,and then under confining pressure at an FO frequency of 1 *** uniaxial and confining pressures were equal to 10 MPa,and the strain in the PMMA sample in all measurements did not exceed 4×10^(-8).The performance of DAS acquisition was compared with the measurements conducted at a strain of 1×10^(-6) using the traditional FO method based on the use of semiconductor strain gauges and the ultrasonic *** results of the DAS measurements are in good agreement with the FO measurements carried out using semiconductor strain gauges and with the literature data.

关键词： Elasticity and anelasticity Acoustic properties Fourier analysis Wave propagation

Effects of coals microscale structural features on their mechanical properties,propensity to crushing and fine dust formation

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International Journal of Coal Science & Technology 2023年第2期10卷 146-158页

作者： Elena Kossovich Svetlana Epshtein Vera Krasilova Jie Hao maxim Minin National University of Science and Technology"MISIS" 4build.1Leninsky prospectMoscow 119049Russian Federation

The work is dedicated to revealing the structural features of coals with different ranks,such as anthracites,metaanthracite and graphite,that determine their ability to crush and form fine *** this purpose,a combination of various nanoin-dentation techniques and Raman spectroscopy was *** mechanical behavior of the selected coals was investigated by cyclic nanoindentation with increasing peak load and quasi-static *** alteration of the mechanical properties was studied by analysis of elastic moduli and damage indices *** groups of coals were identified based on their propensity to crushing during cyclic *** assigned to the first and second groups are characterized by local destruction in the contact zone with the indenter and the formation of a core of crushed *** assigned to the third group are characterized by bulk destruction(outside the zone of contact with the indenter).In general,the ability of coals to fracture under mechanical loading decreases in the series of metamorphism due to microscale compaction of vitrinite *** the series of anthracite,metaanthracite and graphite,it is established that the coal matter compaction takes place for the anthracite and metaanthracite,whereas graphite reveals rather different behavior due to abrupt change of its *** ratios between the amorphous and crystalline phases of carbon(S)were determined by deconvolution of coals Raman *** propensity of coals to crushing(a damage index Rw)increases with growth of the proportion of amorphous carbon in the coal *** the considered coals and metaanthracite,it is established that the proneness to destruction outside the contact zone with the indenter is determined by the ratio of amorphous and crystalline carbon of 1 and *** S parameter is lower than 1,the coals are being crushed only in the zone of contact with the indenter.

关键词： Coal Crushing Cyclic nanoindentation Raman spectroscopy Amorphous carbon Crystalline carbon

Revisiting Emergency Food Reserve Policy and Practiceunder Disaster and Extreme Climate Events

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International Journal of Disaster Risk Science 2019年第1期10卷 1-13页

作者： Jonatan A.Lassa Paul Teng Mely Caballero-Anthony maxim Shrestha Emergency and Disaster Management Studies Charles Darwin University Natural Sciences and Science Education National Institute of Education Centre for Non-Traditional Security Studies S. Rajaratnam School of International Studies (RSIS) Nanyang Technological University

All food systems will continue to be affected by disasters and extreme climate events. Triggered by recent food crises around the world and climate change concerns,some governments have been trying to develop more robust and resilient food systems. One of the oldest options for many governments is to stockpile emergency food reserves for the purpose of food security and disaster preparedness. In the aftermath of the world food price crises in2007–2008 and 2011, some governments in Asia have been maintaining emergency food reserves to ensure greater supply and price stability. Disasters and extreme climate events help governments to justify emergency food reserves. This research examined emergency food reserve policies in Indonesia, the Philippines, and *** food reserves emerged as a practice where the shared objectives of development, disaster risk reduction,and climate change adaptation have been demonstrated by governments. The findings suggest that most governments maintain the strong view that adequate emergency food reserves can buffer national food price shocks and shocks from disasters and climate change, and soften disruptions in trade due to export bans during times of disasters and climate *** global climate change scenarios, food security is at risk and volatile(Porter et al. 2014). The expected increase in climate extremes has generated anticipatory actions from governments, including a new push for EFR policy adoption. Triggered by recent disasters and climate change concerns, some governments have been trying to develop more robust and resilient food systems(Fan and Brzeska 2014; Porter et al. 2014). For many countries in Asia, this means the renewed adoption of EFR. Unfortunately, we argue that this is not well understood in climate change adaptation studies as well as contemporary disaster *** Association of Southeast Asian Nations(ASEAN)first initiated a Food Security Reserve Agreement in 1979,with the purpose of

关键词： Climate change adaptation Disaster preparedness Emergency food reserves Food security Food stockpiling Southeast Asia

Design of small-scale gradient coils in magnetic resonance imaging by using the topology optimization method

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Chinese Physics B 2018年第5期27卷 91-100页

作者： Hui Pan Feng Jia Zhen-Yu Liu maxim Zaitsev Juergen Hennig Jan G Korvink Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China University of Chinese Academy of Sciences Beijing 100049China Deptartment of Radiology Medical PhysicsMedical Center University of FreihurgFaculty of MedicineUniversity of FreiburgFreiburg 79106Germany Institute of Microstructure Technology Karlsruhe Institute of Technology(KIT)Karlsruhe 76344Germany

A topology optimization method based on the solid isotropic material with penalization interpolation scheme is utilized for designing gradient coils for use in magnetic resonance *** the popular stream function method,the proposed method has design variables that are the distribution of conductive material.A voltage-driven transverse gradient coil is proposed to be used as micro-scale magnetic resonance imaging(MRI)gradient coils,thus avoiding introducing a coil-winding pattern and simplifying the coil *** proposed method avoids post-processing errors that occur when the continuous current density is approximated by discrete wires in the stream function *** feasibility and accuracy of the method are verified through designing the z-gradient and y-gradient coils on a cylindrical *** design results show that the proposed method can provide a new coil layout in a compact design space.

关键词： topology optimization method gradient coils solid isotropic material with penalization magnetic resonance imaging

Off-the-shelf deep learning is not enough,and requires parsimony,Bayesianity,and causality

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npj Computational Materials 2021年第1期7卷 133-138页

作者： Rama K.Vasudevan maxim Ziatdinov Lukas Vlcek Sergei.V.Kalinin Center for Nanophase Materials Sciences Oak RidgeTN 37831USA Computational Sciences and Engineering Division Oak RidgeTN 37831USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Present address:Bayer St.LouisMO 63141USA

Deep neural networks(‘deep learning’)have emerged as a technology of choice to tackle problems in speech recognition,computer vision,finance,***,adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal,hypothesis driven nature of modern *** argue that the broad adoption of Bayesian methods incorporating prior knowledge,development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models,and ultimately adoption of causal models,offers a path forward for fundamental and applied research.

关键词： learning enough finance

Machine Learning Application for Prediction of Sapphire Crystals Defects

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电子科技学刊：英文版 2020年第1期18卷 1-9页

作者： Yulia Vladimirovna Klunnikova maxim Vladimirovich Anikeev Alexey Vladimirovich Filimonov Ravi Kumar Institute of Nanotechnology Electronics and Engineering EquipmentSouthern Federal UniversityTaganrog 347928 Institute of Computer Technology and Information Security Southern Federal UniversityTaganrog347928 Department of Physical Electronics Peter the Great St.Petersburg Polytechnic UniversitySt.Petersburg 195251 the Department of Metallurgical and Materials Engineering Indian Institute of Technology MadrasChennai600036

We investigate the impact of different numbers of positive and negative examples on machine learning for sapphire crystals defects prediction. We obtain the models of crystal growth parameters influence on the sapphire crystal growth. For example, these models allow predicting the defects that occur due to local overcooling of crucible walls in the thermal node leading to the accelerated crystal growth. We also develop the prediction models for obtaining the crystal weight, blocks, cracks, bubbles formation, and total defect characteristics. The models were trained on all data sets and later tested for generalization on testing sets, which did not overlap the training *** training and testing, we find the recall and precision of prediction, and analyze the correlation among the features. The results have shown that the precision of the neural network method for predicting defects formed by local overcooling of the crucible reached 0.94.

关键词： Defects machine learning sapphire crystals

Understanding the Li diffusion mechanism and positive effect of current collector volume expansion in anode free batteries

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Chinese Physics B 2020年第6期29卷 17-24页

作者： Yan Zhuang Zheyi Zou Bo Lu Yajie Li Da Wang maxim Avdeev Siqi Shi State Key Laboratory of Advanced Special Steel Shanghai Key Laboratory of Advanced FerrometallurgySchool of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering ScienceShanghai UniversityShanghai 200444China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai UniversityShanghai 200444China Australian Nuclear Science and Technology Organisation Locked Bag 2001Kirrawee DC NSW 2232Australia School of Chemistry The University of SydneySydney 2006Australia

In anode free batteries(AFBs), the current collector acts as anode simultaneously and has large volume expansion which is generally considered as a negative effect decreasing the structural stability of a battery. Moreover, despite many studies on the fast lithium diffusion in the current collector materials of AFB such as copper and aluminum, the involved Li diffusion mechanism in these materials remains poorly understood. Through first-principles calculation and stress-assisted diffusion equations, here we study the Li diffusion mechanism in several current collectors and related alloys and clarify the effect of volume expansion on Li diffusion respectively. It is suggested that due to the lower Li migration barriers in aluminum and tin, they should be more suitable to be used as AFB anodes, compared to copper, silver, and lead. The Li diffusion facilitation in copper with a certain number of vacancies is proposed to explain why the use of copper with a thickness≤100 nm as the protective coating on the anode improves the lifetime of the batteries. We show that the volume expansion has a positive effect on Li diffusion via mechanical–electrochemical coupling. Namely, the volume expansion caused by Li diffusion will further induce stress which in turn affects the diffusion. These findings not only provide in-depth insight into the operating principle of AFBs, but also open a new route toward design of improved anode through utilizing the positive effect of mechanical–electrochemical coupling.

关键词： anode free battery current collector Li diffusion mechanism mechanical-electrochemical coupling stress-assisted diffusion

Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

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npj Computational Materials 2021年第1期7卷 909-916页

作者： Ayana Ghosh Bobby G.Sumpter Ondrej Dyck Sergei V.Kalinin maxim Ziatdinov Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines,allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of ***,applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments,where the network trained for one set of imaging conditions becomes sub-optimal for different *** limitation is particularly stringent in the quest to have an automated experiment setting,where retraining or transfer learning becomes impractical due to the need for human intervention and associated *** we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature *** approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the *** methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

关键词： learning experiment enable