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1.
The influence of different referencing methods on the accuracy of δ13C value measurement of ethanol fuel by gas chromatography/combustion/isotope ratio mass spectrometry 下载免费PDF全文
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Spyridon Koutsoukos Frederik Philippi Francisco Malaret Tom Welton 《Chemical science》2021,12(20):6820
There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids. 相似文献
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Guizellini Filipe Corrêa Franzin Bruno Trevizan da Silva Matheus Antonio Abra Luciana Mazotti Hojo Ossamu Pastre Iêda Aparecida Ribeiro Clóvis Augusto Paiva-Santos Carlos de Oliveira Fertonani Fernando Luis 《Journal of Thermal Analysis and Calorimetry》2021,143(1):47-60
Journal of Thermal Analysis and Calorimetry - The aim of this work is to synthesize and characterize a new structured silver–clay dried, calcined or sintered at different temperatures... 相似文献
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In this paper, we study the local linear convergence properties of a versatile class of Primal–Dual splitting methods for minimizing composite non-smooth convex optimization problems. Under the assumption that the non-smooth components of the problem are partly smooth relative to smooth manifolds, we present a unified local convergence analysis framework for these methods. More precisely, in our framework, we first show that (i) the sequences generated by Primal–Dual splitting methods identify a pair of primal and dual smooth manifolds in a finite number of iterations, and then (ii) enter a local linear convergence regime, which is characterized based on the structure of the underlying active smooth manifolds. We also show how our results for Primal–Dual splitting can be specialized to cover existing ones on Forward–Backward splitting and Douglas–Rachford splitting/ADMM (alternating direction methods of multipliers). Moreover, based on these obtained local convergence analysis result, several practical acceleration techniques are discussed. To exemplify the usefulness of the obtained result, we consider several concrete numerical experiments arising from fields including signal/image processing, inverse problems and machine learning. The demonstration not only verifies the local linear convergence behaviour of Primal–Dual splitting methods, but also the insights on how to accelerate them in practice. 相似文献
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Herrasti P. Mazarío E. Recio Francisco J. 《Journal of Solid State Electrochemistry》2021,25(1):231-236
Journal of Solid State Electrochemistry - In this work, the use of neodymium electrodes as a basis for the immobilization of magnetite nanoparticles has been carried out. The sensitivity and... 相似文献
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Condeixa Lucas Dias Silva Pierry Moah Diego Farias Brenda Leiras Adriana 《Central European Journal of Operations Research》2022,30(3):921-940
Central European Journal of Operations Research - Societal awareness and legislation changes concerning sustainability have affected how organizations generate value for stakeholders, as well as... 相似文献