Nanotribology of metallic glasses in corrosive environments

Metallic glasses (MGs) are promising materials for micromechanical systems, where miniaturized components involving mechanical contact require control of friction. Nanotribological experiments on MGs in corrosive aqueous solutions are carried out using atomic force microscopy (AFM), focusing on the role of surface oxide films formed during corrosion. A new method is developed to study in situ the structure of surface oxide films. The surface oxide film has a bilayer structure as revealed by repeated scanning with the AFM tip. The dependence of friction on electrochemical potential reveals the growth mechanism of the oxide film. Friction and adhesion after different immersion times in different solutions allow to compare the physicochemical processes of surface dissolution at the interfaces of the two layers of surface films and elucidate their influence on friction. An irregular atomic-scale stick-slip friction is observed and attributed to the amorphous nature of corroded surfaces. Finally, we show three different friction processes occurring at increasing normal loads: removal of the dissolution layer at low-load regime; stress-assisted tribo-oxidation in intermediate-load regime; and tribochemical wear in high-load regime. The chemical sensitivity of nanotribology studies demonstrates a novel route to explore fundamental mechanisms of corrosion at the microscopic scale.Metallische Gläser (MG) sind vielversprechende Materialien für mikromechanische Systeme, in denen der mechanische Kontakt eine Kontrolle über Reibung erfordert. Mit Hilfe der Rasterkraftmikroskopie (AFM) wurden nanotribologische Experimente auf MG in korrosiven wässrigen Lösungen durchgeführt, wobei die Rolle von Oxidfilmen im Fokus stand. Eine neuartige Methode für die in situ-Untersuchung der Struktur der Oberflächenoxidfilme wurde entwickelt. Der Oberflächenoxidfilm weist eine zweilagige Struktur auf, die durch wiederholtes Rastern mit der AFM-Spitze nachgewiesen wurde. Die Abhängigkeit der Reibung vom elektrochemischen Potential zeigt die Wachstumsmechanismen der Oxidfilme an. Reibung und Adhäsion nach verschieden langer Immersion erlauben den Vergleich der physikochemischen Prozesse der Oberflächenauflösung an der Grenzfläche der beiden Lagen. Es wurde eine unregelmäßige stick-slip Reibung auf atomarer Skala beobachtet und auf die amorphe Natur der korrodierten Oberflächen zurückgeführt. Schließlich beschreiben wir drei verschiedene Reibungsprozesse, die mit zunehmender Normalkraft auftreten: die Abtragung der abgeschiedenen Lage bei niedrigen Auflagekräften, eine durch mechanische Spannung unterstützte Tribo-Oxidation bei mittleren Kräften sowie tribochemischen Verschleiß bei hohen Kräften. Die chemische Empfindlichkeit der nanotribologischen Studien zeigt eine neue Möglichkeit auf, grundsätzliche Mechanismen der Korrosion auf der mikroskopischen Skala zu erforschen

Relationship between corrosion and nanoscale friction on a metallic glass

Metallic glasses are promising materials for microdevices, although corrosion and friction limit their effectiveness and durability. We investigated nanoscale friction on a metallic glass in corrosive solutions after different periods of immersion time using atomic force microscopy to elucidate the influence of corrosion on nanoscale friction. The evolution of friction upon repeated scanning cycles on the corroded surfaces reveals a bilayer surface oxide film, of which the outer layer is removed by the scanning tip. The measurement of friction and adhesion allows one to compare the physicochemical processes of surface dissolution at the interface of the two layers. The findings contribute to the understanding of mechanical contacts with metallic glasses under corrosive conditions by exploring the interrelation of microscopic corrosion mechanisms and nanoscale friction

A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images

In the past decade, using Street View images and machine learning to measure human perception has become a mainstream research approach in urban science. However, this approach using only image-shallow information makes it difficult to comprehensively understand the deep semantic features of human perception of a scene. In this study, we proposed a new framework based on a pre-train natural language model to understand the relationship between human perception and the sense of a scene. Firstly, Place Pulse 2.0 was used as our base dataset, which contains a variety of human-perceived labels, namely, beautiful, safe, wealthy, depressing, boring, and lively. An image captioning network was used to extract the description information of each street view image. Secondly, a pre-trained BERT model was finetuning and added a regression function for six human perceptual dimensions. Furthermore, we compared the performance of five traditional regression methods with our approach and conducted a migration experiment in Hong Kong. Our results show that human perception scoring by deep semantic features performed better than previous studies by machine learning methods with shallow features. The use of deep scene semantic features provides new ideas for subsequent human perception research, as well as better explanatory power in the face of spatial heterogeneity.Comment: 11 pages, 8 figure

Crystal structure informed mesoscale deformation model for HCP Cu6Sn5 intermetallic compound

In the electronic packaging and energy storage sectors, the study of Cu6Sn5 intermetallic compound (IMC) is getting more attention. At temperatures above 186 oC, this IMC exists in a hexagonal closed packed (HCP) crystalline structure. Crystal plasticity finite element simulations are performed on Cu6Sn5 IMC by taking the information about its lattice parameters and direction dependent elastic properties. Three types of models corresponding to deformations in basal, prismatic and pyramidal modes are developed. With the same type of loading in the elastic regime and boundary conditions, the results of the computations reveal the differences in displacement magnitudes among the three model types

How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery

The Attention Restoration Theory (ART) presents a theoretical framework with four essential indicators (being away, extent, fascinating, and compatibility) for comprehending urban and natural restoration quality. However, previous studies relied on non-sequential data and non-spatial dependent methods, which overlooks the impact of spatial structure defined here as the positional relationships between scene entities on restoration quality. The past methods also make it challenging to measure restoration quality on an urban scale. In this work, a spatial-dependent graph neural networks (GNNs) approach is proposed to reveal the relation between spatial structure and restoration quality on an urban scale. Specifically, we constructed two different types of graphs at the street and city levels. The street-level graphs, using sequential street view images (SVIs) of road segments to capture position relationships between entities, were used to represent spatial structure. The city-level graph, modeling the topological relationships of roads as non-Euclidean data structures and embedding urban features (including Perception-features, Spatial-features, and Socioeconomic-features), was used to measure restoration quality. The results demonstrate that: 1) spatial-dependent GNNs model outperforms traditional methods (Acc = 0.735, F1 = 0.732); 2) spatial structure portrayed through sequential SVIs data significantly influences restoration quality; 3) spaces with the same restoration quality exhibited distinct spatial structures patterns. This study clarifies the association between spatial structure and restoration quality, providing a new perspective to improve urban well-being in the future.Comment: 33 pages, 7 figures, Under revie

Isolation Mondrian Forest for Batch and Online Anomaly Detection

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.Comment: Accepted for presentation at the IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2020. The first three authors contributed equally to this wor

Optimal Planning for Deepwater Oilfield Development Under Uncertainties of Crude Oil Price and Reservoir

The development planning of deepwater oilfield directly influences production costs and benefits. However, the uncertainties of crude oil price and reservoir and the special production requirements make it difficult to optimize development planning of deepwater oilfield. Although there have been a number of scholars researching on this issue, previous models just focused on several special working conditions and few have considered energy supply of floating production storage and offloading (FPSO). In light of the normal deepwater production development cycles, in this paper, a multiscenario mixed integer linear programming (MS-MILP) method is proposed based on reservoir numerical simulation, considering the uncertainties of reservoir and crude oil price and the constraint of energy consumption of FPSO, to obtain the globally optimal development planning of deepwater oilfield. Finally, a real example is taken as the study objective. Compared with previous researches, the method proposed in this paper is testified to be practical and reliable