Manipulating droplet jumping behaviors on hot substrates with surface topography by controlling vapor bubble growth: from vibration to explosion

A major challenge in surface science is rapid removal of sessile liquid droplets from a substrate with complex three-dimensional structures. However, our understanding of interfacial phenomena including droplet wetting dynamics and phase changes on engineered surfaces remains elusive, impeding dexterous designs for agile droplet purging. Here we present a surface topography strategy to modulate droplet jumping behaviors on micropillared substrates at moderate superheat of 20-30 {\deg}C. Specifically, sessile droplets usually dwell in the Wenzel state and therefore the micropillar matrix functions as fin array for heat transfer enhancement. By tuning the feature sizes of micropillars, one can adjust the vapor bubble growth at the droplet base from the heat-transfer-controlled mode to the inertia-controlled mode. As opposed to the relatively slow vibration jumping in seconds, the vapor bubble growth in the inertia-controlled mode on tall-micropillared surface leads to droplet out-of-plane jumping in milliseconds. Such rapid droplet detachment stems from the swift Wenzel-to Cassie transition incurred by vapor bubble burst (explosion), during which the bubble expanding velocity can reach as fast as ~4 m/s. Vapor bubble growth in a droplet and bubble-burst-induced droplet jumping have been less explored. This study unveils the underpinning mechanisms of versatile jumping behaviors of boiling droplets from a hot micro-structured surface and opens up further possibilities for the design of engineered surfaces that mitigate potential damage of vapor explosion or alleviate condensate flooding

The J -integral fracture toughness of PP/CaCO 3 composites

The J -integral method was introduced to investigate the fracture process of PP/CaCO 3 composites. The results showed that the resistance of PP/CaCO 3 composites to crack initiation and propagation was greatly improved with the addition of CaCO 3 filler. Large scale plasticity was caused in PP/CaCO 3 composites, from which a large amount of energy was absorbed by the PP matrix. The reason for the increase in the fracture toughness of PP/CaCO 3 composites was attributed to the partial micro-drawing ahead of the crack tip in the PP matrix, which was formed by the stress concentration caused by the filler particles in the PP matrix and/or by the interfacial debonding between filler particles and the PP matrix. It was indicated that the presence of CaCO 3 filler could augment the ductility of composites locally, resulting in higher fracture energy in the crack initiation and propagation of the PP/CaCO 3 composites in a certain CaCO 3 content range.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44729/1/10853_2004_Article_BF00357345.pd

MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation

Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In this work, we make the first attempt to build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG, with more than 17K conversations collected from the online health consultation community. Five different categories of entities, including diseases, symptoms, attributes, tests, and medicines, are annotated in each conversation of MedDG as additional labels. To push forward the future research on building expert-sensitive medical dialogue system, we proposes two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. To acquire a clear comprehension on these two medical dialogue tasks, we implement several state-of-the-art benchmarks, as well as design two dialogue models with a further consideration on the predicted entities. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset, and the response quality can be enhanced with the help of auxiliary entity information. From human evaluation, the simple retrieval model outperforms several state-of-the-art generative models, indicating that there still remains a large room for improvement on generating medically meaningful responses.Comment: Data and code are available at https://github.com/lwgkzl/MedD

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning

Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions. Most existing researches on building ES conversation systems only considered single-turn interactions with users, which was over-simplified. In comparison, multi-turn ES conversation systems can provide ES more effectively, but face several new technical challenges, including: (1) how to adopt appropriate support strategies to achieve the long-term dialogue goal of comforting the user's emotion; (2) how to dynamically model the user's state. In this paper, we propose a novel system MultiESC to address these issues. For strategy planning, drawing inspiration from the A* search algorithm, we propose lookahead heuristics to estimate the future user feedback after using particular strategies, which helps to select strategies that can lead to the best long-term effects. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes. Extensive experiments show that MultiESC significantly outperforms competitive baselines in both dialogue generation and strategy planning. Our codes are available at https://github.com/lwgkzl/MultiESC.Comment: Accepted by the main conference of EMNLP 202

Enhanced interfacial wettability and mechanical properties of Ni@Al2O3/Cu ceramic matrix composites using spark plasma sintering of Ni coated Al2O3 powders

Poor wettability and weak interfacial bonding between Cu and Al2O3 have been critical issues for sintering of high-quality Ni@Al2O3/Cu composites. In this paper, we explore an interfacial engineering design methodology to achieve good mechanical properties of Ni@Al2O3/Cu composites using spark plasma sintering method. The Ni coated powders were prepared using a heterogeneous precipitation method, which can significantly improve wettability between Cu and Al2O3 and enhance their interfacial bonding. The sintered Ni@Al2O3/Cu composites with a copper content of 15 vol% showed a compact network structure of alumina well-infiltrated with metallic Cu, and achieved good mechanical (e.g., fracture toughness of 6.72 MPam1/2) and physical properties (e.g., relative density of 99.3% and electrical resistivity of 1.2810−3 Ω m). The key mechanisms for the enhanced properties of the composites synthesized using the Ni coated composite powders have been identified as: (1) well-formed ceramic/metal interfacial structures which improve wettability of Al2O3 with Cu, and promote the formation of a homogeneous network structure; (2) enhanced elemental diffusion and interfacial reactions, which result in formation of Cu2O and CuAlO2 and thus improve interfacial wetting and bonding properties

Enhanced functional properties of CeO2 modified graphene/epoxy nanocomposite coating through interface engineering

This paper reports significant enhancement of corrosion resistance and electrical properties of waterborne epoxy coatings through additions of ceria modified graphene. Results showed that ceria particles were uniformly distributed and covalently bonded onto the surface of graphene. A dense interface layer was formed between the ceria modified graphene and epoxy matrix by aliphatic ether bonds. The composite coating with a modified graphene content of 0.5 wt% exhibited the best corrosion resistance with the highest impedance modulus (e.g., 103 Ω cm2 for the damaged coating) and the lowest corrosion rate (e.g., 0.002 mm/year). The excellent corrosion resistance of the composite coating is related to the barrier effect of graphene and the inhibition effect of ceria on metal corrosion. Moreover, the coating showed a low percolation threshold of 0.231 vol% and its electrical conductivity reached 10−5 S/m when the content of modified graphene was 0.5 wt%

TRABAJOS DE MÉTODO ARQUEOLÓGICO [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201