Form Stable Phase-Change Materials

This work investigates the use of two different polyols, xylitol (Xyl) and erythritol (Ery), in conjunction with boron nitride (BN) aerogels, for the purpose of creating thermally conductive composites. While the BN filler in Xyl composites achieved a high anisotropic thermal conductivity of up to 4.53 W/m-K at 18.2 weight percent filler loading, they do not exhibit good phase-change material qualities due to a low solidification enthalpy even at low cooling rates. Alternatively, the BN-Ery composites have shown promising results with a solidification enthalpy of 225.14 J/g and a melting enthalpy of 385.84 J/g at a heat rate of 5 °C/min. These samples, however, have so far exhibited lower thermal conductivity values as high as 2.73 W/m-K at 15.5 weight percent filler loading. In these samples, the boron nitride scaffolds show good anisotropic heat transfer due to the use of ice-template method. However, since the polyol crystals radiate from the BN pillars, they cause the composite to have isotropic overall heat transfer

Information Ambiguity, Market Institutions and Asset Prices: Experimental Evidence

We explore how information ambiguity and traders’ attitudes toward such ambiguity affect expectations and asset prices under three different market institutions. Specifically, we test the prediction of Epstein & Schneider (2008) that information ambiguity will lead market prices to overreact to bad news and to underreact to good news. We find that such an asymmetric reaction exists and is strongest in individual prediction markets. It occurs to a lesser extent in single price call markets. It is weakest of all in double auction markets, where buyers’ asymmetric reaction to good/bad news is cancelled out by the opposite asymmetric reaction of sellers

Excellent performance of Pt-C/TiO2 for methanol oxidation:contribution of mesopores and partially coated carbon

Partial deposition of carbon onto mesoporous TiO2 (C/TiO2) were prepared as supporting substrate for Pt catalyst development. Carbon deposition is achieved by in-situ carbonization of furfuryl alcohol. The hybrid catalysts were characterized by XRD, Raman, SEM and TEM and exhibited outstanding catalytic activity and stability in methanol oxidation reaction. The heterogeneous carbon coated on mesoporous TiO2 fibers provided excellent electrical conductivity and strong interfacial interaction between TiO2 support and Pt metal nanoparticles. Methanol oxidation reaction results showed that the activity of Pt-C/TiO2 is 3.0 and 1.5 times higher than that of Pt-TiO2 and Pt-C, respectively. In addition, the Pt-C/TiO2 exhibited a 6.7 times enhanced stability compared with Pt-C after 2000 cycles. The synergistic effect of C/TiO2 is responsible for the enhanced activity of Pt-C/TiO2, and its excellent durability could be ascribed to the strong interfacial interaction between Pt nanoparticles and C/TiO2 support

Reinforcement of Cu Nanoink Film with Extended Carbon Nanofibers for Large Deformation of Printed Electronics

poster abstractMetallic nanoparticle inks (nanoinks) have attracted great interest in the manufacturing of printed flexible electronics. However, micro-cracks and pores generated during the sintering process deteriorate mechanical and electrical characteristics of the sintered nanoink film. To alleviate these problems, we demonstrated the use of very long carbon nanofiber (CNF, average length 200 μm) to reinforce the sintered nanoink films. In this study, different weight fractions of CNFs are dispersed into the Cu nanoink to improve the mechanical bending characteristics. Scanning electron micrographs (SEM) shows improved dispersion of oxidized CNF in the nanoink compared to the as-received CNF. The composite nanoinks are stencil printed on polyethylene terephthalate (PET) film and sintered by intense pulsed light system using Xe-flash. The electrical measurements show 90 %, 65 %, and 66 % improved electrical conductivity in the composite nanoink film (0.7 % of oxidized CNF) compared to the pure Cu nanoink under the 75 mm, 50 mm, and 25 mm of bending radii, respectively

Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework

Self-supervised learning has shown its great potential to extract powerful visual representations without human annotations. Various works are proposed to deal with self-supervised learning from different perspectives: (1) contrastive learning methods (e.g., MoCo, SimCLR) utilize both positive and negative samples to guide the training direction; (2) asymmetric network methods (e.g., BYOL, SimSiam) get rid of negative samples via the introduction of a predictor network and the stop-gradient operation; (3) feature decorrelation methods (e.g., Barlow Twins, VICReg) instead aim to reduce the redundancy between feature dimensions. These methods appear to be quite different in the designed loss functions from various motivations. The final accuracy numbers also vary, where different networks and tricks are utilized in different works. In this work, we demonstrate that these methods can be unified into the same form. Instead of comparing their loss functions, we derive a unified formula through gradient analysis. Furthermore, we conduct fair and detailed experiments to compare their performances. It turns out that there is little gap between these methods, and the use of momentum encoder is the key factor to boost performance. From this unified framework, we propose UniGrad, a simple but effective gradient form for self-supervised learning. It does not require a memory bank or a predictor network, but can still achieve state-of-the-art performance and easily adopt other training strategies. Extensive experiments on linear evaluation and many downstream tasks also show its effectiveness. Code is released at https://github.com/fundamentalvision/UniGrad.Comment: CVPR202

Federated Learning with New Knowledge: Fundamentals, Advances, and Futures

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: Federated Learning with New Knowledge. The primary challenge here is to effectively incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, extend their lifespan, and facilitate sustainable development. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss how to incorporate new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Furthermore, we comprehensively discuss the potential future directions for FL with new knowledge, considering a variety of factors such as scenario setups, efficiency, and security. There is also a continuously updating repository for this topic: https://github.com/conditionWang/FLNK.Comment: 10 page