Investigation of fabrication of gas diffusion substrate for proton exchange membrane fuel cells

The gas diffusion substrate (GDS) is essential in the proton exchange membrane fuel cells. Its fabrication techniques affect the performance significantly and are worthy of investigation. In this study, a manufacturing process of the GDS is proposed to understand the formation process of GDS and promote its structure and performance more pertinently. Different states during the preparation process, raw carbon paper, pre-curing, curing, carbonation, and graphitization, are characterized and measured. Experimental and numerical methods are employed to determine the relationships between microstructure, transport, and mechanical performance variation with the fabricating processes. The results show that its porosity, average pore size, and effective diffusivity decrease first and increase after curing. These parameters after graphitization are lower than that of the carbon paper (CP). The electrical resistivity increases dramatically while pre-curing and decreases gradually after curing, carbonation, and graphitization, and it is much reduced after graphitization. Moreover, mechanical measurement results show that both the picks of tensile strength and flexural modulus occur after curing. Its tensile strength shows little change after graphitization compared to the initial paper's. In contrast, the flexural modulus is improved significantly.     

One-step to prepare high-performance gas diffusion layer (GDL) with three different functional layers for proton exchange membrane fuel cells (PEMFCs)

Gas diffusion layer (GDL) is one of the most important components of fuel cells. In order to improve the fuel cell performance, GDL has developed from single layer to dual layers, and then to multiple layers. However, dual or multi layers in GDL are usually prepared by layer-by-layer methods, which cost too much time, energy, and resources. In this work, we successfully developed a facile one-step method to prepare a GDL with three functional layers by utilizing the different sedimentation rates and filtration rates of short carbon fiber (CF) and carbon nanotube (CNT). The treatment temperature for this GDL is much lower than that of traditional method. The thickness of the GDL can be effectively controlled from as thin as 50 μm to more than 200 μm by simply adjusting the content of CF. The GDL with high flexibility is suitable to develop high performance flexible electronics. The fuel cell with the GDL has the maximum power density 1021 mW cm^?2, which shows 19% improvement comparing to the conventional one. Therefore, this work breaks the traditional concept that GDL for fuel cells only can be prepared by very complex and high-cost procedure.     

Probabilistic learning on manifolds (PLoM) with partition

The probabilistic learning on manifolds (PLoM) introduced in 2016 has solved difficult supervised problems for the “small data” limit where the number N of points in the training set is small. Many extensions have since been proposed, making it possible to deal with increasingly complex cases. However, the performance limit has been observed and explained for applications for which N is very small and for which the dimension of the diffusion-map basis is close to N. For these cases, we propose a novel extension based on the introduction of a partition in independent random vectors. We take advantage of this development to present improvements of the PLoM such as a simplified algorithm for constructing the diffusion-map basis and a new mathematical result for quantifying the concentration of the probability measure in terms of a probability upper bound. The analysis of the efficiency of this extension is presented through two applications.     

Profiling hot isostatically pressed canister–wasteform interaction for Pu-bearing zirconolite-rich wasteforms

Zirconolite-rich full ceramic wasteforms designed to immobilize Pu-bearing wastes were produced via hot isostatic pressing (HIP) using stainless steel (SS) and nickel (Ni) HIP canisters. A detailed profiling of the elemental compositions of the major and minor phases over the canister–wasteform interaction zone was performed using scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (SEM-EDS) characterization. Bulk sample analyses from regions near the center of the HIP canister were also conducted for both samples using X-ray diffraction and SEM-EDS. The sample with the Ni HIP canister showed almost no interaction zone with only minor diffusion of Ni from the inner wall of the canister into the near-surface region of the wasteform. The sample with the SS HIP canister showed ∼100–120 μm of interaction zone dominated by high-temperature Cr diffusion from canister materials to the wasteform with the Cr predominantly incorporated into the durable zirconolite phase. We also examined, for the first time, changes to the HIP canister wall thickness caused by HIPing and demonstrated that no canister wall thinning occurred. Instead, in the areas examined, the canister wall thickness was observed to increase (up to ∼20%) due to the compression occurring during the HIP cycle. Further, only sparse formation of (Cr, Mn)-rich oxide particles were noted within the HIP canister inner wall area immediately adjacent to the ceramic material, with no evidence for reverse diffusion of ceramic materials. Though the HIP canister–wasteform interaction extends to ∼120 μm when using an SS HIP canister for the system investigated, this translates to <<1 vol.% for an industrial scale HIPed wasteform. Importantly, the HIP canister–wasteform interactions did not produce any obviously less durable phases in the wasteform or had any detrimental impact on the HIP canister properties.     

A novel foam glass obtained from solid waste: A sustainable strategy for application in the degradation of an environmental pollutant

Foams glass were obtained from solid waste of flat glass and exhausted alkaline batteries. The physical, chemical, crystalline and morphological properties of the samples were obtained using the Archimedes principle, X-ray diffractometry (XRD), scanning electron microscopy (SEM). The results revealed glass foams with apparent porosities in the range of 55–64% and apparent densities in the range of 0.40–0.79 g cm^?3. The manganese oxide and graphite contained within the cathode of alkaline batteries acted as both oxidizing agents and as foaming agents. The zinc contained in the anode acted as a pore stabilizing agent and the zinc oxide as a semiconductor material. The foam glass that was composed of flat glass with an anode of Zn and ZnO, and a cathode of Mn₂O₃ and Mn₃O₄ (named An8), showed the greatest potential for heterogeneous photocatalysis, with a maximum efficiency of 95.9% after 3 h of treatment of solution containing dye. These results suggest the feasibility of producing foam glass from waste, as well as its potential application in photocatalytic systems, such as in the low-cost treatment of water.     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

快速近似计算Shapley值的归因解释方法

Shapley值归因解释方法虽然能更准确量化解释结果, 但过高的计算复杂度严重影响了该方法的实用性. 本文引入KD树重新整理待解释模型的预测数据, 通过在KD树上插入虚节点, 使之满足TreeSHAP算法的使用条件, 在此基础上提出了KDSHAP方法. 该方法解除了TreeSHAP算法仅能解释树结构模型的限制, 将该算法计算Shapley值的高效性放宽到对所有的黑盒模型的解释中, 同时保证了计算准确度. 通过实验对比分析, KDSHAP方法的可靠性, 以及在解释高维输入模型时的适用性.     

Automated prediction system of household energy consumption in cities using web crawler and optimized artificial intelligence

The supply of electrical energy is critical to convenient and comfortable living. However, people consume a large amount of energy, contributing to an energy crisis and global warming, and damaging some ecological cycles. Residential electricity consumption has greater elasticity than industrial and business consumption; it therefore has high energy-saving potential. This work establishes an automated platform, which provides information about residential electricity consumption in each city in Taiwan. Machine learning was used to forecast future residential electricity demand. A nature-inspired optimization method was applied to enhance the accuracy of the best machine learner, yielding an even better hybrid ensemble model. Performance measures indicate that the resulting model is accurate and provides effective information for reference. An automatic web-based system based on the model was combined with a web crawler and scheduled to run automatically to provide information on monthly residential electricity consumption in each county and city. By providing energy consumption information across the country, power providers and government can discuss policy and set different goals for energy use. The results of this study can facilitate the early implementation of energy-saving and carbon emission-reducing in cities and aid utility companies in establishing energy conservation guidelines.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

面向小语种机器翻译的平行语料库构建方法

神经机器翻译模型的训练效果在很大程度上取决于平行语料库的规模和质量。除了一些常见语言外,汉语与小语种间高质量平行语料库的建设一直处于滞后状态。现有小语种平行语料库多采用自动句子对齐技术利用网络资源构建而成,在文本质量和领域等方面有诸多局限性。采用人工翻译的方式可以构建高质量平行语料库,但是缺乏相关经验和方法。文中从机器翻译实践者和研究者角度出发,介绍了经济高效的人工构建小语种平行语料库的工作,包括其总体目标、实施过程、流程细节和最后结果。在构建过程中尝试并积累了各种经验,形成了小语种到汉语平行语料库构建方法、建议的总结。最终,成功构建了波斯语到汉语、印地语到汉语、印度尼西亚语到汉语各50万条高质量平行语料。实验结果表明,所构建的平行语料库有较好的质量,提高了小语种神经机器翻译模型的训练效果。