Comb-shaped sulfonated poly(aryl ether sulfone) proton exchange membrane for fuel cell applications

Structure design is the primary strategy to acquire suitable ionomers for preparing proton exchange membranes (PEMs) with excellent performance. A series of comb-shaped sulfonated fluorinated poly(aryl ether sulfone) (SPFAES) membranes are prepared from sulfonated fluorinated poly(aryl ether sulfone) polymer (SPFAE) and sulfonated poly(aryl ether sulfone) oligomer (SPAES-Oligomer). Chemical structures of the comb-shaped membranes are verified by ¹H nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectra. The comb-shaped SPFAES membranes display more continuous hydrophilic domains for ion transfer, because the abundant cations and flexible side-chains structure possess higher mobility and hydrophilicity, which show significantly improved proton conductivity, physicochemical stability, mechanical property compared to the linear SPFAE membranes. In a H₂/O₂ single-cell test, the SPFAES-1.77 membrane achieves a higher power density of 699.3 mW/cm² in comparison with Nafion® 112 (618.0 mW/cm²) at 80 °C and 100% relative humidity. This work offers a promising example for the synthesis of highly branched polymers with flexible comb-shaped side chains for high-performance PEMs.     

Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

Synthesis and characterization of a long side-chain double-cation crosslinked anion-exchange membrane based on poly(styrene-b-(ethylene-co-butylene)-b-styrene)

By choosing a triple block polymer, poly(styrene-b-(ethylene-co-butylene)-b-styrene) (SEBS), as the backbone and adopting a long side-chain double-cation crosslinking strategy, a series of SEBS-based anion-exchange membranes (AEMs) was successively synthesized by chloromethylation, quaternization, crosslinking, solution casting, and alkalization. The 70C16-SEBS-TMHDA membrane showed high OH⁻ conductivity (72.13 mS/cm at 80 °C) and excellent alkali stability (only 10.86% degradation in OH⁻ conductivity after soaking in 4-M NaOH for 1700 h at 80 °C). Furthermore, the SR was only 9.3% at 80 °C and the peak power density of the H₂/O₂ single cell was up to 189 mW/cm² at a current density of 350 mA/cm² at 80 °C. By introducing long flexible side chains into a polymer SEBS backbone, the structure of the hydrophilic–hydrophobic microphase separation in the membrane was constructed to improve the ionic conductivity. Additionally, network crosslinked structure improved dimensional stability and mechanical properties.     

减水剂类型对赤泥—矿渣基地聚物注浆材料性能的影响及机理研究

针对赤泥等固体废弃物对环境危害性大且利用率低等问题,以碱激发赤泥-矿渣基地聚物注浆材料为 研究对象,研究了不同掺量的聚羧酸(PA)减水剂、醛酮缩合物(AKC)减水剂和萘系(N)减水剂对材料凝结时间、流动 性及强度等的影响,并通过 XRD、傅里叶红外光谱及 SEM 等设备对减水剂的作用机理进行研究。 结果表明:减水剂增 强了材料的流动性但降低了材料的剪切应力;N 和 PA 减水剂能缩短材料的凝结时间,但 AKC 减水剂会延长材料的凝 结时间;N 和 AKC 减水剂能提高材料的强度,但 PA 减水剂会降低材料的强度;N 减水剂对材料的综合性能提升效果 更加明显,其最优掺量为 0. 7%;减水剂对赤泥-矿渣基地聚物性能提升的作用机理主要是促进地聚合物凝胶的形成。 研究成果为拓展赤泥在工程上的使用途径和效率提供了理论指导。     

Spatiotemporal Magnetocaloric Microenvironment for Guiding the Fate of Biodegradable Polymer Implants

The degradation behavior of implants is significantly important for bone repair. However, it is still unprocurable to spatiotemporally regulate the degradation of the implants to match bone ingrowth. In this paper, a magneto-controlled biodegradation model is established to explore the degradation behavior of magnetic scaffolds in a magnetothermal microenvironment generated by an alternating magnetic field (AMF). The results demonstrate that the scaffolds can be heated by magnetic nanoparticles (NPs) under AMF, which dramatically accelerated scaffold degradation. Especially, magnetic NPs modified by oleic acid with a better interface compatibility exhibit a greater heating efficiency to further facilitate the degradation. Furthermore, the molecular dynamics simulations reveal that the enhanced motion correlation between magnetic NPs and polymer matrix can accelerate the energy transfer. As a proof-of-concept, the feasibility of magneto-controlled degradation for implants is demonstrated, and an optimizing strategy for better heating efficiency of nanomaterials is provided, which may have great instructive significance for clinical medicine.     

Mechanical Properties of Single-Layer Diamond Reinforced Poly(vinyl alcohol) Nanocomposites through Atomistic Simulation

Low-dimensional carbon nanostructures are ideal nanofillers to reinforce the mechanical performance of polymer nanocomposites due to their excellent mechanical properties. Through molecular dynamics simulations, the mechanical performance of poly(vinyl alchohol) (PVA) nanocomposites reinforced with a single-layer diamond – diamane is investigated. It is found the PVA/diamane exhibits similar interfacial strengths and pull-out characteristics with the PVA/bilayer-graphene counterpart. Specifically, when the nanofiller is fully embedded in the nanocomposite, it is unable to deform simultaneously with the PVA matrix due to the weak interfacial load transfer efficiency, thus the enhancement effect is not significant. In comparison, diamane can effectively promote the tensile properties of the nanocomposite when it has a laminated structure as it deforms simultaneously with the matrix. With this configuration, the interlayer sp³ bonds endows diamane with a much higher resistance under compression and shear tests, thus the nanocomposite can reach very high compressive and shear stress. Overall, enhancement on the mechanical interlocking at the interface as triggered by surface functionalization is only effective for the fully embedded nanofiller. This work provides a fundamental understanding of the mechanical properties of PVA nanocomposites reinforced by diamane, which can shed lights on the design and preparation of next generation high-performance nanocomposites.     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

基于三维胞元空间的MA双向并行路由算法

针对传统移动代理(MA)在监测无线传感器网络(WSNs)的感兴趣信息时产生的延迟较大和能耗较多问题,提出了基于三维胞元空间的MA双向并行(3D-BPMA)路由算法.3D-BPMA将MA与传统的客户/服务器(c/S)模式相结合,在胞元内利用C/S模式搜集信息,在单层胞元系统和路由器与路由器之间采用MA双向并行的策略进行传输.仿真结果表明:3D-BPMA与LCF,DSG-MIP算法相比减少了平均响应时间和网络平均能耗,提高了MA发送率.