Recent progress of electrocatalysts for hydrogen proton exchange membrane fuel cells

Due to stringent environmental regulations and the limited resources of fossil-based fuels, there is an urgent demand for clean and eco-friendly energy conversion devices. These criteria appear to be met by hydrogen proton exchange membrane fuel cells (PEMFCs). PEMFCs have attracted tremendous attention on account of their excellent performance with tunable operability and good portability. Nonetheless, their practical applications are hugely influenced by the scarcity and high cost of platinum (Pt) used as electrocatalysts at both cathode and anode. Pt is also susceptible to easy catalyst poisoning. Herein, this paper reviews the progress of the research regarding the development of electrocatalysts practically used in hydrogen PEMFCs, where the corner-stone reactions are cathodic oxygen reduction reaction (ORR) and anodic hydrogen oxidation reaction (HOR). To reduce the costs of PEMFCs, lessening or eliminating the use of Pt is of prime importance. For current and forthcoming laboratory/large-scale PEMFCs, there is much interest in developing substitute catalysts based on cheaper materials. As such are non-platinum (non-Pt), non-platinum group metals (non-PGMs), metal oxides, and non-metal electrocatalysts. Hence, high-performance, state-of-the-art, and novel structured electrocatalysts as replacements for Pt are needed.     

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.     

Ion migration behavior and its interaction with sintering environment in cation conductor during flash sintering

Flash sintering has been reported in various ceramics. Nevertheless, anion and cation conductors exhibit different flash-sintering behaviors, and the interaction mechanism between the conductive species and the sintering environment has remained unclear. Herein, we report the flash-sintering phenomena of a typical cation conductor, Na₃Zr₂(SiO₄)₂(PO₄) with anode region surrounded by air and NaNO₃ environments. The results prove that the ionic behavior and joule heating distribution can be controlled by changing the electrode environment. Four possible scenarios describing the ion migration behavior and interaction with the environment are proposed for providing a guidance for controlling the ion interaction behavior during flash sintering.     

电信运营商云网规划数字化研究和实现

基于电信运营商数字化转型，系统性地提出了数据驱动的云网发展规划体系，以及六大关键数字化能力构建，设计和实现了一种云网规划数字化平台，该平台可用于实现目标网络精细规划、边缘计算精准预测等场景，并探讨了数字孪生在规划领域的应用前景，对运营商推进云网融合战略、推进高质量发展具有指导和参考意义。     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Analysis and proposition of limiting current density measurement protocol

Limiting current density at different temperatures, backpressures, and balance gases can be used to separate molecular diffusion resistance, Knudsen diffusion resistance and local transport resistance of membrane electrode assembly (MEA). However, the measurement of limiting current density has no unified protocol. The diverse choices in the literature, either in the control of current or voltage or in the atmosphere like relative humidity and O₂ concentrations, make it difficult to compare the results and identify the true bottleneck hindering the mass transport. In this work, the current-voltage curves obtained by current scanning/stepping and voltage scanning/stepping methods under dilute O₂ of different concentrations and a wide range of relative humidity were measured and analyzed systematically. It is found that the voltage stepping method is superior to the other three ways of control for the reliable determination of the limiting current density. Aided with simultaneous electrochemical impedance spectroscopy measurement, the limiting current density can be determined with pinpoint accuracy. When the limiting current density is just used to qualitatively evaluate different MEA, the voltage scanning method can be used instead for its high time efficiency. The selection of the atmosphere also plays an important role in suppressing the distortion from excessive water and reducing the spurious contribution from proton conduction resistance. It is found that O₂ concentrations at 0.5 vol% and relative humidity at 90% can give the best estimation of O₂ transport resistance in membrane electrode assembly.     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

Benchmark experiment of large-angle scattering reaction cross section of iron at 14 MeV using two shadow bars – Comparison of experimental results with ENDF/B-VIII –

ABSTRACT

It is important to perform neutron transport simulations with accurate nuclear data in the neutronics design of a fusion reactor. However, absolute values of large-angle scattering cross sections vary among nuclear data libraries even for well-examined nuclide of iron. Benchmark experiments focusing on large-angle scattering cross sections were thus performed to confirm the correctness of nuclear data libraries. The series benchmark experiments were performed at a DT neutron source facility, OKTAVIAN of Osaka University, Japan, by the unique experimental system established by the authors’ group, which can extract only the contribution of large-angle scattering reactions. This system consists of two shadow bars, target plate (iron), and neutron detector (niobium). Two types of shadow bars were used and four irradiations were conducted for one experiment, so that contribution of room-return neutrons was effectively removed and only large-angle scattering neutrons were extracted from the measured four Nb reaction rates. The obtained experimental results were compared with calculations for five nuclear data libraries including JENDL-4.0, JEFF.-3.3, FENDL-3.1, ENDF/B- VII, and recently released ENDF/B-VIII. It was found from the comparison that ENDF/B-VIII showed the best result, though ENDF/B-VII showed overestimation and others are in large underestimation at 14 MeV.     

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.