Hierarchical Ni-BDC coated FeOOH nanosheets: A coordination tuning synergistic electrocatalyst with enhanced activity for water oxidation

Hierarchical composites represent a class of efficient electrocatalysts for renewable energy storage and conversion technologies owing to the porous structure and additional exposure of metal sites. Herein, a Ni-based metal organic frameworks (MOFs) (marked as Ni-BDC, BDC stands for 1,4-benzenedicarboxylic acid) nanosheet is successfully fabricated on hydroxyl iron oxide (FeOOH) array with carbon fiber cloth (CFC) as substrate. Benefit from the coordination tuning synergistic effect of the distinct chemical composition and the hierarchical structure for fast mass transportation, the as-obtained FeOOH@Ni-BDC illustrates excellent catalytic ability for electrochemical water oxidation with low overpotential of 270 mV to reach 10 mA/cm² current and good durability in alkaline electrolyte. The novelty of this work lies in the modulation of electronic structure of the FeOOH with Ni-BDC through coordination effect to enhance the activity of the hierarchical composite electrocatalyst. This work is expected to guide the preparation of efficient electrocatalyst for new type alternative energy sources exploitation in near future.     

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.     

Interaction of graphene,MnOx, and Ca2+ for enhanced biomimetic, ‘bubble-free’ oxygen evolution reaction at mild pH

Water electrolysis powered by renewable electricity will likely be critical to a future hydrogen economy. However, the typical use of strongly acidic or alkaline electrolytes necessitates the use of expensive materials, while bubbles add to capital and operational costs, due to blocking of the electrode surface and the necessary use of pumps and gas-liquid separators. Here ‘bubble-free’ oxygen evolution at mild pH is carried out using an electrocatalyst that mimics photosystem II (PSII). The bubble-free electrode includes a gas-extracting Gore-Tex® membrane. Edge-functionalised graphene (EFG) is included to mimic the metal-binding local protein environment, and the tyrosine residue, in the oxygen evolving complex (OEC) of PSII, while MnO_x and Ca²⁺ are incorporated to mimic the Mn₄CaO₅ cluster. Interaction between EFG, MnO_x, and Ca²⁺ results in a significant, 130 mV fall in the overpotential required to drive electrocatalytic oxygen evolution at 10 mA cm⁻², compared to the electrode without these biomimetic components.     

Enhanced graphitic domains of unreduced graphene oxide and the interplay of hydration behaviour and catalytic activity

Previous studies indicate that the properties of graphene oxide (GO) can be significantly improved by enhancing its graphitic domain size through thermal diffusion and clustering of functional groups. Remarkably, this transition takes place below the decomposition temperature of the functional groups and thus allows fine tuning of graphitic domains without compromising with the functionality of GO. By studying the transformation of GO under mild thermal treatment, we directly observe this size enhancement of graphitic domains from originally ≤40 nm² to >200 nm² through an extensive transmission electron microscopy (TEM) study. Additionally, we confirm the integrity of the functional groups during this process by a comprehensive chemical analysis. A closer look into the process confirms the theoretical predicted relevance for the room temperature stability of GO and the development of the composition of functional groups is explained with reaction pathways from theoretical calculations. We further investigate the influence of enlarged graphitic domains on the hydration behaviour of GO and the catalytic performance of single atom catalysts supported by GO. Additionally, we show that the sheet resistance of GO is reduced by several orders of magnitude during the mild thermal annealing process.     

Ions motion algorithm for solving optimization problems

This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.     

基于SVM的煤与瓦斯突出预测模型及应用

为有效预测矿井内煤与瓦斯突出的危险程度,对其影响因素做了分析与探讨,分别构建了基于粒子群优化算法以及遗传算法支持向量机的煤与瓦斯突出预测模型,并且通过实例对两种模型预测的准确性进行了验证。分别利用单项以及综合指标、BP神经网络以及PSO-SVM模型、GA-SVM模型,对寺河煤矿二号井的突出区域进行预测比较。结果表明,PSO-SVM的预测模型不仅可以在小样本数据中预测出煤与瓦斯突出程度的大小,而且综合预测结果更加精确,其在解决矿井内煤与瓦斯突出的小样本数据中显示出更加强大、通用的性能。     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.