Flexible Transparent Bifunctional Capacitive Sensors with Superior Areal Capacitance and Sensing Capability based on PEDOT:PSS/MXene/Ag Grid Hybrid Electrodes

Flexible transparent supercapacitors (FTSs) have aroused considerable attention. Nonetheless, balancing energy storage capability and transparency remains challenging. Herein, a new type of FTSs with both excellent energy storage and superior transparency is developed based on PEDOT:PSS/MXene/Ag grid ternary hybrid electrodes. The hybrid electrodes can synergistically utilize the high optoelectronic properties of Ag grids, the excellent capacitive performance of MXenes, and the superior chemical stability of PEDOT:PSS, thus, simultaneously demonstrating excellent optoelectronic properties (T: ≈89%, R_s: ≈39 Ω sq⁻¹), high areal specific capacitance, superior mechanical softness, and excellent anti-oxidation capability. Due to the excellent comprehensive performances of the hybrid electrodes, the resulting FTSs exhibit both high optical transparency (≈71% and ≈60%) and large areal specific capacitance (≈3.7 and ≈12 mF cm⁻²) besides superior energy storage capacity (P: 200.93, E: 0.24 µWh cm⁻²). Notably, the FTSs show not only excellent energy storage but also exceptional sensing capability, viable for human activity recognition. This is the first time to achieve FTSs that combine high transparency, excellent energy storage and good sensing all-in-one, which make them stand out from conventional flexible supercapacitors and promising for next-generation smart flexible energy storage devices.     

Frontiers in Circularly Polarized Phosphorescent Materials

Circularly polarized luminescence (CPL) materials have received increasing attention in recent years. Amongst various CPL materials, circularly polarized phosphorescence (CPP) materials featuring long life-time represent a novel research frontier and exhibit promising applications in various fields. Herein, the state-of-the-art advances of CPP materials are systematically summarized, as classified into transition metal complexes, organic small molecules, polymers, and organic/inorganic hybrid materials. Besides, the recent applications of CPP materials in organic light-emitting diodes and encryption display are also summarized. Furthermore, the current challenges and future perspectives are put forward. It is expected that this review will offer more inspirations for the future rational design of advanced CPP materials, thus further promoting their future practical applications.     

Regulating the Water Molecular in the Solvation Structure for Stable Zinc Metal Batteries

Rechargeable aqueous zinc batteries are promising energy storage devices because of their low cost, high safety, and high energy density. However, their performance is plagued by the unsatisfied cyclability due to the dendrite growth and hydrogen evolution reaction (HER) at the Zn anode. Herein, it is demonstrated that the concentrated hybrid aqueous/non-aqueous ZnCl₂ electrolytes constitute a peculiar chemical environment for not only the Zn-ions but also water molecules. The high concentration of chloride ions substitutes the H₂O molecular in the solvation structure of Zn²⁺, while the acetonitrile further interacts with H₂O to decrease its activity. The hybrid electrolytes both inhibit the dendrite formation and HER, enabling an ultrahigh average Coulombic efficiency of 99.9% in the Zn||Cu half-cell and a highly reversible Zn plating/stripping with a low overpotential of 21 mV. Using this hybrid electrolyte, the Zn||polytriphenylamine (PTPAn) full cell deliveres a high discharge capacity of 110 mAh g⁻¹, a high power density of 9200 W kg⁻¹ at 100 °C and maintains 85% of the capacity for over 6000 cycles at 10 °C. This study provides a deep understanding between the solvation structure and columbic efficiency of Zn anode, thus inspiring the development for stable Zn batteries.     

Surface Oxygen Coordination of Hydrogen Bond Chemistry for Aqueous Ammonium Ion Hybrid Supercapacitor

Aqueous ammonium ion hybrid supercapacitor (A-HSC) combines the charge storage mechanisms of surface adsorption and bulk intercalation, making it a low-cost, safe, and sustainable energy storage candidate. However, its development is hindered by the low capacity and unclear charge storage fundamentals. Here, the strategy of phosphate ion-assisted surface functionalization is used to increase the ammonium ion storage capacity of an α-MoO₃ electrode. Moreover, the understanding of charge storage mechanisms via structural characterization, electrochemical analysis, and theoretical calculation is advanced. It is shown that NH₄⁺ intercalation into layered α-MoO₃ is not dominant in the A-HSC system; rather, the charge storage mainly depends on the adsorption energy of surface “O” to NH₄⁺. It is further revealed that the hydrogen bond chemistry of the coordination between “O” of surface phosphate ion and NH₄⁺ is the reason for the capacity increase of MoO₃. This study not only advances the basic understanding of rechargeable aqueous A-HSC but also demonstrates the promising future of surface engineering strategies for energy storage devices.     

A Hybrid Strategy-Based Ultra-Narrow Stretchable Microelectrodes with Cell-Level Resolution

Stretchable ultra-narrow (e.g., 10 µm in width) microelectrodes are crucial for the electrophysiological monitoring of single cells providing the fundamental understanding to the working mechanism of neuro network or other electrically functional cells. Current fabrication strategies either focus on the preparation of normal stretchable electrodes with hundreds of micrometers or millimeters in width by using inorganic conductive materials or develop conductive organic polymer gel for ultra-narrow electrodes which suffer from low stretchability and instability for long-term implantation, therefore, it is still highly desirable to explore bio-interfacial ultra-narrow stretchable inorganic electrodes. Herein, a hybrid strategy is reported to prepare ultra-narrow multi-channel stretchable microelectrodes without using photolithography or laser-assisting etching. A 10 µm × 10 µm monitoring window is fabricated with enhanced interfacial impedance by the special rough surface. The stretchability achieves to 120% for this 10 µm-width stretchable electrode. Supported by these superior properties, it is demonstrated that the stretchable microelectrodes can detect electrophysiological signals of single cells in vitro and collect electrophysiological signals more precisely in vivo. The reported strategy will open up the accessible preparation of the fine-size stretchable microelectrode. It will significantly improve the resolution of monitoring and stimulation of inorganic stretchable electrodes.     

Maximum Power Extraction Control Algorithm for Hybrid Renewable Energy System

In this research, a modified fractional order proportional integral derivate (FOPID) control method is proposed for the photovoltaic (PV) and thermoelectric generator (TEG) combined hybrid renewable energy system. The faster tracking and steady-state output are aimed at the suggested maximum power point tracking (MPPT) control technique. The derivative order number (µ) value in the improved FOPID (also known as PI^λD^µ) control structure will be dynamically updated utilizing the value of change in PV array voltage output. During the transient, the value of µ is changeable; it’s one at the start and after reaching the maximum power point (MPP), allowing for strong tracking characteristics. TEG will use the freely available waste thermal energy created surrounding the PV array for additional power generation, increasing the system’s energy conversion efficiency. A high-gain DC-DC converter circuit is included in the system to maintain a high amplitude DC input voltage to the inverter circuit. The proposed approach’s performance was investigated using an extensive MATLAB software simulation and validated by comparing findings with the perturbation and observation (P&O) type MPPT control method. The study results demonstrate that the FOPID controller-based MPPT control outperforms the P&O method in harvesting the maximum power achievable from the PV-TEG hybrid source. There is also a better control action and a faster response.     

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure, designers have addressed scaling concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain. In tactical Mobile Ad-hoc Network (MANET), hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out, self-mending and self-administration. Clustering in the routing process is one of the key aspects to increase MANET performance by coordinating the pathways using multiple criteria and analytics. We present a Group Adaptive Hybrid Routing Algorithm (GAHRA) for gathering portability, which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations. Based on this aspect, the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach, with the objectives of enhancing the output of MANET routing computation in each hub. Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.     

Horizontal Voting Ensemble Based Predictive Modeling System for ColonCancer

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.     

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’ experience. Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions. This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce. This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system. The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class. This will mitigate the feature vector's dimensionality by eliminating redundant, irrelevant, or noisy data. This work presents a new hybrid recommender system based on optimized feature selection and systolic tree. The features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), feature selection with the utilization of River Formation Dynamics (RFD), and the Particle Swarm Optimization (PSO) algorithm. The systolic tree is used for pattern mining, and based on this, the recommendations are given. The proposed methods were evaluated using the MovieLens dataset, and the experimental outcomes confirmed the efficiency of the techniques. It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborative filtering, the precision of 0.89 was achieved.     

融合指标分组的高维混合多目标进化优化

高维混合多目标优化问题因包含多个不同类型指标,目前尚缺乏有效求解该问题的进化优化方法。提出一种基于目标分组的高维混合多目标并行进化优化方法。采用深度学习神经网络预测种群隐式性能指标;基于指标相关性,将高维混合多目标优化问题分解为若干子优化问题;采用多种群并行进化算法,求解分解后的每一子优化问题,并基于各子种群的非被占优解构建外部保存集;采用聚合函数对外部保存集个体进一步优化,得到Pareto最优解集。在室内布局优化问题中验证所提方法,实验结果表明,所提方法的Pareto最优解在收敛性、分布性以及延展性等方面均优于对比方法。