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Taking advantage of the high redox activity and excellent structural stability properties of coordination polymers (CPs), we have successfully prepared a one-dimensional CP, {[Fe(pyzdc)]·2H2O}n (Fe-1D), by a hydrothermal method with pyrazine-2,3-dicarboxylic acid (H2pyzdc) as the ligand and iron as the metal center to improve the deficiencies of organic electrode materials in terms of specific capacity and cycling stability in lithium-ion batteries (LIBs). The one-dimensional chains are connected to each other by hydrogen bonds, and a three-dimensional supramolecular network is constructed, which not only enhances the stability of the material during the charge–discharge cycle, but also promotes the rapid transport of ions by virtue of its porous structure and ordered ion channels. Consequently, Fe-1D demonstrates outstanding cycling stability, achieving a remarkable reversible capacity of 833 mAh g−1 after 300 cycles at 200 mA g−1. Theoretical calculations further illustrate that after introducing the metal center into the ligand, Fe-1D can store 12 lithium ions, while significantly narrowing the energy gap, suggesting that Fe-1D has abundant lithium storage sites and good electrochemical activity. Both experimental and theoretical analyses underscored the pivotal role of redox-active metal–organic materials in designing high-performance anodes for rechargeable batteries. [ABSTRACT FROM AUTHOR]

With a growing focus on the environment, the power system is evolving into a cleaner and more efficient energy supply infrastructure. Photovoltaic (PV) and storage are key assets for the power industry's shift to sustainable energy. PV generation has zero carbon emission, and the integration of a substantial number of PV units is fundamentally important to decarbonize the power system. However, it also poses challenges in terms of voltage stability and uncertainty. Besides, the daily load and real-time price are also uncertain. As a prosumer, energy storage demonstrates the capacity to enhance accommodation and stability. The adoption of Virtual Power Plants (VPPs) emerges as a promising strategy to address these challenges, which allows the coordinated orchestration of PV systems and storage to participate power dispatch as a virtual unit. It further augments the flexibility of the power distribution system (PDS). To maximize the profit of VPP, a data-driven price forecasting method is proposed to extract useful information from historical datasets based on a novel LSTM-Transformercombined neural network. Then, an improved sparrow searching algorithm (SSA) is proposed to schedule VPPs by combining the secant line search strategy. The numerical results, obtained from testing the model on IEEE 13-node and 141node distribution systems, demonstrate the effectiveness and efficiency of the proposed model and methodology. [ABSTRACT FROM AUTHOR]

Development of new multi‐ratiometric fluorescence probes has been a popular research field. This approach provides richer and more accurate information for the recognition of different substances which have similar chemical and physical properties. In this work, a ratiometric fluorescence sensor UIO‐66⊃FL/RhB is successfully constructed by incorporating fluorescein (FL) and rhodamine B (RhB) to the UIO‐66. With the addition of fluoroquinolones (FQs), the prepared UIO‐66⊃FL/RhB sensor can be used for the 2D emission‐fingerprint map, the linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) in recognition the presence of each FQs. The fluorescence probe offers a broad linear range (0‐8 µM) and a low detection limit (3.7 nM) in sensing ofloxacin. In addition, a smartphone‐assisted portable sensing platform and a portable UIO‐66⊃FL/RhB hydrogel test material are constructed successfully for screening before fluorescence detection. This study proposes a novel multi‐ratiometric fluorescence probe, which can recognize different FQs. Besides, the UIO‐66⊃FL/RhB‐based hydrogel beads are construction for screening before the detection of FQs, which established a promising strategy for the simple and accurate discrimination of FQs. [ABSTRACT FROM AUTHOR]

ABSTRACT Air conditioning load is a crucial demand response resource for optimizing energy consumption control, and its accurate analysis provides an essential basis for achieving efficient energy management. We aim at solving the problems of scarcity, single type, low accuracy and difficult construction of high‐quality data sets available for air conditioning operation characteristic models at present. This paper proposes a construction method of air conditioning operation characteristic model based on an improved seagull optimization algorithm to optimize deep belief network (ISOA‐DBN). Firstly, the data set for the study of air conditioning operation characteristics is obtained through experiments. Secondly, the Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) are used to study the operating characteristics of air conditioning. The results show that the model effect is better when DBN is used to study the operating characteristics of air conditioning, and the coefficient of determination reaches 0.9439. Then, the SOA is improved, and its performance is tested. The results show that ISOA performs better than SOA in the test of 14 standard functions. Finally, the ISOA is used to adjust the DBN parameters finely. The results show that compared with DBN and SOA‐DBN, ISOA‐DBN has a better model effect when used to study the operating characteristics of air conditioners, and the coefficient of determination reaches 0.9534. This can provide strong support for studying air conditioning operating characteristics under different working conditions and has broad application prospects in optimizing energy consumption control. [ABSTRACT FROM AUTHOR]

[Display omitted] • The experimental system of heat load data acquisition for building air conditioning is constructed to obtain real and reliable data. • The Latin hypercube sampling is introduced to improve the initialization population of the sparrow search algorithm, which improves the performance of the algorithm. • A total of 12 models were implemented to forecast the heat load of building air conditioning, and ISSA-LSTM model shows the highest forecasting accuracy. • Deep learning and machine learning models were developed to forecast the heat load of building air conditioning and the differences between different models in forecasting the heat load of air conditioning have been identified. • The accuracy differences of heat load forecasting by optimizing LSTM model with different population algorithms have been studied. Building air conditioning heat load is a critical demand response resource, and its forecasting serves as a fundamental basis for optimizing building energy consumption control. The existing air conditioning load forecasting model suffers from reduced accuracy and stability due to the significant influence of temperature, humidity, and other factors. This paper proposes a method for building air conditioning heat load forecasting using Long Short-Term Memory neural network (LSTM) optimized by the Improved Sparrow Search Algorithm (ISSA). First, an experimental system for data collection on building air conditioning thermal load is constructed to form a dataset. Subsequently, the Latin Hypercube Sampling (LHS) is introduced to improve the SSA and iteratively optimize the hyperparameters of the LSTM model using ISSA. Finally, different optimization algorithms including Particle Swarm Optimizer (PSO), Crow Search Algorithm (CSA) and Spider Wasp Optimizer (SWO) were developed to optimize the LSTM model to achieve similar purposes, using the coefficient of determination as an indicator for evaluating model accuracy. The results show that the proposed data-driven new method is the most accurate model for forecasting building air conditioning heat load in this study. The R2 of the ISSA-LSTM forecasting model is as high as 0.9971, and compared with the RNN, LSTM, GRU, Bi-LSTM, SWO-LSTM, and SSA-LSTM models, the RMSE of this model in air conditioning heat load forecasting is reduced by 80.9670%, 71.7390%, 87.8040%, 88.3027%, 86.1179%, and 47.3089%, respectively. Employing the ISSA-LSTM method significantly enhances precision in building air conditioning load forecasting and holds promising applications in optimizing building energy consumption control. [ABSTRACT FROM AUTHOR]