Solvents incubatedπ-πstacking in hole transport layer for perovskite-silicon 2-terminal tandem solar cells with 27.21%efficiency

Room temperature sputtered inorganic nickel oxide(NiO_(x))is one of the most promising hole transport layers(HTL)for perovskite-sillion 2-terminal tandem solar cells with the aid of ultrathin and compact organic layers to passivate the surface defects.In this study,the aromatic solvent with different substituent groups was used to regulate the conformation of poly[bis(4-phenyl)(2,4,6-trimethylphenyl)am ine](PTAA)layer.As a result,the single-junction perovskite solar cell(PSC)gained a power conversion efficiency(PCE)of 20.63%,contributing to a 27.21%efficiency for monolithic perovskite/silicon(double-side polished)2-terminal tandem solar cell,by applying the alkyl aromatic solvent to enhance theπ-πstacking of PTAA molecular chains.The tandem solar cell can maintain 95%initial efficiency after aging over 1000 h.This study provides a universal approach for improving the photovoltaic performance of NiO_(x)/polymer-based perovskite/silicon tandem solar cells and other single junction inverted PSCs.

Self-aggregating behavior of poly(4-vinyl pyridine)and the potential in mitigating sand production based onπ-πstacking interaction

Unconsolidated sandstone reservoirs are most susceptible to sand production that leads to a dramatic oil production decline.In this study,the poly(4-vinyl pyridine)(P_(4)VP)incorporated with self-aggregating behavior was proposed for sand migration control.The P_(4)VP could aggregate sand grains spontaneously throughπ-πstacking interactions to withstand the drag forces sufficiently.The influential factors on the self-aggregating behavior of the P_(4)VP were evaluated by adhesion force test.The adsorption as well as desorption behavior of P_(4)VP on sand grains was characterized by scanning electron microscopy and adhesion force test at different pH conditions.The result indicated that the pH altered the forms of surface silanol groups on sand grains,which in turn affected the adsorption process of P_(4)VP.The spontaneous dimerization of P_(4)VP molecules resulting from theπ-πstacking interaction was demonstrated by reduced density gradient analysis,which contributed to the self-aggregating behavior and the thermally reversible characteristic of the P_(4)VP.Dynamic sand stabilization test revealed that the P_(4)VP showed wide pH and temperature ranges of application.The production of sands can be mitigated effectively at 20-130℃ within the pH range of 4-8.

Enhanced photochemical oxidation ability of carbon nitride by π-πstacking interactions with graphene

A one-pot method for the preparation of g-C3N4/reduced graphene oxide（rGO） composite photocatalysts with controllable band structures is presented.The photocatalysts are characterized by Fouirer transform infrared spectroscopy,X-ray diffraction,scanning electron microscope,transmission electron microscope,and Mott-Schottky analysis.The valance band（VB） of g-C3N4 exhibits a noticeable positive shift upon hybridizing with rGO,and thus results in a strong photo-oxidation ability.The g-C3N4/rGO composites show a higher photodegradation activity for 2,4-dichlorophenol（2,4-DCP） and rhodamine B（RhB） under visible light irradiation（λ≥420 ran）.The g-C3N4/rGO-1sample exhibits the highest photocatalytic activity,which is 1.49 and 1.52 times higher than that of bulk g-C3N4 for 2,4-DCP and 1.52 times degradation,respectively.The enhanced photocatalytic activity for g-C3N4 originates from the improved visible light usage,enhanced electronic conductivity and photo-oxidation ability by the formed strong π-π stacking interactions with rGO.

Molecular dynamics simulation study onπ-πstacking of Gemini surfactants in oil/water systems

Whereas theπ-πstacking interactions at oil/water interfaces can affect interfacial structures hence the interfacial properties,the underlying microscopic mechanism remains largely unknown.We reported an all-atom molecular dynamics(MD)simulation study to demonstrate how the Gemini surfactants with pyrenyl groups affect the interracial properties,structural conformations,and the motion of molecules in the water/n-octane/surfactant ternary systems.It is found that the pyrenyl groups tend to be vertical to the interface owing to theπ-πstacking interaction.Besides,a synergistic effect between theπ-πinteraction and steric hindrance is found,which jointly affects the coalescence of liquid droplets.Therefore,the existence of aromatic groups and a moderate number of surfactants helps to form microemulsion.This work provides a molecular understanding of Gemini surfactants with aromatic groups in microemulsion preparation and applications.

Molecule-based vertical transistor via intermolecular charge transport throughπ-πstacking

Theπ-πstacking is a well-recognized intermolecular interaction that is responsible for the construction of electron hopping channels in numerous conducting frameworks/aggregates.However,the exact role ofπ-to-πchannels within typical single crystalline organic semiconductors remains unclear as the orientations of these molecules are diverse,and their control usually requires additional side chain groups that misrepresent the intrinsic properties of the original semiconducting molecules.Therefore,the construction of conduction channels with intrinsicπ-πstacking in the molecule-based device is crucial for the utilization of their unique transport characteristics and understanding of the transport mechanism.To this end,we present a molecular intercalation strategy that integrates two-dimensional layered materials with functional organic semiconductor molecules for functional molecule-based electronics.Various organic semiconductor molecules can be effectively intercalated into the van der Waals gaps of semi-metallic TaS_(2) withπ-πstacking configuration and controlled intercalant content.Our results show that the vertical charge transport in the stacking direction shows a tunneling-dominated mechanism that strongly depends on the molecular structures.Furthermore,we demonstrated a new type of molecule-based vertical transistor in which TaS_(2) andπ-πstacked organic molecules function as the electrical contact and the active channel,respectively.On/off ratios as high as 447 are achieved under electrostatic modulation in ionic liquid,comparable to the current state-of-the-art molecular transistors.Our study provides an ideal platform for probing intrinsic charge transport acrossπ-πstacked conjugated molecules and also a feasible approach for the construction of high-performance molecule-based electronic devices.

基于FIR-Stacking的刀具磨损预测

针对铣刀加工工件时传感器信号存在噪声、单一传统机器学习模型预测效果不理想的问题,提出一种基于自适应FIR滤波器和Stacking集成模型的刀具磨损预测方法。首先,采用自适应FIR滤波器去噪,计算时域、频域和时频域常用统计量作为信号特征,并对同一信号的多源信号特征进行拼接,经Pearson相关系数筛选保留相关系数大于0.2的特征;最后,以LightGBM、支持向量回归(support vector regression,SVR)、多层感知机(multilayer perceptron,MLP)作为基模型,Lasso作为元模型,构建Stacking集成模型进行刀具磨损预测。使用铣削加工数据集进行验证,结果表明该方法可有效提高预测准确性。

基于VMD-Stacking集成学习的新能源发电功率预测模型

在“双碳”背景下,新能源发电功率的准确预测对于电力系统的平稳运行至关重要。提出了一种自适应性的VMD-Stacking集成模型,以解决数据集变化时传统学习模型预测精度不高的问题。利用皮尔逊相关系数选择与发电功率强相关的气象特征,通过变分模态分解(Variational mode decomposition,VMD)将功率数据分解为多个模态分量,由此构成新的数据集。运用贝叶斯优化算法调整超参数,综合评判随机森林等8种学习模型的评价指标,自适应选出预测性能最优的3种模型作为基学习器,并选用稳定性和泛化能力相对较强的线性回归(Linear Regression)作为元学习器,建立Stacking融合模型。对各分量的预测值叠加,得到最终预测结果。以某新能源场站为例,对风、光电站的发电功率进行预测。算例验证结果表明,该模型在面对不同数据集时,体现出较强的适应性,预测性能也得到显著的提升。

基于Stacking融合的LSTM-SA-RBF短期负荷预测

为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。

坝基灌浆量预测ISSA-Stacking集成学习代理模型研究

灌浆量预测对坝基灌浆施工具有重要意义.由于灌浆工程隐蔽且复杂,传统方法难以实现准确高效的灌浆量预测.代理模型是一种能够建立影响因素与响应值之间近似关系的快速求解方法,然而单一代理模型的预测稳定性和准确性较低,组合代理模型仅将单一模型结果进行加权平均,预测精度仍有待提高.为解决上述问题,本文提出一种ISSA-Stacking集成学习代理模型新方法用于灌浆量预测研究.首先,针对灌浆量预测具有数据量小、影响因素与灌浆量之间非线性关系复杂且预测不确定性较大等特性,基于Stacking集成学习策略,选取在小样本预测中表现优越的支持向量回归(SVR)、具有良好非线性拟合能力的BP神经网络(BPNN)和预测泛化性能及稳定性高的随机森林(RF)等算法作为基学习器,采用自适应学习和不确定性处理能力强的自适应神经模糊推理系统(ANFIS)作为元学习器以集成上述机器学习算法的优势,构建具有更优预测性能和泛化能力的Stacking集成学习方法作为代理模型;其次,为进一步提高模型预测精度,采用混沌理论和Lévy飞行策略改进的麻雀搜索算法(ISSA)对集成学习代理模型进行参数同步优化;最后,将所提ISSA-Stacking集成学习代理模型应用于某实际灌浆工程的灌浆量预测并与其他方法进行对比分析.结果表明,所提方法具有较高的预测精度,绝对平均误差仅为0.21 m^(3);与组合代理模型及单一代理模型(SVR、BPNN和RF)相比,平均精度分别提高24.34%、30.84%、32.68%和26.56%,为灌浆量预测提供了一种新思路.

基于WOA-Stacking集成学习的注塑产品尺寸预测

在现有的基于机器学习的注塑产品尺寸预测模型中,存在单一模型预测精度不高的问题,为了提高实时监测注塑产品尺寸变化的精度,提出了一种基于鲸鱼优化算法(WOA)优化Stacking集成学习的注塑产品尺寸预测方法。首先,整合注塑过程收集到的数据,使用3σ准则进行异常值筛选,再通过随机森林法和互信息法选取关键的特征,作为后续模型的输入特征;其次,在Stacking集成学习框架中,选择K近邻、随机森林和轻量级梯度提升机作为基学习器,选择弹性网络回归作为元学习器,使用WOA优化各个基学习器中的超参数,构建WOA-Stacking集成学习预测模型;最后,将所提的模型应用到注塑产品尺寸预测并与其他模型进行对比分析,以验证本方法的有效性。以第四届工业大数据创新竞赛数据为例,在包含3种集成模型和3种单一模型的对比实验中,选择产品的三维尺寸作为预测目标,实验结果表明WOA-Stacking集成学习模型具有更高的预测精度和拟合能力。

How Does Stacking Pressure Affect the Performance of Solid Electrolytes and All-Solid-State Lithium Metal Batteries?

All-solid-state lithium metal batteries(ASSLMBs)with solid electrolytes(SEs)have emerged as a promising alternative to liquid electrolyte-based Li-ion batteries due to their higher energy density and safety.However,since ASSLMBs lack the wetting properties of liquid electrolytes,they require stacking pressure to prevent contact loss between electrodes and SEs.Though previous studies showed that stacking pressure could impact certain performance aspects,a comprehensive investigation into the effects of stacking pressure has not been conducted.To address this gap,we utilized the Li_(6)PS_(5)Cl solid electrolyte as a reference and investigated the effects of stacking pressures on the performance of SEs and ASSLMBs.We also developed models to explain the underlying origin of these effects and predict battery performance,such as ionic conductivity and critical current density.Our results demonstrated that an appropriate stacking pressure is necessary to achieve optimal performance,and each step of applying pressure requires a specific pressure value.These findings can help explain discrepancies in the literature and provide guidance to establish standardized testing conditions and reporting benchmarks for ASSLMBs.Overall,this study contributes to the understanding of the impact of stacking pressure on the performance of ASSLMBs and highlights the importance of careful pressure optimization for optimal battery performance.

VMD-Stacking集成学习的多特征变量短期负荷预测模型

针对当前短期电力负荷预测结果准确度不够高的问题,提出一种由变分模态分解(variational modal decomposition, VMD)和Stacking集成学习框架组合的多特征变量短期负荷预测模型。在预测前使用VMD算法将负荷数据分解,然后加入对模型重要性较高的特征变量,再建立由轻量级梯度提升机(light gradient boosting machine, LightGBM)与极限梯度提升机(extreme gradient boosting, XGBoost)融合的Stacking集成学习预测模型,并比较不同天气情况下对预测模型准确度的影响。经实际算例对比验证表明:多特征的VMD-Stacking集成学习预测模型的误差较小。采用VMD算法分解历史负荷序列,分解后子模态分量的周期性体现了出来,让模型预测波动性较大的负荷时更容易;温度、天气、农历和节假日情况等影响负荷变化的关键因素有被考虑到,模型的准确度得以提高;Stacking集成学习模型对各算法取长补短,泛化能力增强,预测的准确度高于单一模型。

Transformation of long-period stacking ordered structures in Mg-Gd-Y-Zn alloys upon synergistic characterization of first-principles calculation and experiment and its effects on mechanical properties

Based on experiments and first-principles calculations,the microstructures and mechanical properties of as-cast and solution treated Mg-10Gd-4Y-xZn-0.6Zr(x=0,1,2,wt.%)alloys are investigated.The transformation process of long-period stacking ordered(LPSO)structure during solidification and heat treatment and its effect on the mechanical properties of experimental alloys are discussed.Results reveal that the stacking faults and 18R LPSO phases appear in the as-cast Mg-10Gd-4Y-1Zn-0.6Zr and Mg-10Gd-4Y-2Zn-0.6Zr alloys,respectively.After solution treatment,the stacking faults and 18R LPSO phase transform into 14H LPSO phase.The Enthalpies of formation and reaction energy of 14H and 18R LPSO are calculated based on first-principles.Results show that the alloying ability of 18R is stronger than that of 14H.The reaction energies show that the 14H LPSO phase is more stable than the 18R LPSO.The elastic properties of the 14H and 18R LPSO phases are also evaluated by first-principles calculations,and the results are in good agreement with the experimental results.The precipitation of LPSO phase improves the tensile strength,yield strength and elongation of the alloy.After solution treatment,the Mg-10Gd-4Y-2Zn-0.6Zr alloy has the best mechanical properties,and its ultimate tensile strength and yield strength are 278.7 MPa and 196.4 MPa,respectively.The elongation of Mg-10Gd-4Y-2Zn-0.6Zr reaches 15.1,which is higher than that of Mg-10Gd-4Y0.6Zr alloy.The improving mechanism of elastic modulus by the LPSO phases and the influence on the alloy mechanical properties are also analyzed.

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Existing web-based security applications have failed in many situations due to the great intelligence of attackers.Among web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's information.To avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and efficiency.Based on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is proposed.In order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are considered.The hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the URL.The simulation results show that the proposed hybrid model is more efficient than the existing detection methods.It produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS attacks.In order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment.

基于RF-RFECV和Stacking集成学习的脑卒中预测研究

脑卒中具有发病率高、死亡率高和致残率高的特点,提早发现和治疗显得至关重要。在脑卒中预测方法中,机器学习相对于其他方法具有更好的表现。针对传统的单一机器学习模型在预测的精度或稳定性上都存在局限性的问题,提出了一种基于RF-RFECV和Stacking集成学习的脑卒中预测方法。通过实验证明,该方法可以有效地降低特征维度,获得最优特征子集,与其他的单一模型以及其他集成算法模型相比,Stacking模型的预测精度明显提升,可以更有效地预测脑卒中。

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.

A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Cooperative structure of Li/Ni mixing and stacking faults for achieving high-capacity Co-free Li-rich oxides

Co-free Li-rich layered oxides(LLOs)are emerging as promising cathode materials for Li-ion batteries due to their low cost and high capacity.However,they commonly face severe structural instability and poor electrochemical activity,leading to diminished capacity and voltage performance.Herein,we introduce a Co-free LLO,Li_(1.167)Ni_(0.222)Mn_(0.611)O_(2)(Cf-L1),which features a cooperative structure of Li/Ni mixing and stacking faults.This structure regulates the crystal and electronic structures,resulting in a higher discharge capacity of 300.6 mA h g^(-1)and enhanced rate capability compared to the typical Co-free LLO,Li_(1.2)Ni_(0.2)Mn_(0.6)O_(2)(Cf-Ls).Density functional theory(DFT)indicates that Li/Ni mixing in LLOs leads to increased Li-O-Li configurations and higher anionic redox activities,while stacking faults further optimize the electronic interactions of transition metal(TM)3d and non-bonding O 2p orbitals.Moreover,stacking faults accommodate lattice strain,improving electrochemical reversibility during charge/discharge cycles,as demonstrated by the in situ XRD of Cf-L1 showing less lattice evolution than Cf-Ls.This study offers a structured approach to developing Co-free LLOs with enhanced capacity,voltage,rate capability,and cyclability,significantly impacting the advancement of the next-generation Li-ion batteries.

Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field data.We show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

Correlation of work function and stacking fault energy through Kelvin probe force microscopy and nanohardness in diluteα-magnesium

Electronic interactions of the Group 2A elements with magnesium have been studied through the dilute solid solutions in binary Mg-Ca,Mg-Sr and Mg-Ba systems.This investigation incorporated the difference in the‘Work Function'(ΔWF)measured via Kelvin Probe Force Microscopy(KPFM),as a property directly affected by interatomic bond types,i.e.the electronic structure,nanoindentation measurements,and Stacking Fault Energy values reported in the literature.It was shown that the nano-hardness of the solid-solutionα-Mg phase changed in the order of Mg-Ca>Mg-Sr>Mg-Ba.Thus,it was shown,by also considering the nano-hardness levels,that SFE of a solid-solution is closely correlated with its‘Work Function'level.Nano-hardness measurements on the eutectics andΔWF difference between eutectic phases enabled an assessment of the relative bond strength and the pertinent electronic structures of the eutectics in the three alloys.Correlation withΔWF and at least qualitative verification of those computed SFE values with some experimental measurement techniques were considered important as those computational methods are based on zero Kelvin degree,relatively simple atomic models and a number of assumptions.As asserted by this investigation,if the results of measurement techniques can be qualitatively correlated with those of the computational methods,it can be possible to evaluate the electronic structures in alloys,starting from binary systems,going to ternary and then multi-elemental systems.Our investigation has shown that such a qualitative correlation is possible.After all,the SFE values are not treated as absolute values but rather become essential in comparative investigations when assessing the influences of alloying elements at a fundamental level,that is,free electron density distributions.Our study indicated that the principles of‘electronic metallurgy'in developing multi-elemental alloy systems can be followed via practical experimental methods,i.e.ΔWF measurements using KPFM and nanoindentation.