Boosting MA-based two-dimensional Ruddlesden-Popper perovskite solar cells by incorporating a binary spacer

Two-dimensional Ruddlesden-Popper(2DRP)perovskite exhibits excellent stability in perovskite solar cells(PSCs)due to introducing hydrophobic long-chain organic spacers.However,the poor charge transporting property of bulky organic cation spacers limits the performance of 2DRP PSCs.Inspired by the Asite cation alloying strategy in 3D perovskites,2DRP perovskites with a binary spacer can promote charge transporting compared to the unary spacer counterparts.Herein,the superior MA-based 2DRP perovskite films with a binary spacer,including 3-guanidinopropanoic acid(GPA)and 4-fluorophenethylamine(FPEA)are realized.These films(GPA_(0.85)FPEA_(0.15))_(2)MA_(4)Pb_5I_(16)show good morphology,large grain size,decreased trap state density,and preferential orientation of the as-prepared film.Accordingly,the present 2DRP-based PSC with the binary spacer achieves a remarkable efficiency of 18.37%with a V_(OC)of1.15 V,a J_(SC)of 20.13 mA cm^(-2),and an FF of 79.23%.To our knowledge,the PCE value should be the highest for binary spacer MA-based 2DRP(n≤5)PSCs to date.Importantly,owing to the hydrophobic fluorine group of FPEA and the enhanced interlayer interaction by FPEA,the unencapsulated 2DRP PSCs based on binary spacers exhibit much excellent humidity stability and thermal stability than the unary spacer counterparts.

Molecular packing tuning via chlorinated end group enables efficient binary organic solar cells over 18.5%

Designing novel nonfullerene acceptors(NFAs)is of vital importance for the development of organic solar cells(OSC).Modification on the side chain and end group are two powerful tools to construct efficient NFAs.Here,based on the high-performance L8BO,we selected 3-ethylheptyl to substitute the inner chain of 2-ethylhexyl,obtaining the backbone of BON3.Then we introduced different halogen atoms of fluorine and chlorine on 2-(3-oxo-2,3-dihydro-1Hinden-1-ylidene)malononitrile end group(EG)to construct efficient NFAs named BON3-F and BON3-Cl,respectively.Polymer donor D18 was chosen to combine with two novel NFAs to construct OSC devices.Impressively,D18:BON3-Cl-based device shows a remarkable power conversion efficiency(PCE)of 18.57%,with a high open-circuit voltage(V_(OC))of 0.907 V and an excellent fill factor(FF)of 80.44%,which is one of the highest binary PCE of devices based on D18 as the donor.However,BON3-F-based device shows a relatively lower PCE of 17.79%with a decreased FF of 79.05%.The better photovoltaic performance is mainly attributed to the red-shifted absorption,higher electron and hole mobilities,reduced charge recombination,and enhanced molecular packing in the D18:BON3-Cl films.Also,we performed stability tests on two binary systems;the D18:BON3-Cl and D18:BON3-F devices maintain 88.1%and 85.5%of their initial efficiencies after 169 h of storage at 85°C in an N2-filled glove box,respectively.Our work demonstrates the importance of selecting halogen atoms on EG and provides an efficient binary system of D18:BON3-Cl for further improvement of PCE.

Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

Hyperspectral(HS)image classification plays a crucial role in numerous areas including remote sensing(RS),agriculture,and the monitoring of the environment.Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification.This process involves selecting the most informative spectral bands,which leads to a reduction in data volume.Focusing on these key bands also enhances the accuracy of classification algorithms,as redundant or irrelevant bands,which can introduce noise and lower model performance,are excluded.In this paper,we propose an approach for HS image classification using deep Q learning(DQL)and a novel multi-objective binary grey wolf optimizer(MOBGWO).We investigate the MOBGWO for optimal band selection to further enhance the accuracy of HS image classification.In the suggested MOBGWO,a new sigmoid function is introduced as a transfer function to modify the wolves’position.The primary objective of this classification is to reduce the number of bands while maximizing classification accuracy.To evaluate the effectiveness of our approach,we conducted experiments on publicly available HS image datasets,including Pavia University,Washington Mall,and Indian Pines datasets.We compared the performance of our proposed method with several state-of-the-art deep learning(DL)and machine learning(ML)algorithms,including long short-term memory(LSTM),deep neural network(DNN),recurrent neural network(RNN),support vector machine(SVM),and random forest(RF).Our experimental results demonstrate that the Hybrid MOBGWO-DQL significantly improves classification accuracy compared to traditional optimization and DL techniques.MOBGWO-DQL shows greater accuracy in classifying most categories in both datasets used.For the Indian Pine dataset,the MOBGWO-DQL architecture achieved a kappa coefficient(KC)of 97.68%and an overall accuracy(OA)of 94.32%.This was accompanied by the lowest root mean square error(RMSE)of 0.94,indicating very precise predictions with minimal error.In the case of the Pavia University dataset,the MOBGWO-DQL model demonstrated outstanding performance with the highest KC of 98.72%and an impressive OA of 96.01%.It also recorded the lowest RMSE at 0.63,reinforcing its accuracy in predictions.The results clearly demonstrate that the proposed MOBGWO-DQL architecture not only reaches a highly accurate model more quickly but also maintains superior performance throughout the training process.

Generating Sets of the Complete Semigroup of Binary Relations Defined by Semilattices of the Class Σ8(X, 5)

In this article, we study generating sets of the complete semigroups of binary relations defined by X-semilattices of unions of the class Σ8(X, 5). Found uniquely irreducible generating set for the given semigroups and when X is finite set formulas for calculating the number of elements in generating sets are derived.

On Enforcing Dyadic-type Homogeneous Binary Function Product Constraints in MatBase

Homogeneous binary function products are frequently encountered in the sub-universes modeled by databases,spanning from genealogical trees and sports to education and healthcare,etc.Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility.The(Elementary)Mathematical Data Model provides 17 types of dyadic-based homogeneous binary function product constraint categories.MatBase,an intelligent data and knowledge base management system prototype,allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them.This paper describes the algorithms that MatBase uses for enforcing all 17 types of homogeneous binary function product constraint,which may also be employed by developers without access to MatBase.

Analysis of Drug Medical Insurance Access Pricing Based on the Perspective of Binary Equilibrium

Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of drug pricing in China.Methods Through the literature analysis and policy review,the pricing subject,pricing basis and price control system in the pricing process of medical-accessed medicines were analyzed from the perspective of binary equilibrium and harmonious management.Results and Conclusion It is found that four balances in the drug pricing process,two balances in pricing basis and three balances in price control system need to be considered,respectively.Drug pricing is the key content of national medical insurance access,which is also the hotspot of the policy in the pharmaceutical fields in recent years.Drug pricing not only reflects the value of drugs,but also reflects a lot of top-level designs of binary equilibriums in medical insurance policy.While the rational design of drug pricing requires the joint efforts of the government,pharmaceutical companies and relevant experts to comprehensively consider many equilibriums,so as to improve the relevant systems.

Timing and Spectral Analysis of the Black Hole X-Ray Binary MAXI J1803-298 with Insight-HXMT Data

We present a comprehensive analysis of the 2021 outburst of MAXI J1803–298 utilizing observations of the Insight-Hard X-ray Modulation Telescope(Insight-HXMT)spanning from the low hard state to the high soft state.Within the Insight-HXMT data set,compared to the previous work,we identify a more prolonged presence of typeC quasi-periodic oscillations(QPOs)with centroid frequencies ranging from~0.16 to 6.3 Hz,which present correlations with the hardness ratio and the photon index of the Comptonized component.For QPO frequencies less than~2 Hz,the QPO phase lags are hard(photons of 10–19 keV arrive later than those of 1–4 keV),while at higher frequencies,the lags become soft at and above~4 Hz.Furthermore,the spectra in all Insight-HXMT observations consist of a multi-color blackbody component and a Comptonized component,as commonly observed in classical black hole X-ray binaries.We analyze state transitions and the evolution of accretion geometry in this work.The fitted inner disk radius increases abnormally during the low hard state,hypothesized to result from the corona condensing onto the inner disk.Additionally,two significant drops in flux are observed during the soft intermediate state,maybe implying changes in the corona/jet and the disk,respectively.

First Light Curve Analysis of NSVS 8294044,V1023 Her,and V1397 Her Contact Binary Systems

The first photometric light curve investigation of the NSVS 8294044,V1023 Her,and V1397 Her binary systems is presented.We used ground-based observations for the NSVS 8294044 system and Transiting Exoplanet Survey Satellite data for V1023 Her and V1397 Her.The primary and secondary times of minima were extracted from al the data,and,by collecting the literature,a new ephemeris was computed for each system.Linear fits for the O-C diagrams were conducted using the Markov Chain Monte Carlo (MCMC) method.Light curve solutions were performed using the PHysics Of Eclipsing BinariEs Python code and the MCMC approach.The systems were found to be contact binary stars based on the fillout factor and mass ratio.V1023 Her showed the O’Connell effect and a cold starspot on the secondary component was required for the light curve solution.The absolute parameters of the system were estimated based on an empirical relationship between orbital period and mass.We presented a new T–M equation based on a sample of 428 contact binary systems and found that our three target systems were in good agreement with the fit.The positions of the systems were also depicted on the M–L,M–R,q–L_(ratio),and M_(tot)–J_(0)diagrams in the logarithmic scales.

V0405 Dra:A New Deep and Low Mass Ratio Contact Binary with Extremely Fast Decrease in the Orbital Period

V0405 Dra is a W UMa-type binary star.Based on the TESS data,we have conducted an orbital period study and performed a light curve analysis for the system.The orbital period study reveals that the O-C curve for V0405 Dra exhibits secular decrease at an extremely high rate of d P/dt=-2.71×10^(-6)day year^(-1),along with periodic variations characterized by an amplitude of A_(3)=0.0032 day and a period of P_(3)=1.413 years.The orbital periodic change is possibly due to the light-travel time effect resulting from an additional third body in the system,for which we estimate a minimum mass of M_(3)=0.77M_(⊙).By employing the 2013 version of the Wilson-Devinney(W-D)method to synthesize a light curve,we derived photometric solutions indicating that V0405 Dra is a new deep(f=68.7%)and low-mass ratio(q=0.175)contact binary.The fast decrease in its orbital period is likely caused by mass transfer from the more massive primary star to the less massive secondary star,or due to angular momentum loss.With further mass transfer and loss of angular momentum,the binary will gradually evolve into a tighter contact configuration,eventually leading to a merger into a single star,following the evolutionary paths suggested for such deep and low mass ratio contact binaries.

Formation of a Rapidly Rotating Classical Be-star in a Massive Close Binary System

This paper investigates the spin-up of a mass-accreting star in a close binary system passing through the first stage of mass exchange in the Hertzsprung gap. Inside an accreting star, angular momentum is carried by meridional circulation and shear turbulence. The circulation carries part of the angular momentum of the accreted layers to the accretor's surface. The greater the rate of arrival of angular momentum in the accretor is, the greater this part. It is assumed that this part of the angular momentum can be removed by the disk further from the accretor. If the angular momentum in the matter entering the accretor is more than half the Keplerian value, then the angular momentum obtained by the accretor during mass exchange stage does not depend on the rate of arrival of angular momentum. The accretor may have the characteristics of a Be-star immediately after the end of mass exchange.

BSN:Photometric Light Curve Analysis of Two Contact Binary Systems LS Del and V997 Cyg

The light curve analyses and orbital period variations for two contact binary stars,LS Del and V997 Cyg,are presented in this work which was conducted in the frame of the Binary Systems of South and North project Ground-based photometric observations were performed at two observatories in France.We used the Transiting Exoplanet Survey Satellite(TESS)data for extracting times of minima and light curve analysis of the targe systems.The O-C diagram for both systems displays a parabolic trend.LS Del and V997 Cyg’s orbital periods are increasing at rates of dP/dt=7.20×10^(-08)days yr^(-1)and dP/dt=2.54×10^(-08)days yr^(-1),respectively Therefore,it can be concluded that mass is being transferred from the less massive star to the more massive component with a rate of dM/dt=-1.96×10^(-7)M_(⊙)yr^(-1)for the LS Del system,and dM/dt=-3.83×10^(-7)M_(⊙)yr-1for V997 Cyg.The parameters of a third possible object in the system are also considered.The PHysics Of Eclipsing BinariEs Python code was used to analyze the light curves.The light curve solutions needed a cold starspot due to the asymmetry in the LS Del system’s light curve maxima.The mass ratio fill-out factor,and star temperature all indicate that both systems are contact binary types in this investigation.Two methods were applied to estimate the absolute parameters of the systems:one method relied on the parallax of Gaia DR3,and the other used a P-M relationship.The positions of the systems are also depicted on the M-L,M-R q-L_(ratio),and logM_(tot)-logJ_(0)diagrams.We recommend that further observations and investigations be done on the existence of a fourth body in this system.

Vapor-liquid equilibrium modeling for binary system of R152a/R1234ze(E)

At present,the environment impact of refrigerants has been given attention.The binary mixture R152a/R1234ze(E)is an environmentally friendly refrigerant,which solves problems of poor cooling performance of the R1234ze(E)cycle and flammability of R152a.In order to obtain its basic thermal and physical parameters,it is necessary to carry out vapor-liquid equilibrium(VLE)research,and the cubic equation of states(EOS)is often used in the calculation of the thermodynamic properties of mixtures.In this paper,the VLE predicted models for R152a/R1234ze(E)in the temperature range of 298.15-328.15 K were constructed using Soave-Redlich-Kwong(SRK),Peng-Robinson(PR)equations of state(EOS)combined with van der Waals(vd W),Huron-Vidal(HV)mixing rules,respectively.The equilibrium pressures and vapor-phase mole fractions of the models were obtained by calculation,and all four models presented an extreme correlation with the experimental data.And it can be concluded that the calculated results of the PR+HV model are closer to the experimental data than those of the other three models,with the average absolute deviation of 0.0027 for vapor-phase mole fraction(AAD(ycal))and the average absolute relative deviation of 0.243%for equilibrium pressure(AARD(pcal)),which provides a basis for accurately calculating the thermophysical properties of the mixture R152a/R1234ze(E).

Smart Fraud Detection in E-Transactions Using Synthetic Minority Oversampling and Binary Harris Hawks Optimization

Fraud Transactions are haunting the economy of many individuals with several factors across the globe.This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions.This research proposes a novel methodology through three stages.Firstly,Synthetic Minority Oversampling Technique(SMOTE)is applied to get balanced data.Secondly,SMOTE is fed to the nature-inspired Meta Heuristic(MH)algorithm,namely Binary Harris Hawks Optimization(BinHHO),Binary Aquila Optimization(BAO),and Binary Grey Wolf Optimization(BGWO),for feature selection.BinHHO has performed well when compared with the other two.Thirdly,features from BinHHO are fed to the supervised learning algorithms to classify the transactions such as fraud and non-fraud.The efficiency of BinHHO is analyzed with other popular MH algorithms.The BinHHO has achieved the highest accuracy of 99.95%and demonstrates amore significant positive effect on the performance of the proposed model.

Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization

In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of G.The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G.The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)algorithm.This is thefirst attempt to determine the connected resolving set heuristically.BEHHO combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary one.The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set.The feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving set.The proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)algorithms.Computational results confirm the superiority of the BEHHO for determining connected metric dimension.

LIMIT BEHAVIOR OF GROUND STATES OF 2D BINARY BECS IN STEEP POTENTIAL WELLS

We study the ground states of attractive binary Bose-Einstein condensates with N particles,which are trapped in the steep potential wellsλV(x)inℝ2.We show that there exists a positive number N*such that if N>N*,the system admits no ground state for anyλ>0.Moreover,there exist two positive numbers,M*andλ*(N),such that if N<M*,then for anyλ>λ*(N),the system admits at least one ground state.Asλ→∞,for any fixed N<M*,we give a detailed description for the limit behavior of both positive and semi-trivial ground states.

Predicting power conversion efficiency of binary organic solar cells based on Y6 acceptor by machine learning

Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are often complex and expensive in terms of money and time.Machine learning(ML)can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy.Y6 has become the landmark high-performance OSC acceptor material.We collected the power conversion efficiency(PCE)of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors.Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination(R^(2))and Pearson correlation coefficient(r)as the metrics.Among them,decision tree-based algorithms showed excellent predictive capability,especially the Gradient Boosting Regression Tree(GBRT)models based on small molecular donors and polymer donors exhibited that the values of R2are 0.84 and 0.69 for the testing set,respectively.Our work provides a strategy to predict PCEs rapidly,and discovers the influence of the descriptors,thereby being expected to screen high-performance donor material molecules.

Binary molten salt in situ synthesis of sandwich-structure hybrids of hollowβ-Mo2C nanotubes and N-doped carbon nanosheets for hydrogen evolution reaction

Focused exploration of earth-abundant and cost-efficient non-noble metal electrocatalysts with superior hydrogen evolution reaction(HER)performance is very important for large-scale and efficient electrolysis of water.Herein,a sandwich composite structure(designed as MS-Mo2C@NCNS)ofβ-Mo2C hollow nanotubes(HNT)and N-doped carbon nanosheets(NCNS)is designed and prepared using a binary NaCl–KCl molten salt(MS)strategy for HER.The temperature-dominant Kirkendall formation mechanism is tentatively proposed for such a three-dimensional hierarchical framework.Due to its attractive structure and componential synergism,MS-Mo2C@NCNS exposes more effective active sites,confers robust structural stability,and shows significant electrocatalytic activity/stability in HER,with a current density of 10 mA cm-2 and an overpotential of only 98 mV in 1 M KOH.Density functional theory calculations point to the synergistic effect of Mo2C HNT and NCNS,leading to enhanced electronic transport and suitable adsorption free energies of H*(ΔGH*)on the surface of electroactive Mo2C.More significantly,the MS-assisted synthetic methodology here provides an enormous perspective for the commercial development of highly active non-noble metal electrocatalysts toward efficient hydrogen evolution.

First Detailed Analysis of a Relatively Deep, Low Mass-ratio Contact Binary:ATO J108.6991+27.8306

We present the first detailed photometric analysis of ATO J108.6991+27.8306(hereinafter as J108).The shortperiod close binary J108 was observed by the Nanshan 1 m Wide Field Telescope of the Xinjiang Astronomical Observatory.The obtained BVRI-band light curves were used to determine the photometric solution by using the 2003 version of the Wilson-Devinney code.J108 is a typical deep(f>50%),low mass ratio(q<0.25)overcontact binary system with a mass ratio of q=0.1501 and a fill-out factor of f=50.1%,suggesting that it is in the late evolutionary stage of contact binary systems.We found the target to be a W-type W UMa binary and provided evidence for the presence of starspots on both components.From the temperature-luminosity diagram,the main component is the evolved main sequence star with an evolutionary age of about 7.94 Gyr.

Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

The process control-oriented threat,which can exploit OT(Operational Technology)vulnerabilities to forcibly insert abnormal control commands or status information,has become one of the most devastating cyber attacks in industrial automation control.To effectively detect this threat,this paper proposes one functional pattern-related anomaly detection approach,which skillfully collaborates the BinSeg(Binary Segmentation)algorithm with FSM(Finite State Machine)to identify anomalies between measuring data and control data.By detecting the change points of measuring data,the BinSeg algorithm is introduced to generate some initial sequence segments,which can be further classified and merged into different functional patterns due to their backward difference means and lengths.After analyzing the pattern association according to the Bayesian network,one functional state transition model based on FSM,which accurately describes the whole control and monitoring process,is constructed as one feasible detection engine.Finally,we use the typical SWaT(Secure Water Treatment)dataset to evaluate the proposed approach,and the experimental results show that:for one thing,compared with other change-point detection approaches,the BinSeg algorithm can be more suitable for the optimal sequence segmentation of measuring data due to its highest detection accuracy and least consuming time;for another,the proposed approach exhibits relatively excellent detection ability,because the average detection precision,recall rate and F1-score to identify 10 different attacks can reach 0.872,0.982 and 0.896,respectively.

The Effects of the Particle Size Ratio on the Behaviors of Binary Granular Materials

The particle size ratio(PSR)is an important parameter for binary granular materials,which may aect the microstructure and macro behaviors of granular materials.However,the eect of particle ratio on granular assemblies with dierent arrangements is still unclear.To explore and further clarify the eect of PSR in dierent packing structures,three types of numerical samples with regular,layered,and random packing are designed.Numerical results show that PSR has signi􀀀cant eects on binary granular samples with regular packing.The larger the PSR,the stronger the strength,the larger the modulus,and the smaller the angle between the shear band and the load direction.And a theoretical solution of the peak stress ratio vs.PSR is obtained for regular packing,and the results by DEM are in good agreement with the theoretical solution.Under layered packing,PSR has little eect on peak stress ratio due to similar microstructure obtained with the changing of PSR.The modulus slightly increased with the increase of PSR.Under random packing with small grain content of 50%,PSR has little eect in the range of 0.5–0.9,but in a larger range,larger PSR leads to greater modulus.