Self-assembly of perovskite nanocrystals:From driving forces to applications

Self-assembly of metal halide perovskite nanocrystals(NCs)into superlattices can exhibit unique collective properties,which have significant application values in the display,detector,and solar cell field.This review discusses the driving forces behind the self-assembly process of perovskite NCs,and the commonly used self-assembly methods and different self-assembly structures are detailed.Subsequently,we summarize the collective optoelectronic properties and application areas of perovskite superlattice structures.Finally,we conclude with an outlook on the potential issues and future challenges in developing perovskite NCs.

Alcohol solvent effect on the self-assembly behaviors of lignin oligomers

The interactions between lignin oligomers and solvents determine the behaviors of lignin oligomers self-assembling into uniform lignin nanoparticles(LNPs).Herein,several alcohol solvents,which readily interact with the lignin oligomers,were adopted to study their effects during solvent shifting process for LNPs’production.The lignin oligomers with widely distributed molecular weight and abundant guaiacyl units were extracted from wood waste(mainly consists of pine wood),exerting outstanding self-assembly capability.Uniform and spherical LNPs were generated in H_(2)O-n-propanol cosolvent,whereas irregular LNPs were obtained in H_(2)O-methanol cosolvent.The unsatisfactory self-assembly performance of the lignin oligomers in H_(2)O-methanol cosolvent could be attributed to two aspects.On one hand,for the initial dissolution state,the distinguishing Hansen solubility parameter and polarity between methanol solvent and lignin oligomers resulted in the poor dispersion of the lignin oligomers.On the other hand,strong hydrogen bonds between methanol solvent and lignin oligomers during solvent shifting process,hindered the interactions among the lignin oligomers for self-assembly.

Chitosan/Sodium Alginate Multilayer pH-Sensitive Films Based on Layer-by-Layer Self-Assembly for Intelligent Packaging

The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium alginate-based multilayer film is fabricated via a layer-by-layer(LBL)self-assembly method.With the help of superior interaction between the layers,the multilayer film possesses excellent mechanical properties(with a tensile strength of 50 MPa).Besides,the film displays outstanding water retention property(blocking moisture of 97.56%)and ultraviolet blocking property.Anthocyanin is introduced into the film to detect the food quality since it is one natural plant polyphenol that is sensitive to the pH changes ranging from 1 to 13 in food when spoilage occurs.It is noted that the film is also bacteriostatic which is desired for food packaging.This study describes a simple technique for the development of advanced multifunctional and fully biodegradable food packaging film and it is a sustainable alternative to plastic packaging.

Hollow tubes constructed by carbon nanotubes self-assembly for CO_(2) capture

Carbon nanotubes(CNTs)have garnered significant attention in the fields of science,engineering,and medicine due to their numerous advantages.The initial step towards harnessing the potential of CNTs involves their macroscopic assembly.The present study employed a gentle and direct self-assembly technique,wherein controlled growth of CNT sheaths occurred on the metal wire’s surface,followed by etching of the remaining metal to obtain the hollow tubes composed of CNTs.By controlling the growth time and temperature,it is possible to alter the thickness of the CNTs sheath.After immersing in a solution containing 1 g/L of CNTs at 60℃ for 24 h,the resulting CNTs layer achieved a thickness of up to 60μm.These hollow CNTs tubes with varying inner diameters were prepared through surface reinforcement using polymers and sacrificing metal wires,thereby exhibiting exceptional attributes such as robustness,flexibility,air tightness,and high adsorption capacity that effectively capture CO_(2) from the gas mixture.

A Non-parametric Gradient-Based Shape Optimization Approach for Solving Inverse Problems in Directed Self-Assemblyof Block Copolymers

We consider the inverse problem of finding guiding pattern shapes that result in desired self-assembly morphologies of block copolymer melts.Specifically,we model polymer selfassembly using the self-consistent field theory and derive,in a non-parametric setting,the sensitivity of the dissimilarity between the desired and the actual morphologies to arbitrary perturbations in the guiding pattern shape.The sensitivity is then used for the optimization of the confining pattern shapes such that the dissimilarity between the desired and the actual morphologies is minimized.The efficiency and robustness of the proposed gradient-based algorithm are demonstrated in a number of examples related to templating vertical interconnect accesses(VIA).

Confined cobalt single-atom catalysts with strong electronic metal-support interactions based on a biomimetic self-assembly strategy

Designing high-performance and low-cost electrocatalysts for oxygen evolu-tion reaction(OER)is critical for the conversion and storage of sustainable energy technologies.Inspired by the biomineralization process,we utilized the phosphorylation sites of collagen molecules to combine with cobalt-based mononuclear precursors at the molecular level and built a three-dimensional(3D)porous hierarchical material through a bottom-up biomimetic self-assembly strategy to obtain single-atom catalysts confined on carbonized biomimetic self-assembled carriers(Co SACs/cBSC)after subsequent high-temperature annealing.In this strategy,the biomolecule improved the anchoring efficiency of the metal precursor through precise functional groups;meanwhile,the binding-then-assembling strategy also effectively suppressed the nonspecific adsorption of metal ions,ultimately preventing atomic agglomeration and achieving strong electronic metal-support interactions(EMSIs).Experimental characterizations confirm that binding forms between cobalt metal and carbonized self-assembled substrate(Co–O_(4)–P).Theoretical calculations disclose that the local environment changes significantly tailored the Co d-band center,and optimized the binding energy of oxygenated intermediates and the energy barrier of oxygen release.As a result,the obtained Co SACs/cBSC catalyst can achieve remarkable OER activity and 24 h durability in 1 M KOH(η10 at 288 mV;Tafel slope of 44 mV dec-1),better than other transition metal-based catalysts and commercial IrO_(2).Overall,we presented a self-assembly strategy to prepare transition metal SACs with strong EMSIs,providing a new avenue for the preparation of efficient catalysts with fine atomic structures.

Layer by Layer Self-assembly Fiber-based Flexible Electrochemical Transistor

Poly(3,4-ethylenedioxyethiophene)-polystyrene sulfonic acid(PEDOT:PSS)/polyallyl dimethyl ammonium chloride modified reduced graphene oxide(PDDA-rGO)was layer by layer self-assembled on the cotton fiber.The surface morphology and electric property was investigated.The results confirmed the dense membrane of PEDOT:PSS and the lamellar structure of PDDA-rGO on the fibers.It has excellent electrical conductivity and mechanical properties.The fiber based electrochemical transistor(FECTs)prepared by the composite conductive fiber has a maximum output current of 8.7 mA,a transconductance peak of 10 mS,an on time of 1.37 s,an off time of 1.6 s and excellent switching stability.Most importantly,the devices by layer by layer self-assembly technology opens a path for the true integration of organic electronics with traditional textile technologies and materials,laying the foundation for their later widespread application.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks

Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.

Combination therapy to overcome ferroptosis resistance by biomimetic self-assembly nano-prodrug

Ferroptosis has emerged as a potent form of no-apoptotic cell death that offers a promising alternative to avoid the chemoresistance of apoptotic pathways and serves as a vulnerability of cancer.Herein,we have constructed a biomimetic self-assembly nano-prodrug system that enables the co-delivery of gefitinib(Gefi),ferrocene(Fc)and dihydroartemisinin(DHA)for the combined therapy of both ferroptosis and apoptosis.In the tumor microenvironment,this nano-prodrug is able to disassemble and trigger drug release under high levels of GSH.Interestingly,the released DHA can downregulate GPX4 level for the enhancement of intracellular ferroptosis from Fc,further executing tumor cell death with concomitant chemotherapy by Gefi.More importantly,this nano-prodrug provides highly homologous targeting ability by coating related cell membranes and exhibits outstanding inhibition of tumor growth and metastasis,as well as no noticeable side-effects during treatments.This simple small molecular self-assembled nano-prodrug provides a new reasonably designed modality for ferroptosis-combined chemotherapy.

Efficient conversion of lignin waste and self-assembly synthesis of C@MnCo_(2)O_(4)for asymmetric supercapacitors with high energy density

Lignin waste from the papermaking and biorefineries industry is a significantly promising renewable resource to prepare advanced carbon materials for diverse applications,such as the electrodes of supercapacitors;however,the improvement of their energy density remains a challenge.Here,we design a green and universal approach to prepare the composite electrode material,which is composed of lignin-phenolformaldehyde resins derived hierarchical porous carbon(LR-HPC)as conductive skeletons and the self-assembly manganese cobaltite(MnCo_(2)O_(4))nanocrystals as active sites.The synthesized C@MnCo_(2)O_(4)composite has an abundant porous structure and superior electronic conductivity,allowing for more charge/electron mass transfer channels and active sites for the redox reactions.The composite shows excellent electrochemical performance,such as the maximum specific capacitance of~726 mF cm^(-2)at 0.5 mV s^(-1),due to the significantly enhanced interactive interface between LR-HPC and MnCo_(2)O_(4)crystals.The assembled all-solid-state asymmetric supercapacitor,with the LR-HPC and C@MnCo_(2)O_(4)as cathode and anode,respectively,exhibits the highest volumetric energy density of 0.68 mWh cm^(-3)at a power density of 8.2 mW cm^(-3).Moreover,this device shows a high capacity retention ratio of~87.6%at 5 mA cm^(-2)after 5000 cycles.

Underwater four-quadrant dual-beam circumferential scanning laser fuze using nonlinear adaptive backscatter filter based on pauseable SAF-LMS algorithm

The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.

Rao Algorithms-Based Structure Optimization for Heterogeneous Wireless Sensor Networks

The structural optimization of wireless sensor networks is a critical issue because it impacts energy consumption and hence the network’s lifetime.Many studies have been conducted for homogeneous networks,but few have been performed for heterogeneouswireless sensor networks.This paper utilizes Rao algorithms to optimize the structure of heterogeneous wireless sensor networks according to node locations and their initial energies.The proposed algorithms lack algorithm-specific parameters and metaphorical connotations.The proposed algorithms examine the search space based on the relations of the population with the best,worst,and randomly assigned solutions.The proposed algorithms can be evaluated using any routing protocol,however,we have chosen the well-known routing protocols in the literature:Low Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Gathering in Sensor Information Systems(PEAGSIS),Partitioned-based Energy-efficient LEACH(PE-LEACH),and the Power-Efficient Gathering in Sensor Information Systems Neural Network(PEAGSIS-NN)recent routing protocol.We compare our optimized method with the Jaya,the Particle Swarm Optimization-based Energy Efficient Clustering(PSO-EEC)protocol,and the hybrid Harmony Search Algorithm and PSO(HSA-PSO)algorithms.The efficiencies of our proposed algorithms are evaluated by conducting experiments in terms of the network lifetime(first dead node,half dead nodes,and last dead node),energy consumption,packets to cluster head,and packets to the base station.The experimental results were compared with those obtained using the Jaya optimization algorithm.The proposed algorithms exhibited the best performance.The proposed approach successfully prolongs the network lifetime by 71% for the PEAGSIS protocol,51% for the LEACH protocol,10% for the PE-LEACH protocol,and 73% for the PEGSIS-NN protocol;Moreover,it enhances other criteria such as energy conservation,fitness convergence,packets to cluster head,and packets to the base station.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Analysis on the formation principle and present situation of nano phase state of multi-component self-assembly of traditional Chinese compound medicine decoction

Traditional Chinese medicine decoction is a complex polydispersed phase system containing real solution,colloid solution,emulsion and suspension.The compound decoction of traditional Chinese medicine has complex components,including saponins,alkaloids,polysaccharides,flavonoids,amino acids and so on,which can be self-assembled to form gels,fibers,micelles,vesicles and so on.The self-assembled nano-phase not only neutralizes the single drug and reduces the toxicity and side effects,but also has its own pharmacological effects,which complement each other to achieve synergistic effect,so as to achieve the role of drug supplement,which is of research significance.The formation principle,solubilization and synergism principle and characterization method of multi-component self-assembly of traditional Chinese medicine compound decoction are discussed in this paper.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

A Review of Image Steganography Based on Multiple Hashing Algorithm

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item.From ancient times to the present,the security of secret or vital information has always been a significant problem.The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest.Therefore,several approaches,including steganography,have been developed by researchers over time to enable safe data transit.In this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,etc.We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)approach.In this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this strategy.Before encoding the data and embedding it into a carry image,this review verifies that it has been encrypted.Usually,embedded text in photos conveys crucial signals about the content.This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport.If the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.