Computer Simulation Study On Controlling Nanoparticle Aggregated Structures

Recently,aggregated structures of nanoparticles have drawn great research interest due to their special properties and potential applications.Nanoparticles can form rich cluster structures that are of great significance to materials fabrication.However,when preparing nanoparticle clusters,people still rely on trial and luck.Therefore we need to know how to control nanoparticle aggregated structures,which requires more understanding about nanoparticle aggregation mechanisms.However,we are not clear about the mechanisms of dispersed nanoparticles aggregating into a cluster.Besides,many research works focused on ground-state structures of nanoparticles at low temperatures,but many other amorphous aggregated structures can also form during cluster formation under realistic conditions due to thermal fluctuations.The amorphous aggregated structures are closely related to the thermodynamic properties of systems at finite temperatures.However,to describe these amorphous structures,a quantitative method is still lacked.Moreover,because materials properties are closely related to their structure morphologies,it is important to control structure morphologies in order to design materials with specific functions.Can we start from a specific target nanoparticle aggregated structure to inversely design the interactions between nanoparticles and other factors,in order to achieve target nanoparticle assembled structures?Because these questions in nanoparticle systems are too complicated for experiments to answer,we need some other research tools.Nowadays with the development of computer technologies,computer simulation is becoming another scientific research tool.Many works have used computer simulations(e.g.Monte Carlo methods and molecular dynamics)to investigate thermodynamic and kinetic properties of materials.Besides,when analyzing mechanisms of assembled structure formation in some systems,Markov state models have been used.Moreover,because of the complexity of materials systems,there are large configuration space and complex energy landscapes,which require us to sample the systems with sufficiently long simulations or even use some enhanced sampling methods.In addition,appropriate parameters are required to describe the system and analyze the change of structures in a quantitative way.In this dissertation,we focus on the problem of how to control nanoparticle aggregated structures.By using computer simulations,we analyzed the possible mechanisms of nanoparticle cluster formation,tried to quantitatively describe various amorphous structures during cluster formation,and investigated the possibility of inversely design the sequences and interactions of a nanoparticle chain from a target structure.The main results of this dissertation are:(1)We investigated the possible mechanisms of nanoparticle cluster formation,and the effect of attraction ranges on mechanisms.We used Wang-Landau Monte Carlo to precisely calculate the free-energy barriers of nanoparticles transforming from dispersed states to aggregated states,then we used molecular dynamics to obtain kinetic trajectories,subsequently we used Markov state model to analyze the kinetic trajectories,and finally we obtained the transition pathways and rates of nanoparticle aggregation.We found that during nanoparticle cluster formation,there are multiple aggregation pathways without or with one or more intermediate states,and these intermediate states affect the size dispersity of aggregates during nanoparticle aggregation.When the attraction range becomes narrower,the two-step pathways,which pass through intermediate states,become more competitive when compared with one-step aggregation pathways.Besides,when the attraction range becomes narrower,the transitions between dispersed states and aggregated states becomes slower.Our results provide computational insights into controllable preparation of nanoparticle clusters.(2)In(1),we have studied the mechanisms of aggregation.However,it is still unclear that how to form a specific cluster structure,which requires a method to quantitatively describe different structures during cluster formation.We proposed a method to quantitatively describe various amorphous structures during cluster formation.To show the capability of our method,we did several case studies on systems with different attraction ranges,different attraction strengths,and different degrees of directional attraction.We quantitatively analyzed how aggregated states are affected by external controlling factors,which is meaningful for precisely control and design particle assemblies through tuning the interactions between nanoparticles.(3)We investigated the possibility of inversely designing the sequences and interactions of a nanoparticle chain from a target structure.We extend the method proposed in(2)to describe folded structures of a single copolymer chain.To achieve structures with controlled morphologies,we proposed an inverse design procedure and applied it to a chain model in poor solvent conditions.Starting from a target structure,we exactly enumerated geometrically possible sequences,from which we then randomly selected one sequence and sufficiently sampled the conformation space of the chain with this sequence.Then we inversely designed appropriate interaction strength parameters,and found these parameters are transferable to many other sequences and ensure a high yield of the target structure.Our results confirm the possibility of controlling a single chain to form target structure through inverse design,providing computational insight into controllable preparation of nanoparticle aggregated structures.