Bridging from the Sequence to Architecture: Graft Copolymers Engineering via Successive Latent Monomer and Grafting-from Strategies

The on-demand building copolymer structures,from sequence to architecture,is crucial in understanding the relation between pol-ymer structure and property,meanwhile motivating the innovation of polymer hierarchy.However,the challenge is conspicuous for,complicated polymer structures from inherently intricate polymerization.In this work,copolymers with tailored grafting density and distributions were achieved using successive latent monomer and grafting-from strategies.The hydroxyl group functionalized fu-ran/maleimide adduct(FMOH)was selected as the latent monomer for RAFT polymerization of an array of copolymers with tailored localization of hydroxyl group along the main chain.The hydroxyI group further initiated the ring opening polymerization(ROP)of L-lactide or ε-caprolactone,resulting in a library of multicomponent copolymers via grafting-from strategy.The initiating efficiency reached to-100% with variable molecular weight(21300-58600 Da)and narrow distributions(Ð_(M)<1.25),indicating that such graft copolymers possessed controlled density and distribution of side chains as its linear template.The investigation on thermal properties of the well-defined graft copolymers implied that the precise tailoring over copolymer structures at the molecule level could lead to tunable chemical/physical properties.This work bridged polymer from sequence to architecture,unveiled a new meth-od in creating graft copolymers with programmable structures and provided the insight into the structure/property relationship.

Entity set expansion in knowledge graph:a heterogeneous information network perspective

Entity set expansion(ESE)aims to expand an entity seed set to obtain more entities which have common properties.ESE is important for many applications such as dictionary con-struction and query suggestion.Traditional ESE methods relied heavily on the text and Web information of entities.Recently,some ESE methods employed knowledge graphs(KGs)to extend entities.However,they failed to effectively and fficiently utilize the rich semantics contained in a KG and ignored the text information of entities in Wikipedia.In this paper,we model a KG as a heterogeneous information network(HIN)containing multiple types of objects and relations.Fine-grained multi-type meta paths are proposed to capture the hidden relation among seed entities in a KG and thus to retrieve candidate entities.Then we rank the entities according to the meta path based structural similarity.Furthermore,to utilize the text description of entities in Wikipedia,we propose an extended model CoMeSE++which combines both structural information revealed by a KG and text information in Wikipedia for ESE.Extensive experiments on real-world datasets demonstrate that our model achieves better performance by combining structural and textual information of entities.