Research on Bast Fiber Extracted from the White Bark of Three Species in the Genus Broussonetia

Against the backdrop of a global paper resource shortage,there is a growing need to identify fast-growing tree species capable of producing long-lasting paper.Three plant species namely Broussonetia kazinoki,Broussonetia papyrifera and hybrid paper mulberry,belong to the Broussonetia genus,were collected from China to study their white bark suitability for pulp and papermaking.Their chemical composition revealed that the holocellulose content in Broussonetia kazinoki and Broussonetia papyrifera was more than 80%.The molecular weight distribution of several holocellulose/α-cellulose is observed by GPC,which allows us to better observe the changes of various components on the molecular weight.The yield,lignin,whiteness,and molecular weight of the pulps obtained by NaOH treatment were determined.Optical microscope was used to characterize the fiber length-width ratio and rigidity.Finally,the improvement of the fiber rigidity method based on the Kratky-Porod chain model makes it more theoretical and further reveals the influencing factors of fiber rigidity.This study demonstrates the high potentiality of these three species for papermaking applications.

Designs to Improve Capability of Neural Networks to Make Structural Predictions

A deep neural network model generally consists of different modules that play essential roles in performing a task.The optimal design of a module for use in modeling a physical problem is directly related to the success of the model.In this work,the effectiveness of a number of special modules,the self-attention mechanism for recognizing the importance of molecular sequence information in a polymer,as well as the big-stride representation and conditional random field for enhancing the network ability to produce desired local configurations,is numerically studied.Network models containing these modules are trained by using the well documented data of the native structures of the HP model and assessed according to their capability in making structural predictions of unseen data.The specific network design of self-attention mechanism adopted here is modified from a similar idea in natural language recognition.The big-stride representation module introduced in this work is shown to drastically improve network's capability to model polymer segments of strong lattice position correlations.

Fiber Bending Flexibility Evaluation by Worm-like Chain Model

A new method for characterizing fiber bending flexibility was developed by worm-like chain model proposed by Kratky-Porod,[1]which was first introduced to the pulp and paper field in this study.For the three types of pulps,the experimental results were compared with the KP chain model,and the resulting determination coefficients were all above 0.95,which proved that the model was feasible to be applied to these three fibers.The relation between fiber bending rigidity and that of cellulosic chains inside was discussed to deduce the fiber bending flexibility.The flexibility of an individual fiber can be approximated as the contribution of that of all the cellulose chains inside.By this method,the fiber flexibility values were determined to be in the range of 0.6×10^(11)-3.5×10^(11)N^(-1)·m^(-2),which was comparable to that of the conventional methods recorded in the literature.

Force-Extension Curve of an Entangled Polymer Chain:A Superspace Approach

The statistical mechanics of an ideal polymer chain entangled with static topological constraints is studied using a superspace approach,in which the probability distribution of the polymer is obtained as solutions of the Fokker-Planck equation in a superspace with an inner structure characterized by the n-generator free group.The theory predicts that the force-extension curve of the polymer under the topological constraints has the generic form F=kl+Z/l,where l is an effective extension.Aside from the elastic term that is linear in l,the forceextension curve contains a universal term of the form Z/l.The magnitude of this topological term is determined by the topological charge number Z,which characterizes the topological nature of the static constraints.The theoretical results are further verified by a scaling analysis based on a blob model of the chain conformations.

Kinetic Pattern Formation with Intermolecular Interactions:A Modified Brusselator Model

In this work,we modify the traditi onal Brusselator model to in corporate the intermolecular interactions,based on which a systematic study is performed on the pattern formation mediated by chemical reaction and phase separation.It is found that if the chemical reaction dominates,the pattern formation will be inhibited by the phase separation while if the phase separation dominates,the chemical reaction will preve nt,un der certain conditi ons,the domain size from growing which results in dissipative patter ns other tha n macroscopic phase separations.

An Efficient Algorithm for Self-consistent Field Theory Calculations of Complex Self-assembled Structures of Block Copolymer Melts

Self-consistent field theory（SCFT）, as a state-of-the-art technique for studying the self-assembly of block copolymers, is attracting continuous efforts to improve its accuracy and efficiency. Here we present a fourth-order exponential time differencing Runge-Kutta algorithm（ETDRK4） to solve the modified diffusion equation（MDE） which is the most time-consuming part of a SCFT calculation. By making a careful comparison with currently most efficient and popular algorithms, we demonstrate that the ETDRK4 algorithm significantly reduces the number of chain contour steps in solving the MDE, resulting in a boost of the overall computation efficiency, while it shares the same spatial accuracy with other algorithms. In addition, to demonstrate the power of our ETDRK4 algorithm, we apply it to compute the phase boundaries of the bicontinuous gyroid phase in the strong segregation regime and to verify the existence of the triple point of the O70 phase, the lamellar phase and the cylindrical phase.

Structures and Surface States of Polymer Brushes in Good Solvents:Effects of Surface Interactions

The influence of the surface interaction on the mesoscopic structure of grafted polymers in good solvents has been examined. At high surface coverage, tethered polymers are in the brush state and the parabolic segment density profile is confirmed by self-consistent field theory （SCFT） calculations. It is found that this is a universal behavior for a whole range of surface interactions from complete repulsion to strong attraction. More interestingly, finite surface repulsion may lead to the maximum in the proximal layer of its segment density profile, which is significantly different from both the depletion layer of pure repulsion and the adsorbing layer of attraction. In addition to the brush state on both repulsive and attractive surfaces, three additional surface states were identified by analyzing the scaling behavior of the layer thickness of polymer brushes： the mushroom state on repulsive substrates, the dilute and the semidilute surface states on attractive substrates.