Forming nanomaterials into hierarchic and organized structures is a rational way of preparing advanced functional materials. The term nanoarchitectonics can express this innovation. This review focuses on recent researches to develop functional materials by forming nanomaterials into organized structures, especially in well-ordered layered structural motifs. This layered nanoarchitectonics can be achieved by using the versatile technology of layer-by-layer assembly. Reassembly of bulk materials into novel layered structures through layered nanoarchitectonics has created many innovative functional materials in a wide variety of fields as can be seen in ferromagnetic nanosheets, sensors, flame-retardant coatings, transparent conductors, electrodes and transistors, walking devices, drug release surfaces, targeting drug carriers and cell culturing.

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Forming nanomaterials as layered functional structures toward materials nanoarchitectonics

Origami-inspired active graphene-based paper with programmed gradients in vertical and lateral directions is developed to address many of the limitations of polymer active materials including slow response and violent operation methods. Specifically, we used function-designed graphene oxide as nanoscale building blocks to fabricate an all-graphene self-folding paper that has a single-component gradient structure. A functional device composed of this graphene paper can (i) adopt predesigned shapes, (ii) walk, and (iii) turn a corner. These processes can be remote-controlled by gentle light or heating. We believe that this self-folding material holds potential for a wide range of applications such as sensing, artificial muscles, and robotics.

https://advances.sciencemag.org/content/advances/1/10/e1500533.full.pdf

Origami-inspired active graphene-based paper for programmable instant self-folding walking devices.

“2D or not 2D”: Shape-programming polymer sheets

Water-enabled self-healing of polyelectrolyte multilayer coatings.

Enough to make your hair curl! Moisture-responsive graphene (G) fibers can be prepared by the positioned laser reduction of graphene oxide (GO) counterparts. When exposed to moisture, the asymmetric G/GO fibers display complex, well-controlled motion/deformation in a predetermined manner. These fibers can function not only as a single-fiber walking robot under humidity alternation but also as a new platform for woven devices and smart textiles.

Graphene fibers with predetermined deformation as moisture-triggered actuators and robots.

Polyelectrolyte Multilayer Films for Building Energetic Walking Devices

This paper deals with static reanalysis of a structure with added degrees of freedom where the nodes of the original structure form a subset of the nodes of the modified structure. A preconditioned conjugate-gradient approach is developed. The preconditioner is constructed, and the implementation of the approach involves only decomposition of the stiffness matrix corresponding to the newly added degrees of freedom. In particular, the approach can adaptively monitor the accuracy of approximate solutions. The approach is applicable to the reanalysis of the structural layout modifications for the case of addition of some nodes, deletion and addition of elements and further changes in the geometry as well as to the local mesh refinements. Numerical examples show that the condition number of the selected preconditioned matrix is largely reduced. Therefore, the fast convergence and accurate results can be achieved by the approach. Copyright © 2005 John Wiley & Sons, Ltd.

Static reanalysis of structures with added degrees of freedom

A finite element algorithm for reanalysis of structures with added degrees of freedom

T HE computation of derivatives of eigenvalues and eigenvectors with respect to model parameters is crucial for many applications [1]. Nelson [2] proposed an approach for general real eigensystemswith distinct eigenvalueswhere only the eigenvector of interest was required. Ojalvo [3], Mills-Curran [4], Dailey [5], and Shaw and Jayasuriya [6] developed Nelson’s method for real symmetric eigensystems with repeated eigenvalues. For computation of a particular solution, themethods described in [3,5]may fail in some circumstances. Other methods have been suggested by MillsCurran [4] and Song et al. [7], but they are difficult to implement. When a system of linear equations is solved, one hopes that the calculated solution is a close representation of the true solution. The computed solution for a system with a large condition number may be inexact due to extreme sensitivity of the solution to small changes in its coefficient matrix and right column vectors [8]. Because some diagonal elements in the coefficient matrix for particular solutions are set to units, its coefficients are not all of the same order of magnitude. So the coefficient matrix has a large condition number. In this Note, Nelson’s method is first extended to the case of repeated eigenvalues for the symmetric real eigensystems and its improvements are then presented. The condition number of the coefficient matrix of the improved method is considerably reduced.

Zhengguang Li

Papers

Polyelectrolyte Multilayer Films for Building Energetic Walking Devices

Static reanalysis of structures with added degrees of freedom

A finite element algorithm for reanalysis of structures with added degrees of freedom

Improved Nelson's Method for Computing Eigenvector Derivatives with Distinct and Repeated Eigenvalues

Analytical approximations to large post-buckling deformation of elastic rings under uniform hydrostatic pressure