Dataset Information

Spatially variant microstructured adhesive with one-way friction.

ABSTRACT: Surface microstructures in nature enable diverse and intriguing properties, from the iridescence of butterfly wings to the hydrophobicity of lotus leaves to the controllable adhesion of gecko toes. Many artificial analogues exist; however, there is a key characteristic of the natural materials that is largely absent from the synthetic versions-spatial variation. Here we show that exploiting spatial variation in the design of one class of synthetic microstructure, gecko-inspired adhesives, enables one-way friction, an intriguing property of natural gecko adhesive. When loaded along a surface in the preferred direction, our adhesive material supports forces 100 times larger than when loaded in the reverse direction, representing an asymmetry significantly larger than demonstrated in spatially uniform adhesives. Our study suggests that spatial variation has the potential to advance artificial microstructures, helping to close the gap between synthetic and natural materials.

SUBMITTER: Suresh SA

PROVIDER: S-EPMC6364657 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatially variant microstructured adhesive with one-way friction.

Suresh Srinivasan A SA Kerst Capella F CF Cutkosky Mark R MR Hawkes Elliot W EW

Journal of the Royal Society, Interface 20190101 150

Surface microstructures in nature enable diverse and intriguing properties, from the iridescence of butterfly wings to the hydrophobicity of lotus leaves to the controllable adhesion of gecko toes. Many artificial analogues exist; however, there is a key characteristic of the natural materials that is largely absent from the synthetic versions-spatial variation. Here we show that exploiting spatial variation in the design of one class of synthetic microstructure, gecko-inspired adhesives, enable ...[more]

PMID: 30958166

Similar Datasets

Project description:BackgroundA family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix. This particular form of factor loadings matrix results in a block-diagonal covariance matrix, which is a useful property in gene expression analyses, specifically in biomarker discovery scenarios where blood can potentially act as a surrogate tissue for other less accessible tissues. Prior knowledge of the factor loadings matrix is useful in this application and is reflected in the one-way supervised nature of the algorithm. Additionally, the factor loadings matrix can be assumed to be constant across all components because of the relationship desired between the various types of tissue samples. Parameter estimates are obtained through a variant of the expectation-maximization algorithm and the best-fitting model is selected using the Bayesian information criterion. The family of models is demonstrated using simulated data and two real microarray data sets. The first real data set is from a rat study that investigated the influence of diabetes on gene expression in different tissues. The second real data set is from a human transcriptomics study that focused on blood and immune tissues. The microarray data sets illustrate the biclustering family's performance in biomarker discovery involving peripheral blood as surrogate biopsy material.ResultsThe simulation studies indicate that the algorithm identifies the correct biclusters, most optimally when the number of observation clusters is known. Moreover, the biclustering algorithm identified biclusters comprised of biologically meaningful data related to insulin resistance and immune function in the rat and human real data sets, respectively.ConclusionsInitial results using real data show that this biclustering technique provides a novel approach for biomarker discovery by enabling blood to be used as a surrogate for hard-to-obtain tissues.

Dataset Information

Spatially variant microstructured adhesive with one-way friction.

Publications

Spatially variant microstructured adhesive with one-way friction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets