Dataset Information

The MoSeS dynamic omnigami paradigm for smart shape and composition programmable 2D materials.

ABSTRACT: The properties of 2D materials can be broadly tuned through alloying and phase and strain engineering. Shape programmable materials offer tremendous functionality, but sub-micron objects are typically unachievable with conventional thin films. Here we propose a new approach, combining phase/strain engineering with shape programming, to form 3D objects by patterned alloying of 2D transition metal dichalcogenide (TMD) monolayers. Conjugately, monolayers can be compositionally patterned using non-flat substrates. For concreteness, we focus on the TMD alloy MoSe[Formula: see text]S[Formula: see text]; i.e., MoSeS. These 2D materials down-scale shape/composition programming to nanoscale objects/patterns, provide control of both bending and stretching deformations, are reversibly actuatable with electric fields, and possess the extraordinary and diverse properties of TMDs. Utilizing a first principles-informed continuum model, we demonstrate how a variety of shapes/composition patterns can be programmed and reversibly modulated across length scales. The vast space of possible designs and scales enables novel material properties and thus new applications spanning flexible electronics/optics, catalysis, responsive coatings, and soft robotics.

SUBMITTER: Berry J

PROVIDER: S-EPMC6858317 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The MoSeS dynamic omnigami paradigm for smart shape and composition programmable 2D materials.

Berry Joel J Ristić Simeon S Zhou Songsong S Park Jiwoong J Srolovitz David J DJ

Nature communications 20191115 1

The properties of 2D materials can be broadly tuned through alloying and phase and strain engineering. Shape programmable materials offer tremendous functionality, but sub-micron objects are typically unachievable with conventional thin films. Here we propose a new approach, combining phase/strain engineering with shape programming, to form 3D objects by patterned alloying of 2D transition metal dichalcogenide (TMD) monolayers. Conjugately, monolayers can be compositionally patterned using non-f ...[more]

PMID: 31729363

Similar Datasets

Project description:PurposeTotal and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.MethodsDuplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets.ResultsResults were reported on a held-out set. Body composition prediction achieved R2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests.ConclusionTotal and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible.

Dataset Information

The MoSeS dynamic omnigami paradigm for smart shape and composition programmable 2D materials.

Publications

The MoSeS dynamic omnigami paradigm for smart shape and composition programmable 2D materials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets