Shock Loading of Bone-Inspired Metallic Nanocomposites

2008 ◽  
Vol 139 ◽  
pp. 11-22 ◽  
Author(s):  
Dipanjan Sen ◽  
Markus J. Buehler
Keyword(s):  
Large Scale ◽  
Shock Loading ◽  
Interatomic Potential ◽  
Tensile Load ◽  
Interfacial Strength ◽  
Structural Features ◽  
Valuable Insight ◽  
Length Scales ◽  
Design Elements ◽  
Interfacial Sliding

Nanostructured composites inspired by structural biomaterials such as bone and nacre form intriguing design templates for biomimetic materials. Here we use large scale molecular dynamics to study the shock response of nanocomposites with similar nanoscopic structural features as bone, to determine whether bioinspired nanostructures provide an improved shock mitigating performance. The utilization of these nanostructures is motivated by the toughness of bone under tensile load, which is far greater than its constituent phases and greater than most synthetic materials. To facilitate the computational experiments, we develop a modified version of an Embedded Atom Method (EAM) alloy multi-body interatomic potential to model the mechanical and physical properties of dissimilar phases of the biomimetic bone nanostructure. We find that the geometric arrangement and the specific length scales of design elements at nanoscale does not have a significant effect on shock dissipation, in contrast to the case of tensile loading where the nanostructural length scales strongly influence the mechanical properties. We find that interfacial sliding between the composite’s constituents is a major source of plasticity under shock loading. Based on this finding, we conclude that controlling the interfacial strength can be used to design a material with larger shock absorption. These observations provide valuable insight towards improving the design of nanostructures in shock-absorbing applications, and suggest that by tuning the interfacial properties in the nanocomposite may provide a path to design materials with enhanced shock absorbing capability.

Microstructure Evolution Across Interfaces of Heterogeneous Metal Systems Under Ultrasonic Impact

2004 ◽  
Vol 854 ◽  
Author(s):  
Youhong Li ◽  
Yinon Ashkenazy ◽  
Robert S. Averback
Keyword(s):  
Large Scale ◽  
Shock Loading ◽  
Interfacial Strength ◽  
Interface Layer ◽  
Structural Defects ◽  
Interfacial Region ◽  
Heterogeneous Model ◽  
Interfacial Defects ◽  
Temperature And Pressure ◽  
Pressure Changes

ABSTRACTLarge-scale Molecular Dynamics (MD) studies on heterogeneous, model metal systems subjected to intense shock loading by a flyer plate were carried out. Of interest here is the effect of structural defects on interfacial strength under these extreme conditions. The metal target and flyer were essentially single crystals of Cu, but an interface layer was created by varying the mass of the Cu atoms in part of the sample. Interfacial defects in the form of vacancies, and at different concentrations, were introduced into the interfacial region. In addition to microstructural evolution of damage in this system, the shock induced temperature and pressure changes were also analyzed.

Structural Investigation of a Surfactant-Templated Silica Aerogel by Small-angle Scattering

1998 ◽  
Vol 520 ◽  
Author(s):  
Thomas P. Riekerlt ◽  
Mark T. Anderson ◽  
Patricia S. Sawyer ◽  
Shrish Rane ◽  
Gregory Beaucage
Keyword(s):  
Light Scattering ◽  
Small Angle ◽  
Silica Aerogel ◽  
Large Scale ◽  
Structural Investigation ◽  
Structural Information ◽  
Structural Features ◽  
Length Scales ◽  
X Ray ◽  
Angle Scattering

ABSTRACTThe structure of a surfactant-templated silica aerogel is studied by small-angle x-ray and light scattering. By combining the two techniques, we obtain structural information on length scales from Ångstroms to 0.1 millimeters. For this sample, we find five structural features, including the morphology of large scale aggregates.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

scholarly journals Unusual Structural Features in the Adduct of Dirhodium Tetraacetate with Lysozyme

2021 ◽  
Vol 22 (3) ◽  
pp. 1496
Author(s):  
Domenico Loreto ◽  
Giarita Ferraro ◽  
Antonello Merlino
Keyword(s):  
Anticancer Agents ◽  
Structural Features ◽  
Side Chain ◽  
Side Chains ◽  
Valuable Insight ◽  
Experimental Conditions ◽  
Egg White Lysozyme ◽  
Potential Applications ◽  
Model Protein ◽  
Hen Egg White

The structures of the adducts formed upon reaction of the cytotoxic paddlewheel dirhodium complex [Rh2(μ-O2CCH3)4] with the model protein hen egg white lysozyme (HEWL) under different experimental conditions are reported. Results indicate that [Rh2(μ-O2CCH3)4] extensively reacts with HEWL:it in part breaks down, at variance with what happens in reactions with other proteins. A Rh center coordinates the side chains of Arg14 and His15. Dimeric Rh–Rh units with Rh–Rh distances between 2.3 and 2.5 Å are bound to the side chains of Asp18, Asp101, Asn93, and Lys96, while a dirhodium unit with a Rh–Rh distance of 3.2–3.4 Å binds the C-terminal carboxylate and the side chain of Lys13 at the interface between two symmetry-related molecules. An additional monometallic fragment binds the side chain of Lys33. These data, which are supported by replicated structural determinations, shed light on the reactivity of dirhodium tetracarboxylates with proteins, providing useful information for the design of new Rh-containing biomaterials with an array of potential applications in the field of catalysis or of medicinal chemistry and valuable insight into the mechanism of action of these potential anticancer agents.

scholarly journals Understanding Student Engagement in Large-Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly Rated MOOCs

2018 ◽  
Vol 19 (3) ◽  
Author(s):  
Khe Foon Hew ◽  
Chen Qiao ◽  
Ying Tang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Online Courses ◽  
Subject Area ◽  
Online Course ◽  
Valuable Insight ◽  
Course Quality ◽  
Learning Classifier ◽  
Quality Rating ◽  
Open Courses

Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884 students, who participated in one or more of 18 highly rated MOOCs. Highly rated MOOCs were sampled because they exemplify good practices or teaching strategies. We selected highly rated MOOCs from Coursetalk, an open user-driven aggregator and discovery website that allows students to search and review various MOOCs. We defined a highly rated MOOC as a free online course that received an overall five-star course quality rating, and received at least 50 reviews from different learners within a specific subject area. We described six specific themes found across the entire data corpus: (a) structure and pace, (b) video, (c) instructor, (d) content and resources, (e) interaction and support, and (f) assignment and assessment. The findings of this study provide valuable insight into factors that students find engaging in large-scale open online courses.

scholarly journals The history of settlement and agrarian land use in a boreal forest in Värmland, Sweden, new evidence from pollen analysis

2021 ◽  
Author(s):  
Sigrún Dögg Eddudóttir ◽  
Eva Svensson ◽  
Stefan Nilsson ◽  
Anneli Ekblom ◽  
Karl-Johan Lindholm ◽  
...  
Keyword(s):  
Land Use ◽  
Pollen Analysis ◽  
Large Scale ◽  
Pollen Grains ◽  
Ice Age ◽  
Valuable Insight ◽  
Archaeological Survey ◽  
History Of ◽  
New Evidence ◽  
Insight Into

AbstractShielings are the historically known form of transhumance in Scandinavia, where livestock were moved from the farmstead to sites in the outlands for summer grazing. Pollen analysis has provided a valuable insight into the history of shielings. This paper presents a vegetation reconstruction and archaeological survey from the shieling Kårebolssätern in northern Värmland, western Sweden, a renovated shieling that is still operating today. The first evidence of human activities in the area near Kårebolssätern are Hordeum- and Cannabis-type pollen grains occurring from ca. 100 bc. Further signs of human impact are charcoal and sporadic occurrences of apophyte pollen from ca. ad 250 and pollen indicating opening of the canopy ca. ad 570, probably a result of modification of the forest for grazing. A decrease in land use is seen between ad 1000 and 1250, possibly in response to a shift in emphasis towards large scale commodity production in the outlands. Emphasis on bloomery iron production and pitfall hunting may have caused a shift from agrarian shieling activity. The clearest changes in the pollen assemblage indicating grazing and cultivation occur from the mid-thirteenth century, coinciding with wetter climate at the beginning of the Little Ice Age. The earliest occurrences of anthropochores in the record predate those of other shieling sites in Sweden. The pollen analysis reveals evidence of land use that predates the results of the archaeological survey. The study highlights how pollen analysis can reveal vegetation changes where early archaeological remains are obscure.

scholarly journals Inductive Transfer Learning for Molecular Activity Prediction: Next-Gen QSAR Models with MolPMoFiT

2020 ◽  
Author(s):  
Xinhao Li ◽  
Denis Fourches
Keyword(s):  
Transfer Learning ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Large Scale ◽  
High Reliability ◽  
Structural Features ◽  
Fine Tuning ◽  
Qsar Modeling ◽  
Chemical Structures ◽  
Effective Transfer

<p>Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user’s particular series of compounds. In this study, we propose the <b>Mol</b>ecular <b>P</b>rediction <b>Mo</b>del <b>Fi</b>ne-<b>T</b>uning (<b>MolPMoFiT</b>) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far. <br></p>

scholarly journals The cardiac nanoenvironment: form and function at the nanoscale

2021 ◽  
Author(s):  
Jashan P. Singh ◽  
Jennifer L. Young
Keyword(s):  
Large Scale ◽  
New Materials ◽  
Length Scales ◽  
Form And Function ◽  
Molecular Features ◽  
Wide Range ◽  
Physiological Homeostasis ◽  
Organ Level ◽  
And Function

AbstractMechanical forces in the cardiovascular system occur over a wide range of length scales. At the whole organ level, large scale forces drive the beating heart as a synergistic unit. On the microscale, individual cells and their surrounding extracellular matrix (ECM) exhibit dynamic reciprocity, with mechanical feedback moving bidirectionally. Finally, in the nanometer regime, molecular features of cells and the ECM show remarkable sensitivity to mechanical cues. While small, these nanoscale properties are in many cases directly responsible for the mechanosensitive signaling processes that elicit cellular outcomes. Given the inherent challenges in observing, quantifying, and reconstituting this nanoscale environment, it is not surprising that this landscape has been understudied compared to larger length scales. Here, we aim to shine light upon the cardiac nanoenvironment, which plays a crucial role in maintaining physiological homeostasis while also underlying pathological processes. Thus, we will highlight strategies aimed at (1) elucidating the nanoscale components of the cardiac matrix, and (2) designing new materials and biosystems capable of mimicking these features in vitro.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

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