scholarly journals Quantifying the role of mineral bridges on the fracture resistance of nacre-like composites

2018 ◽  
Vol 115 (50) ◽  
pp. 12698-12703 ◽  
Author(s):  
Madeleine Grossman ◽  
Florian Bouville ◽  
Kunal Masania ◽  
André R. Studart
Keyword(s):  
Flexural Strength ◽  
Fracture Resistance ◽  
Brittle Materials ◽  
Nacreous Layer ◽  
Structure Property ◽  
Design Concepts ◽  
Experimental Systems ◽  
Brick And Mortar ◽  
Balance Of Forces

The nacreous layer of mollusk shells holds design concepts that can effectively enhance the fracture resistance of lightweight brittle materials. Mineral bridges are known to increase the fracture resistance of nacre-inspired materials, but their role is difficult to quantify due to the lack of experimental systems where only this parameter is controllably varied. In this study, we fabricate tunable nacre-like composites that are used as a model to experimentally quantify the influence of the density of mineral bridges alone on the fracture properties of nacre-like architectures. The composites exhibit a brick-and-mortar architecture comprising highly aligned alumina platelets that are interconnected by titania mineral bridges and infiltrated by an epoxy organic phase. By combining experimental mechanical data with image analysis of such composite microstructures, an analytical model is put forward based on a simple balance of forces acting on an individual bridged platelet. Based on this model, we predict the flexural strength of the nacre-like composite to scale linearly with the density of mineral bridges, as long as the mineral interconnectivity is low enough to keep fracture in a platelet pullout mode. Increasing the mineral interconnectivity beyond this limit leads to platelet fracture and catastrophic failure of the composite. This structure-property correlation provides powerful quantitative guidelines for the design of lightweight brittle materials with enhanced fracture resistance. We illustrate this potential by fabricating nacre-like bulk composites with unparalleled flexural strength combined with noncatastrophic failure.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

UNDER-GRADUATE STUDENTS AND THE ROLE OF E- LIBRARIES TO UPLIFT THE QUALITY OF HIGHER EDUCATION

2018 ◽  
Vol 6 (26) ◽  
Author(s):  
Dharambeer Singh
Keyword(s):  
Graduate Students ◽  
Digital Libraries ◽  
Rural Areas ◽  
Multiple Access ◽  
Information Needs ◽  
Quality Of Higher Education ◽  
Brick And Mortar ◽  
Education Status

Digital libraries, designed to serve people and their information needs in the same way as traditional libraries, present distinct advantages over brick and mortar facilities: elimination of physical boundaries, round-the-clock access to information, multiple access points, networking abilities, and extended search functions. As a result, they should be especially well-suited for the disables. However, minorities, those affected by lower income and education status, persons living in rural areas, the physically challanged, and developing countries as a whole consistently suffer from a lack of accessibility to digital libraries. This paper evaluates the effectiveness and relevance of digital libraries currently in place and discusses what could and should be done to improve accessibility to digital libraries for under-graduate students.

Stroke Genetics: Turning Discoveries into Clinical Applications

Stroke ◽  
2021 ◽  
Author(s):  
Martin Dichgans ◽  
Nathalie Beaufort ◽  
Stephanie Debette ◽  
Christopher D. Anderson
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Clinical Applications ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Neuroprotective Agents ◽  
Experimental Systems ◽  
Genome Wide ◽  
Rapid Pace

The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.

scholarly journals Revisiting the Organic Template Model through the Microstructural Study of Shell Development in Pinctada margaritifera, the Polynesian Pearl Oyster

Minerals ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 370 ◽  
Author(s):  
Jean-Pierre Cuif ◽  
Yannicke Dauphin ◽  
Gilles Luquet ◽  
Kadda Medjoubi ◽  
Andrea Somogyi ◽  
...  
Keyword(s):  
Pearl Oyster ◽  
Nacreous Layer ◽  
Prismatic Layer ◽  
Pinctada Margaritifera ◽  
Microstructural Study ◽  
Leading Role ◽  
Complex Process ◽  
Gathering Data

A top-down approach to the mineralized structures and developmental steps that can be separated in the shells of Pinctada margaritifera was carried out. Detailed characterizations show that each of the two major layers usually taken into account (the outer prismatic layer and the inner nacreous layer) is actually the result of a complex process during which the microstructural patterns were progressively established. From its early growing stages in the deeper part of the periostracal grove up to the formation of the most inner nacreous layers, this species provides a demonstrative case study illustrating the leading role of specifically secreted organic structures as determinants of the crystallographic properties of the shell-building units. Gathering data established at various observational scales ranging from morphology to the nanometer level, this study allows for a reexamination of the recent and current biomineralization models.

Role of interface properties on the toughness of brittle matrix composites reinforced with ductile fibers

1992 ◽  
Vol 7 (11) ◽  
pp. 3132-3138 ◽  
Author(s):  
H.E. Dève ◽  
S. Schmauder
Keyword(s):  
Fracture Resistance ◽  
Crack Opening ◽  
Geometrical Model ◽  
Interface Properties ◽  
Matrix Composites ◽  
Brittle Matrix Composites ◽  
The Matrix ◽  
Interface Sliding ◽  
Brittle Composites

The incorporation of ductile fibers in brittle matrices can lead to a significant increase in fracture resistance. The increase in toughness that derives from crack bridging is governed by the properties of the matrix/fiber interface and the ductility of the fibers. The current study addresses the role of interface sliding stress on the toughness of brittle composites reinforced with ductile fibers. The debond length is explicitly related to the interface sliding stress and the properties of the fiber. It is then incorporated into a geometrical model to simulate the bridging tractions versus crack opening under condition of continuous debonding. The implications on the effect of interfaces on the resistance curve are discussed.

scholarly journals The role of taking into account the interatomic interaction in predicting the complex of structurally-sensitive properties of steels and alloys for special purpose

2018 ◽  
pp. 361-370
Author(s):  
I.R. Snihura ◽  
D.N. Togobitskaya
Keyword(s):  
Computational Models ◽  
State Parameter ◽  
Interatomic Interaction ◽  
Homogeneous System ◽  
Experimental Information ◽  
Structure Property ◽  
Chain Composition ◽  
Forecast Models ◽  
Composition Structure

The aim of the work is to identify the influence of the chemical composition of steels and special-purpose alloys on the formation of their physicochemical and structural-sensitive properties. This problem is solved by mathematical modeling of the inseparable chain «composition - structure – property» taking into account the parameters of interatomic interaction in the melt based on the concept of a directed chemical bond. A steel melt is considered as a chemically homogeneous system, and the state of the melts is expressed through a set of integral parameters, the main of which are: Zy - system charge state parameter (e); r - statistically average internuclear distance (10-1nm); tgα is a constant for each element, which characterizes the change in the radius of the ion as its charge changes. On the basis of experimental information on properties and using the parameters of interatomic interaction, computational models are proposed for predicting the properties of steels and alloys. The forecast models took into account the parameters of micro-inhomogeneity of steel, which ensured a high accuracy of the operational forecast. A comparative analysis of the results of steel melting with the corresponding calculations based on the JMatPro software package confirmed the effectiveness of using the interatomic interaction parameters as models. The proposed models for determining the melting of chromium-nickel steels are recommended for use with the content of basic elements Cr, Ni from 0 to 30%. The research results are recommended for use in industrial environments through the integration of the developed models in the process control system of steelmaking, which will contribute to the directed formation of the composition and properties of smelting products, as well as reducing energy costs.

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