Relation of Physical Behavior to Molecular Packing in Crosslinked Rubber

1961 ◽  
Vol 35 (4) ◽  
pp. 1523-1524 ◽  
Author(s):  
P. Mason
Keyword(s):  
Molecular Packing ◽  
Physical Behavior

Nanomechanical Characterization in the FIB

2005 ◽  
Author(s):  
Thomas M. Moore
Keyword(s):  
Focused Ion Beam ◽  
Ion Beam ◽  
Materials Characterization ◽  
Depth Of Focus ◽  
Ion Milling ◽  
Nano Technology ◽  
Physical Behavior ◽  
Nanomechanical Testing ◽  
Load Cells ◽  
Scanning Electron

Abstract The availability of the focused ion beam (FIB) microscope with its excellent imaging resolution, depth of focus and ion milling capability has made it an appealing platform for materials characterization at the sub-micron, or "nano" level. This article focuses on nanomechanical characterization in the FIB, which is an extension of the FIB capabilities into the realm of nano-technology. It presents examples that demonstrate the power and flexibility of nanomechanical testing in the FIB or scanning electron microscope with a probe shaft that includes a built-in strain gauge. Loads that range from grams to micrograms are achievable. Calibration is limited only by the availability of calibrated load cells in the smallest load ranges. Deflections in the range of a few nanometers range can be accurately applied. Simultaneous electrical, mechanical, and visual data can be combined to provide a revealing study of physical behavior of complex and dynamic nanostructures.

LEARNING NAIVE PHYSICS BY VISUAL OBSERVATION: USING QUALITATIVE SPATIAL REPRESENTATIONS AND PROBABILISTIC REASONING

2001 ◽  
Vol 01 (03) ◽  
pp. 273-285 ◽  
Author(s):  
PAUL A. BOXER
Keyword(s):  
Bayesian Network ◽  
Probabilistic Reasoning ◽  
Autonomous Robots ◽  
Visual Observation ◽  
Spatial Representations ◽  
Physical Behavior ◽  
Naive Physics ◽  
Action At A Distance ◽  
General Physical ◽  
Test Scenarios

Autonomous robots are unsuccessful at operating in complex, unconstrained environments. They lack the ability to learn about the physical behavior of different objects through the use of vision. We combine Bayesian networks and qualitative spatial representation to learn general physical behavior by visual observation. We input training scenarios that allow the system to observe and learn normal physical behavior. The position and velocity of the visible objects are represented as qualitative states. Transitions between these states over time are entered as evidence into a Bayesian network. The network provides probabilities of future transitions to produce predictions of future physical behavior. We use test scenarios to determine how well the approach discriminates between normal and abnormal physical behavior and actively predicts future behavior. We examine the ability of the system to learn three naive physical concepts, "no action at a distance", "solidity" and "movement on continuous paths". We conclude that the combination of qualitative spatial representations and Bayesian network techniques is capable of learning these three rules of naive physics.

Stereoisomerization during Molecular Packing

2021 ◽  
pp. 2100986
Author(s):  
Shidang Xu ◽  
Yukun Duan ◽  
Purnima Manghnani ◽  
Kenry ◽  
Chengjian Chen ◽  
...  
Keyword(s):  
Molecular Packing

scholarly journals Symmetry Based Material Optimization

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 315
Author(s):  
Zeyun Shi ◽  
Jinkeng Lin ◽  
Jiong Chen ◽  
Yao Jin ◽  
Jin Huang
Keyword(s):  
Force Field ◽  
Vector Fields ◽  
General Situation ◽  
Kernel Space ◽  
Dynamic Simulations ◽  
Physical Behavior ◽  
Material Parameters ◽  
Material Optimization ◽  
The Asymmetry ◽  
Asymmetric Mesh

Many man-made or natural objects are composed of symmetric parts and possess symmetric physical behavior. Although its shape can exactly follow a symmetry in the designing or modeling stage, its discretized mesh in the analysis stage may be asymmetric because generating a mesh exactly following the symmetry is usually costly. As a consequence, the expected symmetric physical behavior may not be faithfully reproduced due to the asymmetry of the mesh. To solve this problem, we propose to optimize the material parameters of the mesh for static and kinematic symmetry behavior. Specifically, under the situation of static equilibrium, Young’s modulus is properly scaled so that a symmetric force field leads to symmetric displacement. For kinematics, the mass is optimized to reproduce symmetric acceleration under a symmetric force field. To efficiently measure the deviation from symmetry, we formulate a linear operator whose kernel contains all the symmetric vector fields, which helps to characterize the asymmetry error via a simple ℓ2 norm. To make the resulting material suitable for the general situation, the symmetric training force fields are derived from modal analysis in the above kernel space. Results show that our optimized material significantly reduces the asymmetric error on an asymmetric mesh in both static and dynamic simulations.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

Unveiling the crystalline packing of Y6 in thin films by thermal induced “backbone-on” orientation

2021 ◽  
Author(s):  
Yiqun Xiao ◽  
Jun Yuan ◽  
Guodong Zhou ◽  
Ka Chak Ngan ◽  
Xinxin Xia ◽  
...  
Keyword(s):  
Thin Films ◽  
Solar Cells ◽  
Organic Solar Cells ◽  
High Performance ◽  
Molecular Packing

Researchers are endeavoring to decode the fundamental reasons for the non-fullerene acceptor, Y6, to deliver high-performance organic solar cells. In this manuscript, we tackle this problem from the molecular packing...

scholarly journals Physical behavior of electrostrictive polymers. Part 1: Polarization forces

2021 ◽  
Vol 190 ◽  
pp. 110294
Author(s):  
Gildas Diguet ◽  
Jean-Yves Cavaille ◽  
Gael Sebald ◽  
Toshiyuki Takagi ◽  
Hiroshi Yabu ◽  
...  
Keyword(s):  
Physical Behavior
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