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Dynamics and Mechanics of Glasses in...

Ivancic, Robert J. S.

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Dynamics and Mechanics of Glasses in Bulk and Confined Materials.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamics and Mechanics of Glasses in Bulk and Confined Materials./
作者:	Ivancic, Robert J. S.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	131 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
標題:	Condensed matter physics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27958435
ISBN:	9798641241920

Dynamics and Mechanics of Glasses in Bulk and Confined Materials.
Ivancic, Robert J. S.

Dynamics and Mechanics of Glasses in Bulk and Confined Materials. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 131 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2020.

This item must not be sold to any third party vendors.

Disordered materials surround us in our daily lives from glasses and plastics to sandcastles and mudslides; however, their properties are poorly understood compared to their crystalline counterparts. Here we address two longstanding questions about glassy materials: how the microscopic structural defects in these systems lead to macroscopic mechanical properties and the causes of anomalous changes in the glass transition upon strong confinement. Until recently, defects in amorphous solids were elusive; however, a novel machine learning method for uncovering them has been discovered. This method allows for the construction of a structural quantity, softness, related to the rearrangement probability of a particle. We begin by comparing rearrangement size of to defect size, the softness correlation length, in a broad set of experimental and simulated materials. These length scales are similar suggesting that softness sets the rearrangement length scale in these materials. Next, we find these defects react similarly for a given amount of strain across all studied materials. Thus, a build-up of softness may set the universal yield strain in disordered materials. To better understand material failure, we introduce an ensemble of simulated polymer nanopillars that we strain apart. We build a machine learning model to detect where shear bands will form using structural features measured prior to deformation. We find that small density fluctuations at the pillar's surface are the most important structural features to determine where shear bands form up to approximately 100nm in diameter. The importance of a plane's mean softness to shear band classification grows with diameter suggesting softness predicts shear banding in the bulk. Finally, we turn to examining the dynamic heterogeneity of glassy films under strong confinement. To understand this, we bias model polymer thin film trajectories toward the dynamic first-order phase transition between high and low mobility dynamic basins. Changes in the transition under confinement are reminiscent of capillary condensation. The changes parallel changes in the glass transition observed in ultrathin film experiments suggesting a possible link between the effects we see and experiments.

ISBN: 9798641241920Subjects--Topical Terms:

3173567
Condensed matter physics.
Subjects--Index Terms:

Disordered material

Dynamics and Mechanics of Glasses in Bulk and Confined Materials.
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300 $a 131 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
500 $a Advisor: Riggleman, Robert A.
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506 $a This item must not be sold to any third party vendors.
520 $a Disordered materials surround us in our daily lives from glasses and plastics to sandcastles and mudslides; however, their properties are poorly understood compared to their crystalline counterparts. Here we address two longstanding questions about glassy materials: how the microscopic structural defects in these systems lead to macroscopic mechanical properties and the causes of anomalous changes in the glass transition upon strong confinement. Until recently, defects in amorphous solids were elusive; however, a novel machine learning method for uncovering them has been discovered. This method allows for the construction of a structural quantity, softness, related to the rearrangement probability of a particle. We begin by comparing rearrangement size of to defect size, the softness correlation length, in a broad set of experimental and simulated materials. These length scales are similar suggesting that softness sets the rearrangement length scale in these materials. Next, we find these defects react similarly for a given amount of strain across all studied materials. Thus, a build-up of softness may set the universal yield strain in disordered materials. To better understand material failure, we introduce an ensemble of simulated polymer nanopillars that we strain apart. We build a machine learning model to detect where shear bands will form using structural features measured prior to deformation. We find that small density fluctuations at the pillar's surface are the most important structural features to determine where shear bands form up to approximately 100nm in diameter. The importance of a plane's mean softness to shear band classification grows with diameter suggesting softness predicts shear banding in the bulk. Finally, we turn to examining the dynamic heterogeneity of glassy films under strong confinement. To understand this, we bias model polymer thin film trajectories toward the dynamic first-order phase transition between high and low mobility dynamic basins. Changes in the transition under confinement are reminiscent of capillary condensation. The changes parallel changes in the glass transition observed in ultrathin film experiments suggesting a possible link between the effects we see and experiments.
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650 4 $a Molecular physics. $3 3174737
650 4 $a Polymer chemistry. $3 3173488
650 4 $a Mechanical engineering. $3 649730
653 $a Disordered material
653 $a Glassy material
653 $a Microscopic structural defect
653 $a Macroscopic mechanical properties
653 $a Anomalous change
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690 $a 0495
690 $a 0609
710 2 $a University of Pennsylvania. $b Physics and Astronomy. $3 2101621
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793 $a English
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