thermodynamic surface
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Binary Ligands-Mediated Morphological Evolution of Methylammonium Lead Bromide Nanocrystals

CrystEngComm ◽  
2021 ◽  
Author(s):  
Artavazd Kirakosyan ◽  
Min-Gi Jeon ◽  
Chang-Yeon Kim ◽  
Yeonho Kim ◽  
Jihoon Choi
Keyword(s):  
Aging Time ◽  
Feed Rate ◽  
Morphological Evolution ◽  
Surface Ligands ◽  
Thermodynamic Surface ◽  
Lead Bromide

Diverse shape (i.e. nanocube, nanowire, and nanosheet) of CH3NH3PbBr3 nanocrystals could be achieved by a control of thermodynamic (surface ligands) and kinetic (aging time, feed rate, and concentration) parameters for...

scholarly journals Thermodynamic Surface Analyses to Inform Biofilm Resistance

iScience ◽  
2020 ◽  
Vol 23 (11) ◽  
pp. 101702
Author(s):  
T. Brian Cavitt ◽  
Jasmine G. Carlisle ◽  
Alexandra R. Dodds ◽  
Rebecca A. Faulkner ◽  
Tyson C. Garfield ◽  
...  
Keyword(s):  
Thermodynamic Surface

Thermodynamic Surface Analyses to Inform Biofilm Resistance

2020 ◽  
Author(s):  
T. Brian Cavitt ◽  
Jasmine G. Carlisle ◽  
Alexandra R. Dodds ◽  
Rebecca A. Faulkner ◽  
Tyson C. Garfield ◽  
...  
Keyword(s):  
Thermodynamic Surface

Surface and grain boundary energy as the key enabler of ferroelectricity in nanoscale hafnia-zirconia: a comparison of model and experiment

Nanoscale ◽  
2017 ◽  
Vol 9 (28) ◽  
pp. 9973-9986 ◽  
Author(s):  
Min Hyuk Park ◽  
Young Hwan Lee ◽  
Han Joon Kim ◽  
Tony Schenk ◽  
Woongkyu Lee ◽  
...  
Keyword(s):  
Thin Films ◽  
Solid Solution ◽  
Grain Boundary ◽  
Boundary Energy ◽  
Energy Model ◽  
Grain Boundary Energy ◽  
Crystalline Phases ◽  
Thermodynamic Surface

The crystalline phases in hafnia-zirconia solid solution thin films are comprehensively studied by a comparison with the thermodynamic surface or interface/grain boundary energy model.

Machine learnt bond order potential to model metal–organic (Co–C) heterostructures

Nanoscale ◽  
2017 ◽  
Vol 9 (46) ◽  
pp. 18229-18239 ◽  
Author(s):  
Badri Narayanan ◽  
Henry Chan ◽  
Alper Kinaci ◽  
Fatih G. Sen ◽  
Stephen K. Gray ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
First Principles ◽  
Bond Order ◽  
Interatomic Potential ◽  
Thermodynamic Surface ◽  
Bond Order Potential ◽  
Metal Organic ◽  
Robust Framework

We develop a bond-order based interatomic potential for cobalt–carbon from first-principles data using machine learning. This model accurately captures structural, thermodynamic, surface and mechanical properties of metal–organic heterostructures within a single robust framework.

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