東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning./
作者:	Geoffrion, Luke David.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	176 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Nanotechnology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28862181
ISBN:	9798209888376

Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning.
Geoffrion, Luke David.

Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 176 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of Arkansas at Little Rock, 2021.

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

Nanoscience and nanotechnology are some of the fastest growing fields of materials science that have given impact to several fields of science including biology, medicine, and catalysis. These applications take advantage of several properties of nanoscale materials that are present at sizes below 100 nm namely the increased surface to volume ratio. From a theoretical point of view, nanoscience research is dominated by Density Functional Theory and Molecular Dynamics. These techniques dominate for good reason, but are not without their own limitations due to their computational complexity which results in very long calculation time to complete on materials whose size is greater than ~10 nm. Nano-thermodynamics is a complementary technique to answer fundamental questions theoretically and has shown great accuracy in predicting several materials properties of materials from bulk sizes to sizes around ~4 nm. In this dissertation, the nano-thermodynamics framework is presented and used on three binary nano-alloys: Cu1-xPtx, Bi1-xSbx, and Se1-xTex. In the Cu1-xPtx alloy system, a feature is predicted in its nanophase diagram suggests a transition from a substitutional alloy to an interstitial one. This prediction is confirmed experimentally. In the Bi1-xSbx system, the nanophase diagram is predicted. The results support the idea of a non-enhanced miscibility at the nanoscale which is similar to what is predicted via other techniques for other alloy systems. Additionally, the surface segregation index is introduced. The predicted phase diagrams are confirmed with respect to experimental data in the literature. In the Se1-xTex system, the full phase diagram are predicted at the bulk and nanoscale. In particular, machine learning techniques are used to classify the temperature-composition space for the glassy and crystalline transitions in the Se1-xTex alloy system. Additionally, the systems optical properties are evaluated at the bulk scale and predicted at the nanoscale. Moreover, the optimized range of particles and sizes are suggested for this. Additionally, a model to determine the limit of nano-thermodynamics in predicting the energy bandgap of nanomaterials is described and assessed. The results here in provide a roadmap for the synthesis, application range, and properties of the alloys studied.

ISBN: 9798209888376Subjects--Topical Terms:

526235
Nanotechnology.
Subjects--Index Terms:

Machine learning

Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning.
LDR:03560nmm a2200385 4500 001 2352528
005 20221128104009.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798209888376
035 $a (MiAaPQ)AAI28862181
035 $a AAI28862181
040 $a MiAaPQ $c MiAaPQ
100 1 $a Geoffrion, Luke David. $3 3692165
245 1 0 $a Modelling Physico-Chemical Properties of Binary Nano-Alloys by Nano-Thermodynamics and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 176 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Guisbiers, Gregory.
502 $a Thesis (Ph.D.)--University of Arkansas at Little Rock, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Nanoscience and nanotechnology are some of the fastest growing fields of materials science that have given impact to several fields of science including biology, medicine, and catalysis. These applications take advantage of several properties of nanoscale materials that are present at sizes below 100 nm namely the increased surface to volume ratio. From a theoretical point of view, nanoscience research is dominated by Density Functional Theory and Molecular Dynamics. These techniques dominate for good reason, but are not without their own limitations due to their computational complexity which results in very long calculation time to complete on materials whose size is greater than ~10 nm. Nano-thermodynamics is a complementary technique to answer fundamental questions theoretically and has shown great accuracy in predicting several materials properties of materials from bulk sizes to sizes around ~4 nm. In this dissertation, the nano-thermodynamics framework is presented and used on three binary nano-alloys: Cu1-xPtx, Bi1-xSbx, and Se1-xTex. In the Cu1-xPtx alloy system, a feature is predicted in its nanophase diagram suggests a transition from a substitutional alloy to an interstitial one. This prediction is confirmed experimentally. In the Bi1-xSbx system, the nanophase diagram is predicted. The results support the idea of a non-enhanced miscibility at the nanoscale which is similar to what is predicted via other techniques for other alloy systems. Additionally, the surface segregation index is introduced. The predicted phase diagrams are confirmed with respect to experimental data in the literature. In the Se1-xTex system, the full phase diagram are predicted at the bulk and nanoscale. In particular, machine learning techniques are used to classify the temperature-composition space for the glassy and crystalline transitions in the Se1-xTex alloy system. Additionally, the systems optical properties are evaluated at the bulk scale and predicted at the nanoscale. Moreover, the optimized range of particles and sizes are suggested for this. Additionally, a model to determine the limit of nano-thermodynamics in predicting the energy bandgap of nanomaterials is described and assessed. The results here in provide a roadmap for the synthesis, application range, and properties of the alloys studied.
590 $a School code: 1204.
650 4 $a Nanotechnology. $3 526235
650 4 $a Theoretical physics. $3 2144760
650 4 $a Thermodynamics. $3 517304
653 $a Machine learning
653 $a Metallurgy
653 $a Nanoalloys
653 $a Nanoparticles
653 $a Nanothermodynamics
653 $a Phase diagram
690 $a 0652
690 $a 0753
690 $a 0348
710 2 $a University of Arkansas at Little Rock. $b Physics and Astronomy. $3 3346601
773 0 $t Dissertations Abstracts International $g 83-09B.
790 $a 1204
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28862181