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 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Search and Characterization of New M...

Baldassarri, Bianca.

FindBook

Google Book

Amazon

博客來

Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning./
作者:	Baldassarri, Bianca.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	177 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
標題:	Energy. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631356
ISBN:	9798380144902

Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning.
Baldassarri, Bianca.

Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 177 p.

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--Northwestern University, 2023.

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

Large scale adoption of sustainable technologies for energy production and storage can be greatly facilitated by scientific advances impacting efficiency, cost and availability. The study of materials is instrumental in both upgrading the performance of existing technologies and enabling the development of new ones, and ab-initio methods and machine learning represent powerful tools in accelerating the process of materials discovery and characterization. This thesis presents multiple works leveraging computational methods to enable increased understanding and predictions of different properties across various classes of materials for renewable energy applications. A large portion of the discussion is dedicated to the thermodynamics of oxygen loss, with particular focus on solar-thermochemical water splitting applications for the production of green hydrogen. After successfully confirming the predictive accuracy of DFT computations of oxygen vacancy formation energy through comparison with experimental data, multiple high-throughput DFT studies are conducted surveying different classes of materials and identifying hundreds of new candidates compounds for solar-thermochemical hydrogen (STCH) applications. The data generated and insight gained through the high-throughput studies are then leveraged to construct machine learning models predicting the oxygen vacancy formation energy, and uncover additional hundreds of new STCH candidate materials. The knowledge acquired from such works is subsequently applied in a different context by exploring the stability of oxygen in cathode materials for Li-ion batteries. Finally, the focus is shifted from bulk to surface phenomena by studying segregation and adsorption behaviours of interest for catalytic applications. A dataset of hundreds of DFT computed segregation energies is constructed and used to train a predictive model, and segregation and oxygen adsorption behaviours in mixed transition metal carbides are investigated to guide the search for new corrosion resistant supports for fuel cell applications.

ISBN: 9798380144902Subjects--Topical Terms:

876794
Energy.
Subjects--Index Terms:

Sustainable technologies

Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning.
LDR:03376nmm a2200397 4500 001 2394889
005 20240513061039.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798380144902
035 $a (MiAaPQ)AAI30631356
035 $a AAI30631356
040 $a MiAaPQ $c MiAaPQ
100 1 $a Baldassarri, Bianca. $0 (orcid)0000-0003-4027-7736 $3 3764385
245 1 0 $a Search and Characterization of New Materials for Renewable Energy Applications Through First Principles Calculations and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 177 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
500 $a Advisor: Wolverton, Chris.
502 $a Thesis (Ph.D.)--Northwestern University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Large scale adoption of sustainable technologies for energy production and storage can be greatly facilitated by scientific advances impacting efficiency, cost and availability. The study of materials is instrumental in both upgrading the performance of existing technologies and enabling the development of new ones, and ab-initio methods and machine learning represent powerful tools in accelerating the process of materials discovery and characterization. This thesis presents multiple works leveraging computational methods to enable increased understanding and predictions of different properties across various classes of materials for renewable energy applications. A large portion of the discussion is dedicated to the thermodynamics of oxygen loss, with particular focus on solar-thermochemical water splitting applications for the production of green hydrogen. After successfully confirming the predictive accuracy of DFT computations of oxygen vacancy formation energy through comparison with experimental data, multiple high-throughput DFT studies are conducted surveying different classes of materials and identifying hundreds of new candidates compounds for solar-thermochemical hydrogen (STCH) applications. The data generated and insight gained through the high-throughput studies are then leveraged to construct machine learning models predicting the oxygen vacancy formation energy, and uncover additional hundreds of new STCH candidate materials. The knowledge acquired from such works is subsequently applied in a different context by exploring the stability of oxygen in cathode materials for Li-ion batteries. Finally, the focus is shifted from bulk to surface phenomena by studying segregation and adsorption behaviours of interest for catalytic applications. A dataset of hundreds of DFT computed segregation energies is constructed and used to train a predictive model, and segregation and oxygen adsorption behaviours in mixed transition metal carbides are investigated to guide the search for new corrosion resistant supports for fuel cell applications.
590 $a School code: 0163.
650 4 $a Energy. $3 876794
650 4 $a Materials science. $3 543314
650 4 $a Applied physics. $3 3343996
653 $a Sustainable technologies
653 $a Renewable energy
653 $a Solar-thermochemical hydrogen
653 $a Oxygen adsorption
690 $a 0794
690 $a 0791
690 $a 0800
690 $a 0215
710 2 $a Northwestern University. $b Applied Physics. $3 3349890
773 0 $t Dissertations Abstracts International $g 85-02B.
790 $a 0163
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631356