東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

High-throughput Quantum Chemical Val...

Ransom, Brandi.

Linked to FindBook

Google Book

Amazon

博客來

High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics./
Author:	Ransom, Brandi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	164 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
Contained By:	Dissertations Abstracts International85-06A.
Subject:	Medical equipment. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726815
ISBN:	9798381020748

High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics.
Ransom, Brandi.

High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 164 p.

Source: Dissertations Abstracts International, Volume: 85-06, Section: A.

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

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

My work applied a vast array of materials discovery techniques to identify polymers which would be suitable for biodegradation and recycling and materials for the improved performance of solid-state batteries. New chemistries in both of these fields will allow for optimization to specific tasks without compromise in performance. Chemically recyclable plastics are a necessary step in order to minimize our feedstock usage which drains earth resources and solid-state batteries are an avenue to bring lithium metal batteries to market. In my attempts to quantify the kinetic barriers for polymers, I found that most techniques are not fast enough to screen a wide field of polymers accurately. Through a tight-binding-density-functional-theory protocol I found patented polycarbonates which can undergo the entropically controlled ring-closing depolymerization for infinite chemical recycling. To mitigate mechanical failure for batteries, I found a zero-strain cathode NbFe3(PO4)6 that has a theoretical capacity orders of magnitude larger than current cathode materials. I also developed a parameter to quickly calculate adhesion between two solid materials and identified electrolyte coating materials which could increase surface contact for lower interfacial resistance. In addition I quantified errors between experimental and computational values of diffusion in solid electrolyte materials. The range of data science, quantum, and classical methods have limitations and trade offs with speed, accuracy, and breadth of application, which I discuss extensively to qualify all presented results. This work contributes to accelerating the commercialization of necessary environmentally-friendly products by presenting a range of viable options to be further optimized by experimentalists.

ISBN: 9798381020748Subjects--Topical Terms:

3560831
Medical equipment.

High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics.
LDR:02955nmm a2200361 4500 001 2393864
005 20240604073611.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798381020748
035 $a (MiAaPQ)AAI30726815
035 $a (MiAaPQ)STANFORDzt049rp1371
035 $a AAI30726815
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ransom, Brandi. $3 3763344
245 1 0 $a High-throughput Quantum Chemical Validation of Machine Learning on Solid-State Batteries and Sustainable Plastics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 164 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: A.
500 $a Advisor: Reed, Evan;Qin, Jian.
502 $a Thesis (Ph.D.)--Stanford University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a My work applied a vast array of materials discovery techniques to identify polymers which would be suitable for biodegradation and recycling and materials for the improved performance of solid-state batteries. New chemistries in both of these fields will allow for optimization to specific tasks without compromise in performance. Chemically recyclable plastics are a necessary step in order to minimize our feedstock usage which drains earth resources and solid-state batteries are an avenue to bring lithium metal batteries to market. In my attempts to quantify the kinetic barriers for polymers, I found that most techniques are not fast enough to screen a wide field of polymers accurately. Through a tight-binding-density-functional-theory protocol I found patented polycarbonates which can undergo the entropically controlled ring-closing depolymerization for infinite chemical recycling. To mitigate mechanical failure for batteries, I found a zero-strain cathode NbFe3(PO4)6 that has a theoretical capacity orders of magnitude larger than current cathode materials. I also developed a parameter to quickly calculate adhesion between two solid materials and identified electrolyte coating materials which could increase surface contact for lower interfacial resistance. In addition I quantified errors between experimental and computational values of diffusion in solid electrolyte materials. The range of data science, quantum, and classical methods have limitations and trade offs with speed, accuracy, and breadth of application, which I discuss extensively to qualify all presented results. This work contributes to accelerating the commercialization of necessary environmentally-friendly products by presenting a range of viable options to be further optimized by experimentalists.
590 $a School code: 0212.
650 4 $a Medical equipment. $3 3560831
650 4 $a Recycling. $3 3564521
650 4 $a Natural resources. $3 544074
650 4 $a Industrial engineering. $3 526216
650 4 $a Medicine. $3 641104
650 4 $a Natural resource management. $3 589570
650 4 $a Sustainability. $3 1029978
690 $a 0546
690 $a 0564
690 $a 0528
690 $a 0640
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 85-06A.
790 $a 0212
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30726815