BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Thesis/Dissertations DESCRIPTION:Topic: Entropy-Driven Design and Physics-Enforced Neural Networ k Modeling of Multi-Polymer Solid Electrolytes for Structural Energy Stora ge Abstract: Structural energy storage requires electrolytes that can both conduct ions and carry mechanical loads, yet in solid polymer electrolyte s (SPEs) ionic conductivity and mechanical properties are often traded aga inst each other. This proposal examines configurational entropy, introduce d by blending chemically distinct polymers, as a design variable to ease t his trade-off. Dual-polymer electrolytes of poly(lactic acid) (PLA) and po ly(methyl methacrylate) (PMMA) with LiTFSI formed miscible, single-phase m atrices with higher ionic conductivity than single-polymer electrolytes, a nd tuning polymer chain length balanced ionic transport against mechanical integrity. Adding poly(vinylidene fluoride) (PVDF) to form ternary blends produced more diverse Li+ coordination environments in experiments and mo lecular dynamics simulations, but only two of seven ternary compositions o utperformed all single- and dual-polymer electrolytes, indicating that com positional balance governs multifunctional performance. Preliminary five-p olymer blends also show promising ionic conductivity, but the composition space is too large to explore experimentally. The proposed work will devel op physics-guided machine learning models, trained on a consistently measu red dataset and informed by molecular simulations, to predict ionic conduc tivity and mechanical properties and to guide composition selection. Toget her, this work aims to establish how entropy and compositional balance can guide the design of SPEs for structural energy storage. ADVISOR(s): Dr. Caiwei Shen, Department of Mechanical Engineering, (cshen2@umassd.edu) CO MMITTEE MEMBERS: Dr. Maricris Mayes, Department of Chemistry and Biochemi stry Dr. Ruolin Zhou, Department of Electrical and Computer Engineering NOTE: All EAS Students are ENCOURAGED to attend.\nEvent page: https://www.umassd.edu/events/cms/11-9-26-eas-doctoral-proposal-defense-- by-md-shovon-hossain.php X-ALT-DESC;FMTTYPE=text/html:

Topic: Entropy-Driven Design an d Physics-Enforced Neural Network Modeling of Multi-Polymer Solid Electrol ytes for Structural Energy Storage

Abstract:

Structural en ergy storage requires electrolytes that can both conduct ions and carry me chanical loads\, yet in solid polymer electrolytes (SPEs) ionic conductivi ty and mechanical properties are often traded against each other. This pro posal examines configurational entropy\, introduced by blending chemically distinct polymers\, as a design variable to ease this trade-off. Dual-pol ymer electrolytes of poly(lactic acid) (PLA) and poly(methyl methacrylate) (PMMA) with LiTFSI formed miscible\, single-phase matrices with higher io nic conductivity than single-polymer electrolytes\, and tuning polymer cha in length balanced ionic transport against mechanical integrity. Adding po ly(vinylidene fluoride) (PVDF) to form ternary blends produced more divers e Li+ coordination environments in experiments and molecular dynamics simu lations\, but only two of seven ternary compositions outperformed all sing le- and dual-polymer electrolytes\, indicating that compositional balance governs multifunctional performance. Preliminary five-polymer blends also show promising ionic conductivity\, but the composition space is too large to explore experimentally. The proposed work will develop physics-guided machine learning models\, trained on a consistently measured dataset and i nformed by molecular simulations\, to predict ionic conductivity and mecha nical properties and to guide composition selection. Together\, this work aims to establish how entropy and compositional balance can guide the desi gn of SPEs for structural energy storage.

ADVISOR(s): Dr. Caiwei Shen\, Department of Mechanical Engineering\, (cshen2@umassd.edu)

COMMITTEE MEMBERS:

Dr. Maricris Mayes\, Department of Chem istry and Biochemistry
Dr. Ruolin Zhou\, Department of Electrica l and Computer Engineering

NOTE: All EAS Stu dents are ENCOURAGED to attend.

Event page: https://www.umassd.edu/events/cms/11-9-26-eas-doctoral-proposal- defense--by-md-shovon-hossain.php

DTSTAMP:20261022T160142 DTSTART;TZID=America/New_York:20261109T110000 DTEND;TZID=America/New_York:20261109T130000 LOCATION:SENG 214 SUMMARY;LANGUAGE=en-us:EAS Doctoral Proposal Defense by Md Shovon Hossain UID:67f9719b3ba64c6ae9ed45804a2a102a@www.umassd.edu END:VEVENT END:VCALENDAR