• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Structure-Function Relationships and Advanced Data Analysis in Single Molecule Quantum Transport

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_etd_19202_sip1_m.pdf
    Size:
    17.63Mb
    Format:
    PDF
    Download
    Author
    Bamberger, Nathan
    Issue Date
    2021
    Keywords
    break junction
    conductance
    machine learning
    quantum transport
    single molecule transport
    Advisor
    Monti, Oliver L.A.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Incorporating individual small organic molecules into electronic circuits has the potential to enable smaller and more efficient devices, while also providing an excellent experimental platform for investigating the fundamental physics and chemistry of quantum transport. Key to advancing both of these goals is the continued development of structure-function relationships that predictively connect molecular design to observed transport behavior. Despite their apparent simplicity, single-molecule systems often display complex interactions between different physical effects, and so structure-function relationships that account for these interconnections are an especially important, and relatively understudied, need for the field. A second major challenge for single-molecule transport research is that modern experimental platforms tend to produce large, stochastic, and high-dimensional datasets. Methods to robustly extract meaningful information from such datasets are thus required to fully probe the range of behaviors occurring in single-molecule circuits, and to understand how those behaviors relate back to molecular design. In this dissertation, I describe contributions to help address the need for both nuanced structure-function relationships and sophisticated data analysis strategies for single-molecule quantum transport research. The experimental platform I used to measure single-molecule charge transport is described in detail, along with the type of data it collects and the subtleties of how those data are processed. Motivated by those details, I describe my overall approach to analyzing single-molecule data and then introduce, validate, and utilize novel machine learning algorithms that I developed to address specific challenges. These include a novel segment clustering algorithm for reliably extracting molecular features and an original correlation-based framework for identifying meaningful rare events. Using some of these new tools, I then report single-molecule conductance measurements for two series of molecules that reveal previously unknown connections between different physical effects in metal/single-molecule/metal junctions. The first study focuses on energy-level alignment between the bridging molecule and the metal electrodes, and finds that linked effects determine the tunability of conductance for molecules with varying chemical substituents. Finally, in the second study I demonstrate how backbone conformation and metal/molecule electronic coupling, which are often approximated as independent, can in fact be strongly correlated in the case of fairly common structural components. Together, all of these advances in the collection, analysis, and interpretation of single-molecule transport data help to deepen our understanding of physical chemistry in nanoscopic systems.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Chemistry
    Degree Grantor
    University of Arizona
    Collections
    Dissertations

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.