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Computational Materials Design for Molecular Machinery: From Nanoporous Crystals to Nanoscale Racecars

Sezginel, Kutay (2020) Computational Materials Design for Molecular Machinery: From Nanoporous Crystals to Nanoscale Racecars. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Over billions of years of evolution, Nature mastered molecular nanotechology, manipulating atoms and molecules with high precision. Among them are machines that perform tasks such as protein synthesis (ribosomes), gene replication (DNA and RNA polymerases), transporting molecular cargo (kinesin and dynein), and locomotion (flagella). Today these machines serve as a source of inspiration for the design of artificial molecular machines (AMMs). We are now exploring molecular motors, actuators, and logic gates at the nanoscale just as we did at the macroscale in the 19th century with electric motors and combustion engines.
Inspired by the pursuit of AMMs, this dissertation describes my research on developing computational methods to aid in the design of AMMs with targeted geometry and functionality. We first focused on the design of nanoporous crystals, developing a novel algorithm that can test whether two given crystalline structures can interpenetrate each other. Using this algorithm, we screened a database of ~6000 metal-organic frameworks (MOFs) and identified 18 hetero-interpenetrating MOF candidates. We then found that interpenetration enhances thermal conductivity which is important for various applications such as adsorbent gas storage.
Later, we developed tools to study nanoscale racecars, which are large organic molecules (~200-2000 Da) designed to diffuse quickly on atomically smooth surfaces. Here we developed both computational strategies to study their surface diffusion and tools to rapidly build hypothetical nanocars and assess their surface diffusion performance. We found that the surface diffusion gets slower with higher molecular weight and stronger molecule-surface interaction energy. We also suggested a geometric parameter, i.e. elevation weighted density, which we found to be useful for quickly ranking diffusion of different molecular designs. Our study suggests that by careful design of the molecular structure and selection of the appropriate surface, molecular diffusion can be tailored.
In summary, we show that by developing tools and using appropriate methods we can design and study properties of both static and dynamic molecular machines. We hope that these studies, and the tools developed, will collectively help to push the frontier of knowledge (even if incrementally) towards the eventual building of useful AMMs.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Sezginel, Kutaykbs37@pitt.edukbs37
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWilmer,
Committee MemberMpourmpakis,
Committee MemberFullerton,
Committee MemberHutchison,
Date: 14 January 2020
Defense Date: 10 December 2019
Approval Date: 30 July 2020
Submission Date: 27 March 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 148
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: molecular machines, nanocar, computational chemistry, molecular design, materials science
Date Deposited: 30 Jul 2020 18:45
Last Modified: 30 Jul 2020 18:45

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