Advancing High-Throughput Molecular Information Systems

Arcadia, Christopher E.

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Overview

Year:: 2020
Contributor:: Arcadia, Christopher E (creator); Rosenstein, Jacob (Advisor); Reda, Sherief (Reader); Stein, Derek (Reader); Brown University. Engineering: Electrical Sciences and Computer Engineering (sponsor)
Genre:: theses
Subject:: Machine Learning; Mass spectrometry; Nanopores; Multicomponent Reactions; Chemical Computing; Molecular Memory; Single Molecule Sensing; Meniscus Contact; Low Noise Amplifier; Automation; High Throughput Screening; combinatorial chemistry; information storage; Scanning Probe
Extent:: xv, 150 p.

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Description

Abstract:: While information systems are often associated with microelectronic devices, every physical process can be interpreted as an exchange of information. With the slowing of Moore's law, engineers and scientists are scrambling to come up with creative solutions that further push the boundaries of data storage and computing devices. To scale beyond the limits of silicon, perhaps we can exploit molecular systems, which are ubiquitous and yet comparatively unexplored for information processing. Genomic technologies have been advancing at an exponential pace, which has inspired visions of molecular data systems that complement traditional ones with new opportunities to store and process information at dramatically higher densities, using less energy, and potentially lasting for millennia. In this thesis, we demonstrate several distinct uses of chemical systems to acquire, represent, and operate on information. An effective molecular data platform will demand high-performance chemical detection, so we first present a novel technique for nanopore sensing. The instrument enables hundreds of solid-state nanopores to be fabricated and experimentally measured per day. Additionally, thanks to micron-scale fluid contacts, we achieve unprecedentedly low parasitic capacitances, which can support high speed electrochemical measurements of single molecules. Second, we propose a general framework for digital information storage in molecular mixtures. By utilizing multicomponent chemical reactions to rapidly produce thousands of unique small molecules, we demonstrate that this format is capable of holding significant amounts of data. By combining high resolution mass spectrometry with contemporary machine learning algorithms, we show that the original data can be recovered with excellent fidelity. Finally, we extend the concept of small-molecule memory with a method of classifying chemically-encoded data, by mapping a single-layer neural network onto a series of of liquid transfer and pooling operations. Because multiple compounds can co-exist in a single fluid volume, any operation performed on a mixture will apply to all its chemical constituents. With a sufficiently large chemical library, this arrangement can provide a highly efficient means of parallel data processing. Each of these demonstrations were made possible with a combination of sensitive instrumentation, precise liquid handling, robotic automation, and flexible chemistry. These interdisciplinary tools for high throughput experimentation will be critical for the further advancement of molecular information systems.
Notes:: Thesis (Ph. D.)--Brown University, 2020

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Rights: In Copyright
Restrictions on Use: Collection is open for research.

Citation

Arcadia, Christopher E., "Advancing High-Throughput Molecular Information Systems" (2020). Electrical Sciences and Computer Engineering Theses and Dissertations. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:1129443/

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Electrical Sciences and Computer Engineering Theses and Dissertations

Theses and Dissertations for the Electrical Sciences and Computer Engineering department.
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