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  5. Storage of Information Using Small Organic Molecules

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Article
en
2019

Storage of Information Using Small Organic Molecules

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en
2019
Vol 5 (5)
Vol. 5
DOI: 10.1021/acscentsci.9b00210

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George M M Whitesides
George M M Whitesides

Harvard University

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Brian J. Cafferty
Alexei S. Ten
M. Fink
+4 more

Abstract

Although information is ubiquitous, and its technology arguably among the highest that humankind has produced, its very ubiquity has posed new types of problems. Three that involve storage of information (rather than computation) include its usage of energy, the robustness of stored information over long times, and its ability to resist corruption through tampering. The difficulty in solving these problems using present methods has stimulated interest in the possibilities available through fundamentally different strategies, including storage of information in molecules. Here we show that storage of information in mixtures of readily available, stable, low-molecular-weight molecules offers new approaches to this problem. This procedure uses a common, small set of molecules (here, 32 oligopeptides) to write binary information. It minimizes the time and difficulty of synthesis of new molecules. It also circumvents the challenges of encoding and reading messages in linear macromolecules. We have encoded, written, stored, and read a total of approximately 400 kilobits (both text and images), coded as mixtures of molecules, with greater than 99% recovery of information, written at an average rate of 8 bits/s, and read at a rate of 20 bits/s. This demonstration indicates that organic and analytical chemistry offer many new strategies and capabilities to problems in long-term, zero-energy, robust information storage.

How to cite this publication

Brian J. Cafferty, Alexei S. Ten, M. Fink, Scott Morey, Daniel J. Preston, Milan Mrksich, George M M Whitesides (2019). Storage of Information Using Small Organic Molecules. , 5(5), DOI: https://doi.org/10.1021/acscentsci.9b00210.

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Publication Details

Type

Article

Year

2019

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acscentsci.9b00210

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