Dataset Information

Storage of Information Using Small Organic Molecules.

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.

SUBMITTER: Cafferty BJ

PROVIDER: S-EPMC6535762 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Storage of Information Using Small Organic Molecules.

Cafferty Brian J BJ Ten Alexei S AS Fink Michael J MJ Morey Scott S Preston Daniel J DJ Mrksich Milan M Whitesides George M GM

ACS central science 20190501 5

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 funda ...[more]

PMID: 31139727

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Project description:When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties-specifically NMR chemical shifts in this manuscript-via the open source, high-performance computational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model. The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries.

Dataset Information

Storage of Information Using Small Organic Molecules.

Publications

Storage of Information Using Small Organic Molecules.

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