Chemical Data Encoding for High-Density Storage
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Solution Overview
Problem
Current data storage technologies face limitations in achieving high information density and parallel speedy memory interrogation, particularly in molecular systems, where existing methods for reading biomolecular information are cumbersome and inefficient.
Innovation Solution
The development of a method for computing with chemicals, where abstract digital data is encoded into liquid volumes of chemicals, translated into a chemical form, and read using spectrometry or spectroscopy, allowing for the analysis of chemical structure and concentration to develop a chemical computational language, enabling efficient data storage and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If DNA is used for long-term archival information storage, then information density is improved (achieving about 214 petabytes per gram), but reading information becomes cumbersome and inefficient
Solution Approach 1:
The patent replaces mechanical/sequential reading methods (PCR, shotgun sequencing, nanopores) with a chemical field-based parallel reading system using spectrometry and spectroscopy. This substitution enables simultaneous interrogation of multiple chemical data points, dramatically improving reading efficiency while maintaining the high information density of molecular storage systems.
Solution Approach 2:
The patent implements periodic action through the use of spectrometric and spectroscopic analysis cycles that can rapidly interrogate chemical data stored in metabolites. This periodic chemical analysis approach allows for efficient, repeated reading of the same data without degrading the storage medium, enabling both high density and high productivity.
2Measurement precision
If sequential methods are used for reading biomolecular information, then measurement accuracy is maintained, but processing speed deteriorates
Solution Approach 1:
The patent transitions from one-dimensional sequential reading to multi-dimensional parallel reading by encoding data across multiple chemical dimensions (different metabolites, concentrations, and spectral characteristics). This dimensional expansion allows simultaneous measurement of multiple data points through spectrometry and spectroscopy, achieving both high precision and high speed.
Solution Approach 2:
The patent segments the reading process into multiple independent chemical channels, where different metabolites and their spectral signatures serve as separate measurement pathways. This segmentation enables parallel processing of multiple data points simultaneously, dramatically increasing processing speed while maintaining measurement precision through the redundancy of multiple reading channels.
3Ease of operation
If conventional semiconductor technologies are used, then ease of operation is maintained, but information density deteriorates compared to molecular storage
Solution Approach 1:
The patent introduces chemical intermediaries (metabolites and their spectral signatures) that bridge the gap between simple digital data and high-density molecular storage. These chemical intermediaries encode multiple bits of information through their spectral characteristics, enabling high information density while maintaining ease of operation through standardized spectrometric and spectroscopic reading procedures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables high-density data storage and parallel data interrogation, achieving >99% accuracy in kilobyte-scale image data sets and demonstrating the potential for gigabyte-scale data storage with increased storage density and energy efficiency.
Implementation Method 1
reading the data set using spectrometry, spectroscopy, or both analytical methods
Implementation Method 2
reading the data set using spectrometry, spectroscopy, or both analytical methods
Data Source
AI summary
The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.