Analog Computers and the Future of Soft Matter Fluidic Systems
The recent work presented in this article introduces a novel approach to building analog computers using soft matter fluidic systems. The researchers aim to invent a new information-theoretic language by creating two-dimensional Quick Response (QR) codes as a digital representation of the analog signals exhibited by proteinoids.
To capture these analog signals, two different experimental techniques are employed: a voltage-sensitive dye and a pair of differential electrodes. These techniques allow the researchers to record and sample the analog signals, which are then transformed into binary representations. The researchers go a step further by demonstrating the representation of key logic gates (such as AND, OR, NOT, XOR, NOR, NAND, and XNOR) using the digitally-sampled proteinoid signals.
Building on this foundation of binary representation, additional encoding schemes convert the binary code into two-dimensional QR codes. As a result, each QR code becomes a unique digital marker for a specific proteinoid network. Remarkably, the researchers establish that these QR codes can be scanned using a mobile phone to retrieve the original analog signal, effectively translating the digital representation back into its analog counterpart.
This groundbreaking work goes beyond the practical applications of QR codes with proteinoids. It unveils the concept of a fundamental information-theoretic language specific to soft matter fluidic systems, expanding the possibilities for their internal information transmission capabilities. By digitally encoding the internal properties of these systems using QR codes, a universal and accessible language is created, allowing a wider audience to harness their intricate workings.
Furthermore, this study demonstrates a significant advancement in approximating the continuum properties of soft matter fluids. By using a series representation of logic gates and QR codes, the researchers take a step closer to programming these fluids through information-theoretic methods. This approach aligns with Tao’s fluid program, proposed almost a decade ago, which aimed to program fluids using information theory.
The implications of this research are vast. The ability to digitally encode and decode analog signals from soft matter fluidic systems opens doors to a myriad of applications in various fields such as biotechnology, medicine, and data storage. By understanding the information-theoretic language of these systems and developing methodologies to program them, we may unlock new avenues for advanced computing and engineering.