Optical Recognition of the English Alphabet Using Proteinoids.

We introduce a new abiotic-protein-based substrate for identifying English alphabet characters optically using proteinoids. Proteinoids, which are amino acid polymers produced under thermal stress conditions, have demonstrated promise as materials that are compatible with living organisms and can be used in a wide range of applications. We explore the potential of using proteinoids for the optical stimulation and detection of English alphabet characters. We performed experiments to quantify the potential and period statistics of proteinoids under optical stimulation corresponding to individual alphabet characters. For each character, we recorded the potential statistics, which included amplitude quartiles, mean, maximum, minimum, and standard deviation. Additionally, the statistical measures of the period, including quartiles, mean, maximum, minimum, and standard deviation, were also recorded. The data gathered unveil unique patterns and features for each alphabet character. The potential and period statistics display fluctuations that can be used for character identification. Characters such as 'D', 'H', and 'L' exhibit greater amplitude means in comparison to others, showing their distinct response to optical stimulation. The period data also reveal variations among characters, with certain characters exhibiting durations that are longer or shorter than others. Our research indicates that proteinoids have the potential to be highly effective unconventional materials for accurately identifying English alphabet symbols using light. Through the analysis of potential and period statistics, we may create recognition algorithms capable of distinguishing characters by their optical response. This technique introduces novel opportunities for biocompatible systems that can recognize characters, and it has promise for applications in diverse sectors, including biocomputing and biosensing. Further research is needed to optimize the proteinoid synthesis process, refine the optical stimulation setup, and create reliable recognition algorithms. The data presented serve as a basis for future research in the domain of unconventional computing, especially on abiotic-protein-based computing devices.