Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

ABSTRACT This paper describes the development of a bioinspired composite material capable of audio classification applications. Hydrogel matrices produced by microorganisms combined with synthetic biology elements, allow for the development of adaptable bioelectronics that connect biology and technology in a customized way. In this study, a composite population of kombucha, chlorella, and proteinoids (thermal proteins) is utilized to respond to acoustic signals converted to electrical waveforms. The kombucha zoogleal mats, which are made and populated by over 60 species of yeasts and bacteria, offer a matrix at the micro level that is connected to the photosynthetic microalgae chlorella. Proteinoids formed through thermal condensation exhibit unique patterns of signaling kinetics. This living material has the ability to be electrically stimulated and can process signals in a way feasible for sensory applications. Using English alphabet audio inputs, a systematic analysis demonstrates the capability to differentiate audio waveforms based solely on biological composite responses. The use of spectral analysis allows for the identification of specific spike timing patterns that encode unique characteristics of individual letters. Moreover, network disturbances result in specific changes in output, so validating the ability to adjust waveform classification. The study demonstrates that kombucha–chlorella–proteinoid composites provide a durable and versatile bioelectronic platform for immediate auditory processing. The work represents progress toward the development of bioelectronic systems that can be customized based on the principles of biological sensory processing, cognition, and adaptation.

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Publication Details

Journal
Nano Select
Published
2024-08-12
DOI
https://doi.org/10.1002/nano.202400080
Citations
7
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
4.64

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article

Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

Anna E. Nikolaidou, Andrew Adamatzky, Panagiotis Mougkogiannis
7 citations
Nano Select
Cell Image Analysis Techniques
4.64
article

Kombucha–Chlorella–Proteinoid Biosynthetic Classifiers of Audio Signals

Anna E. Nikolaidou, Andrew Adamatzky, Panagiotis Mougkogiannis
article en
7 citations

Abstract

ABSTRACT This paper describes the development of a bioinspired composite material capable of audio classification applications. Hydrogel matrices produced by microorganisms combined with synthetic biology elements, allow for the development of adaptable bioelectronics that connect biology and technology in a customized way. In this study, a composite population of kombucha, chlorella, and proteinoids (thermal proteins) is utilized to respond to acoustic signals converted to electrical waveforms. The kombucha zoogleal mats, which are made and populated by over 60 species of yeasts and bacteria, offer a matrix at the micro level that is connected to the photosynthetic microalgae chlorella. Proteinoids formed through thermal condensation exhibit unique patterns of signaling kinetics. This living material has the ability to be electrically stimulated and can process signals in a way feasible for sensory applications. Using English alphabet audio inputs, a systematic analysis demonstrates the capability to differentiate audio waveforms based solely on biological composite responses. The use of spectral analysis allows for the identification of specific spike timing patterns that encode unique characteristics of individual letters. Moreover, network disturbances result in specific changes in output, so validating the ability to adjust waveform classification. The study demonstrates that kombucha–chlorella–proteinoid composites provide a durable and versatile bioelectronic platform for immediate auditory processing. The work represents progress toward the development of bioelectronic systems that can be customized based on the principles of biological sensory processing, cognition, and adaptation.

Nano SelectVol. 6(2)
University of the West of England (GB)
Engineering and Physical Sciences Research Council
Quality Education
Openalex Percentile: Top 6%
Cell Image Analysis Techniques
4.64
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