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Electrochemically and Bioelectrochemically Induced Ammonium Recovery
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Grape Must as a Bioelectrochemical Processor.

Panagiotis Mougkogiannis1, Andrew Adamatzky1

  • 1Unconventional Computing Laboratory, University of the West of England, Bristol BS16 1QY, U.K.

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|March 9, 2026
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Summary
This summary is machine-generated.

Grape must fermentation exhibits spontaneous voltage oscillations, behaving like a self-organizing bioelectrochemical processor. These complex patterns, influenced by temperature, demonstrate memory effects and distributed computation, impacting fermentation monitoring.

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Area of Science:

  • Bioelectrochemistry
  • Fermentation Science
  • Complex Systems

Background:

  • Traditional food fermentation systems, like grape must (mustalevria), are complex biological processes.
  • Understanding the underlying bioelectrochemical dynamics is crucial for process optimization and control.
  • Spontaneous voltage oscillations have been observed but not fully characterized in these systems.

Purpose of the Study:

  • To investigate spontaneous voltage oscillations in grape must fermentation.
  • To analyze the spatio-temporal patterns and computational properties of these oscillations.
  • To correlate bioelectrochemical activity with environmental factors and understand memory effects.

Main Methods:

  • Utilized multichannel differential electrode arrays with platinum-iridium (Pt/Ir) electrodes to track bioelectrochemical changes over 200,000 seconds.
  • Applied power spectral density analysis, environmental correlation analysis, binary state analysis, principal component analysis, and mutual information calculations.
  • Monitored voltage oscillations and their frequencies, spectral slopes, and entropy.

Main Results:

  • Observed complex spatio-temporal voltage oscillations with frequencies from 0.00044 to 0.00215 Hz, exhibiting brown noise characteristics (spectral slopes -2.01 to -3.28).
  • Identified temperature as a primary modulator (r = 0.245-0.558) and humidity as a negative correlator (-0.052 to -0.245).
  • Demonstrated that the system operates with natural Boolean logic (XOR gates with high entropy) and exhibits significant temporal asynchrony and uneven metabolic activity.

Conclusions:

  • Grape must fermentation systems function as self-organizing bioelectrochemical processors capable of distributed computation.
  • Brown noise scaling and memory effects are inherent, suggesting short-term measurements may not predict long-term fermentation behavior.
  • These systems serve as valuable models for studying computational properties in biological electrochemical systems.