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Area of Science:

  • Bioelectrochemistry
  • Renewable Energy Technologies
  • Biosensing

Background:

  • Living biophotovoltaic (LBPV) systems offer a sustainable approach to energy generation and environmental monitoring.
  • Integrating cyanobacteria with modified electrodes enhances photoelectrochemical performance.
  • Accurate modeling of LBPV dynamics is crucial for optimizing their application.

Purpose of the Study:

  • To develop and optimize a cyanobacteria-based LBPV system for simultaneous electricity generation and herbicide detection.
  • To employ deep learning models for forecasting the chronoamperometric photocurrent dynamics of the LBPV system.
  • To evaluate the sensitivity, stability, and selectivity of the LBPV system as a biosensor.

Main Methods:

  • Fabrication of a photoanode using electropolymerized dithieno[3,2-b:2',3'-d] pyrrole derivatives and gold nanoparticle-modified electrodes.
  • Optimization of electrode parameters and cyanobacterial concentration for maximal photocurrent.
  • Application of deep learning architectures (LSTM, BiLSTM, GRU) for photocurrent prediction.
  • Experimental validation of the LBPV system's performance in terms of energy generation and herbicide sensing.

Main Results:

  • The optimized LBPV system demonstrated stable photocurrent generation under simulated sunlight and maintained activity for 50 days.
  • The BiLSTM-SGDM deep learning model achieved high accuracy (R² = 0.92) in predicting photocurrent dynamics.
  • The LBPV system exhibited high sensitivity for detecting diuron (1.12 nM) and linuron (9.70 nM) with excellent selectivity.

Conclusions:

  • The developed LBPV system effectively integrates green energy harvesting with sensitive photoelectrochemical biosensing of herbicides.
  • AI-based photocurrent forecasting provides a powerful tool for understanding and optimizing complex biohybrid systems.
  • This dual-function LBPV technology presents a promising framework for sustainable energy solutions and advanced environmental monitoring.