Real-Time Electrochemical Assessment of Soil Health Through EAB Formation and the Development of a Stimulus-Responsive Remediation of Microbial Soild Degradation
Abstract:
The objective of this study is to establish a real-time bio electrochemical assessment of microbial soil health through EAB formation, due to the flaws of conventional soil health evaluation methods in the status quo. This study employs a three-electrode system to run CA and CV trials in the soil environment in healthy and unhealthy soil categories. In the healthy soil category, the trials have effectively validated that the CA and CV trials were able to effectively represent the soil’s health in real-time, while EAB formation and maturation synergistically enhanced soil health microbiologically through confirmation from CA and CV trials. In contrast, the unhealthy soil category which was simulated by killing the microorganisms through applying extreme heat via vertical pressure steam sterilizer, has dis played a weak EAB formation as well as real-time soil health, which was also confirmed through CA and CV trials. However, EAB was not utilized effectively as the soil barely had any microorganisms available to form communities which could lead to EAB formation. As a result, this research proposes an engineering extension to leverage the f indings and add supplemental technologies in pursuit of not only assessing microbial soil health real time but also actively remediating soil. To achieve such agenda, the engineering extension involved developing an inoculation sys tem involving electrothermal wax cartridge and alginate beads enclosed with biodegradable outer mesh, to release micronutrients and microbial consortia based on decisions from AI trained using the CA and CV trials of this study. Furthermore, a sample website was built to run sample CA and CV trials, where users could upload their own CA or CV trials to receive analysis/recommendation from the trained AI model, and receive recommendations on further actions on the CA and CV trial simulation from the trained AI model. Overall, this study brings value to the realms of this research as it enables in-situ, non-destructive methods of soil biosensing as well as soil remediation, without affecting soil acidification issues, but enhancing carbon sequestration capacities of the soil. Therefore, when plants are planted in healthy soil, the aforementioned factors will indirectly enable the optimal efficiency in photosynthesis, contributing to climate change mitigation. A potential limitation to tackle is implementing the unpredictable nature of soil into this bio electrochemical approach, in order to optimize it for a more effective real-world application.
