Neural Networks Predicting Microbial Fuel Cells Output for Soft Robotics Applications

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as 'biohybrids', designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra musselDreissena polymorphaand water fleaDaphniaspp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Using AI and BES/MFC to decrease the prediction time of BOD5 measurement

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD5 is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD5 measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD5 measurements were carried out within 6-24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD5 sensor in real-life scenarios.

Customized Multichannel Measurement System for Microbial Fuel Cell Characterization

This work presents the development of an automatic and customized measuring system employing sigma-delta analog-to-digital converters and transimpedance amplifiers for precise measurements of voltage and current signals generated by microbial fuel cells (MFCs). The system can perform multi-step discharge protocols to accurately measure the power output of MFCs, and has been calibrated to ensure high precision and low noise measurements. One of the key features of the proposed measuring system is its ability to conduct long-term measurements with variable time steps. Moreover, it is portable and cost-effective, making it ideal for use in laboratories without sophisticated bench instrumentation. The system is expandable, ranging from 2 to 12 channels by adding dual-channel boards, which allows for testing of multiple MFCs simultaneously. The functionality of the system was tested using a six-channel setup, and the results demonstrated its ability to detect and distinguish current signals from different MFCs with varying output characteristics. The power measurements obtained using the system also allow for the determination of the output resistance of the MFCs being tested. Overall, the developed measuring system is a useful tool for characterizing the performance of MFCs, and can be helpful in the optimization and development of sustainable energy production technologies.

Self-Concern Across Scales: A Biologically Inspired Direction for Embodied Artificial Intelligence

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence.

A critical review on early-warning electrochemical system on microbial fuel cell-based biosensor for on-site water quality monitoring

The microbial fuel cell (MFC) sensor is a very promising self-powered self-sustainable system for early warning water quality detection. These sensors are cost-effective, biodegradable, compact in design, and portable in nature are favorable for real-time in situ water quality monitoring. This review represents the mechanism action behind the toxicity detection, optimization strategies, process parameters, role of biofilm, the role of external resistance, hydrodynamic study, and mathematical modeling for improving the performance of the sensor. Additionally, the techno-economic prospect of this MFC-based sensor and its challenges, limitations are addressed to make it economically more favorable for commercial use. The future direction is also explored based on the sensor's disadvantages and limitations. Comprehensively, this review covered all the possible directions of MFC sensor fabrication, their application, recent advancement, prospects challenges, and their possible solutions.

Recent progress in microbial fuel cells for industrial effluent treatment and energy generation: Fundamentals to scale-up application and challenges

Microbial fuel cells (MFCs) technology have the potential to decarbonize electricity generation and offer an eco-friendly route for treating a wide range of industrial effluents from power generation, petrochemical, tannery, brewery, dairy, textile, pulp/paper industries, and agro-industries. Despite successful laboratory-scale studies, several obstacles limit the MFC technology for real-world applications. This review article aimed to discuss the most recent state-of-the-art information on MFC architecture, design, components, electrode materials, and anodic exoelectrogens to enhance MFC performance and reduce cost. In addition, the article comprehensively reviewed the industrial effluent characteristics, integrating conventional technologies with MFCs for advanced resource recycling with a particular focus on the simultaneous bioelectricity generation and treatment of various industrial effluents. Finally, the article discussed the challenges, opportunities, and future perspectives for the large-scale applications of MFCs for sustainable industrial effluent management and energy recovery.

Leveraging artificial intelligence in bioelectrochemical systems

Bioelectrochemical systems (BESs) are highly evolved and sophisticated systems that produce bioenergy via exoelectrogenic microbes. Artificial intelligence (AI) helps to understand, relate, model, and predict both process parameters and microbial diversity, resulting in higher performance. This approach has revolutionized BESs through highly advanced computational algorithms that best suit the systems' architecture.