An Energy-Autonomous Chemical Oxygen Demand Sensor Using a Microbial Fuel Cell and Embedded Machine Learning

The current methods of water quality monitoring tend to be costly, labor-intensive, and off-site. Also, they are not energetically sustainable and often require environmentally damaging power sources such as batteries. Microbial fuel cell (MFC) technology is a promising sustainable alternative to combat these issues due to its low cost, eco-friendly energy generation, and bio-sensing features. Extensive work has been done on using MFCs as bio-sensors or sources of power separately. However, little work has been done toward using MFCs for both applications at the same time. Additionally, previous studies using MFCs for water quality measurement have been mostly limited to laboratory conditions due to the biochemical complexity of the real-world. Another limitation of MFCs is how little power they can generate, requiring the MFC-based systems to have minimal power consumption. This work addresses these challenges and presents an energy-autonomous water quality sensing unit that uses a single MFC both as its sensory input and the sole source of power for computing the chemical oxygen demand (COD). In the proposed unit, geometric features of the voltage profile of the MFC (e.g., peak heights) are used as the inputs to a machine learning algorithm (support vector regression (SVR)). The electrical power generated by the MFC is used to drive a low-power microcontroller which logs the MFC voltage and runs the machine learning algorithm. Experimental evaluation showed that the device is capable of detecting the COD of natural pond water samples accurately (coefficient of determination (R 2 )=0.94). This work is the first demonstration of energy autonomy in an MFC-based sensing unit for measuring water quality and represents a step forward in the development of energy-autonomous sensors for environmental monitoring applications

Cast and 3D printed ion exchange membranes for monolithic microbial fuel cell fabrication

© 2015 Elsevier B.V. All rights reserved. We present novel solutions to a key challenge in microbial fuel cell (MFC) technology; greater power density through increased relative surface area of the ion exchange membrane that separates the anode and cathode electrodes. The first use of a 3D printed polymer and a cast latex membrane are compared to a conventionally used cation exchange membrane. These new techniques significantly expand the geometric versatility available to ion exchange membranes in MFCs, which may be instrumental in answering challenges in the design of MFCs including miniaturisation, cost and ease of fabrication. Under electrical load conditions selected for optimal power transfer, peak power production (mean 10 batch feeds) was 11.39 μW (CEM), 10.51 μW (latex) and 0.92 μW (Tangoplus). Change in conductivity and pH of anolyte were correlated with MFC power production. Digital and environmental scanning electron microscopy show structural changes to and biological precipitation on membrane materials following long term use in an MFC. The cost of the novel membranes was lower than the conventional CEM. The efficacy of two novel membranes for ion exchange indicates that further characterisation of these materials and their fabrication techniques, shows great potential to significantly increase the range and type of MFCs that can be produced

Neural networks predicting microbial fuel cells output for soft robotics applications

The development of biodegradable soft robotics requires an appropriate eco-friendly source of energy. The use of Microbial Fuel Cells (MFCs) is suggested as they can be designed completely from soft materials with little or no negative effects to the environment. Nonetheless, their responsiveness and functionality is not strictly defined as in other conventional technologies, i.e. lithium batteries. Consequently, the use of artificial intelligence methods in their control techniques is highly recommended. The use of neural networks, namely a nonlinear autoregressive network with exogenous inputs was employed to predict the electrical output of an MFC, given its previous outputs and feeding volumes. Thus, predicting MFC outputs as a time series, enables accurate determination of feeding intervals and quantities required for sustenance that can be incorporated in the behavioural repertoire of a soft robot

Long Term Feasibility Study of In-field Floating Microbial Fuel Cells for Monitoring Anoxic Wastewater and Energy Harvesting

© Copyright © 2019 Cristiani, Gajda, Greenman, Pizza, Bonelli and Ieropoulos. In the present work different prototypes of floating MFCs have been tested in anoxic water environments of wastewater plants in Italy, over a period of 3 years. Several configurations of horizontal (flat) and vertical (tubular) MFCs were assembled, using low-cost and light-weight materials, such as plastic lunch boxes, polystyrene or wood to keep the systems afloat, and ceramics for the MFCs. Untreated carbon cloth or veil was used for both anode and cathode electrodes. Felt (flat MFCs) or clay (tubular MFCs) was used as the cation-exchange separator. Single flat MFCs generated power up to 12 mW/m2 while a 32 cylindrical MFC stack generated up to 18 mW/m2. The testing lasted for more than 2 years and there was no inoculation other than exposing the MFCs to the denitrification environment. The cathodes of the flat MFCs were spontaneously colonized by algae and plants, and this did not affect the stability of the systems. Natural light increased the power output of the flat MFCs which were smaller than 50 × 50 cm. Diurnal oscillation of temperature and periodic water flow did not significantly affect the performance of the MFCs. The largest flat MFC produced the highest absolute power, although in a disrupted way. A new, simple low-energy remote monitoring system, based on LoRa technology was used for data transmission over distances of >500 m. This is a piece of hardware that could potentially be suitable for remote monitoring as part of a network, as it can be directly powered by the deployed MFCs

3D printed components of microbial fuel cells: Towards monolithic microbial fuel cell fabrication using additive layer manufacturing

© 2016 The Authors For practical applications of the MFC technology, the design as well as the processes of manufacturing and assembly, should be optimised for the specific target use. Another burgeoning technology, additive manufacturing (3D printing), can contribute significantly to this approach by offering a high degree of design freedom. In this study, we investigated the use of commercially available 3D printable polymer materials as the MFC membrane and anode. The best performing membrane material, Gel-Lay, produced a maximum power of 240 ± 11 μW, which was 1.4-fold higher than the control CEM with PMAX of 177 ± 29 μW. Peak power values of Gel-Lay (133.8–184.6 μW) during fed-batch cycles were also higher than the control (133.4–160.5 μW). In terms of material cost, the tested membranes were slightly higher than the control CEM, primarily due to the small purchased quantity. Finally, the first 3D printable polymer anode, a conductive PLA material, showed significant potential as a low-cost and easy to fabricate MFC anode, producing a stable level of power output, despite poor conductivity and relatively small surface area per unit volume. These results demonstrate the practicality of monolithic MFC fabrication with individually optimised components at relatively low cost

Toward energy Autonomy in heterogeneous Modular Plant-Inspired Robots through Artificial evolution

Contemporary robots perform energy intensive tasks—e.g., manipulation and locomotion—making the development of energy autonomous robots challenging. Since plants are primary energy producers in natural ecosystems, we took plants as a source of inspiration for designing our robotics platform. This led us to investigate energy autonomy in robots through employing solar panels. As plants move slowly compared to other large terrestrial organisms, it is expected that plant-inspired robots can enable robotic applications, such as long-term monitoring and exploration, where energy consumption could be minimized. Since it is difficult to manually design robotic systems that adhere to full energy autonomy, we utilize evolutionary algorithms to automate the design and evaluation of energy harvesting robots. We demonstrate how artificial evolution can lead to the design and control of a modular plant-like robot. Robotic phenotypes were acquired through implementing an evolutionary algorithm, a generative encoding and modular building blocks in a simulation environment. The generative encoding is based on a context sensitive Lindenmayer-System (L-System) and the evolutionary algorithm is used to optimize compositions of heterogeneous modular building blocks in the simulation environment. Phenotypes that evolved from the simulation environment are in turn transferred to a physical robot platform. The robotics platform consists of five different types of modules: (1) a base module, (2) a cube module, (3) servo modules, and (4,5) two types of solar panel modules that are used to harvest energy. The control system for the platform is initially evolved in the simulation environment and afterward transferred to an actual physical robot. A few experiments were done showing the relationship between energy cost and the amount of light tracking that evolved in the simulation. The reconfigurable modular robots are eventually used to harvest light with the possibility to be reconfigured based on the needs of the designer, the type of usable modules, and/or the optimal configuration derived from the simulation environment. Long-term energy autonomy has not been tested in this robotics platform. However, we think our robotics platform can serve as a stepping stone toward full energy autonomy in modular robots

Additive manufacturing: unlocking the evolution of energy materials

The global energy infrastructure is undergoing a drastic transformation towards renewable energy, posing huge challenges on the energy materials research, development and manufacturing. Additive manufacturing has shown its promise to change the way how future energy system can be designed and delivered. It offers capability in manufacturing complex 3D structures, with near-complete design freedom and high sustainability due to minimal use of materials and toxic chemicals. Recent literatures have reported that additive manufacturing could unlock the evolution of energy materials and chemistries with unprecedented performance in the way that could never be achieved by conventional manufacturing techniques. This comprehensive review will fill the gap in communicating on recent breakthroughs in additive manufacturing for energy material and device applications. It will underpin the discoveries on what 3D functional energy structures can be created without design constraints, which bespoke energy materials could be additively manufactured with customised solutions, and how the additively manufactured devices could be integrated into energy systems. This review will also highlight emerging and important applications in energy additive manufacturing, including fuel cells, batteries, hydrogen, solar cell as well as carbon capture and storage

Microbial fuel cells: From fundamentals to applications. A review

© 2017 The Author(s) In the past 10–15 years, the microbial fuel cell (MFC) technology has captured the attention of the scientific community for the possibility of transforming organic waste directly into electricity through microbially catalyzed anodic, and microbial/enzymatic/abiotic cathodic electrochemical reactions. In this review, several aspects of the technology are considered. Firstly, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Secondly, the development of the concept of microbial fuel cell into a wider range of derivative technologies, called bioelectrochemical systems, is described introducing briefly microbial electrolysis cells, microbial desalination cells and microbial electrosynthesis cells. The focus is then shifted to electroactive biofilms and electron transfer mechanisms involved with solid electrodes. Carbonaceous and metallic anode materials are then introduced, followed by an explanation of the electro catalysis of the oxygen reduction reaction and its behavior in neutral media, from recent studies. Cathode catalysts based on carbonaceous, platinum-group metal and platinum-group-metal-free materials are presented, along with membrane materials with a view to future directions. Finally, microbial fuel cell practical implementation, through the utilization of energy output for practical applications, is described

Haptic vs. Visual Neurofeedback for Brain Training: A Proof-of-Concept Study

The current practice of administering neurofeedback using the patients' visual and/or auditory channel(s) is known to cause fatigue, excessive boredom, and restricted mobility during prolonged therapy sessions. This paper proposes haptics as an alternative means to provide neurofeedback and investigates its effectiveness by conducting two user studies (Study- I & II) using a novel compact wearable haptic device that provides vibrotactile feedback to the user's neck. Each user study has three neurofeedback modes: visual-only, haptics-only, and visual-and-haptics combined. Study- I examines the participant's performance in a brain-training task by measuring their attention level (AL) and the task completion time (CT). Study- II, in addition to the brain-training task, investigates the participants' ability to perform a secondary task (playing a shape-sorting game) while receiving the neurofeedback. Results show that users performed similarly well in brain-training with haptics-only and visual-only feedback. However, when engaged in a secondary task, the users performed significantly better (AL and CT improved around 11% and 17%, respectively) with haptics, indicating a clear advantage of haptics over visual neurofeedback. Being able to perform routine activities during brain-training would likely increase user adherence to longer therapy sessions. In the future, we plan to verify these findings by conducting experiments on ADHD-patients