C. Ngaw, Cui-e Zhao, V. B. Wang et al.

Sustainable Energy & Fuels • 2017

Poor extracellular electron transfer (EET) between microorganisms and the anode in microbial fuel cells (MFCs) has been the limiting factor hindering the widespread application of this technology. Thus, the employment of high performance anodes with efficient EET is crucial in enhancing the overall performance of MFCs. This work exploits the effect of MFC anodes with superior EET on significantly improving solar microbial hybrid technology formed by coupling a high performance MFC with a photoelectrochemical (PEC) cell. Based on the results, the hybrid device, which comprises a graphene/carbon nanotube (G/CNT) based MFC and a gold/titanium dioxide (Au/TiO2) PEC cell, generated a photocurrent density of ∼0.758 mA cm−2 and a H2 evolution rate of ∼11.2 μmol h−1. This performance is ∼2.5 times higher than that of an unmodified carbon bio-anode (∼0.294 mA cm−2, ∼4.6 μmol h−1) under 1 sun illumination (100 mW cm−2) at zero bias (0 V vs. Pt). The enhancement is attributed to the improved EET of the G/CNT biofilm due to two factors: (1) the large surface area of graphene sheets enables more bacterial cells to adhere onto the surface of the anode, and (2) the incorporation of CNTs into the G-biofilm also improves the conductivity of the biofilm, facilitating direct electron transfer between Shewanella oneidensis and the electrode. This successful demonstration points towards the possibility of further enhancing the H2 performance of hybrid devices through the employment of MFC anodes with higher EET.

G. Bhowmick, E. Kibena‐Põldsepp, L. Matisen et al.

Sustainable Energy & Fuels • 2019

Metal phthalocyanine (CoPc and FePc) modified MWCNT or CDC materials were explored as superior cathode catalysts for MFC technology.

Xue Ting, Qin Xian-sheng, Zhang Shunqi et al.

Journal of Mechanical Engineering • 2020

: Vibration suppression has been a problem needs to be overcome for large-scale flexible structures in the aerospace environment . High elastic modulus, low density advantages of carbon nanotube (CNT) and large actuation forces and flexibility of micro-fiber composite (MFC) make the CNT gradient-reinforced fiber piezoelectric smart structure particularly suitable for aerospace high drop temperature environment.Based on the first order shear deformation and large rotation theory with six parameters, the nonlinear finite element modeling and analysis of the carbon nanotube-reinforced gradient composite plate with MFCs is meaningful.The geometrically nonlinear finite element model is developed for two different kinds of MFC, namely MFC-d31 and MFC-d33.First, the accuracy of the model is validated by experiment results of a reinforcement direction, different control modes make a big difference in the torsion control of the plate and shell. The simulation analysis of piezoelectric smart thin-shell structure based on large rotation theory with six parameters has great application value for shape control and vibration suppression of large flexible components in the aerospace field.

A. O. Sirajudeen, M. Annuar, S. Ibrahim

Chemical Engineering Communications • 2025

Abstract The performance of a microbial fuel cell (MFC) was investigated using a carbon nanotube (CNT)-composited blend of medium-chain-length polyhydroxyalkanoates-co-polycaprolactone (PHA-co-PCL) as a surface coating on the anode electrode. A 1:1 (w/w) blend of PHA and PCL resulted in a material with enhanced thermal properties and efficient bacterial colonization on the anode surface. When composited with CNTs, this blend significantly improved electrochemical performance and reduced overall internal resistance by 81%. The blend composite anode demonstrated superior longevity and stability in generating maximum voltage, outperforming the control anode. The power and current densities, measured at 354 mW/m2 and 768 mA/m2 respectively, were 3.2 and 1.8 times higher than the control anode. Additionally, the blend composite resisted (bio)degradation even after 60 days of MFC operation.

Sophia S. Y. Chan, Shao‐Xiang Go, M. P. Meivita et al.

Materials Advances • 2022

A combined alternating current pulse carbon nanotube platform that can enhance cancer cell-specific thermal ablation via Joule heating.

Yuyang Wang, Guangxu Hu, Dayu Zheng et al.

Coatings • 2023

Microbial fuel cell (MFC) technology can potentially recover bioelectricity from wastewater. However, its practical applications have been limited because of its low power density and since the energy generated from an MFC cannot be stored. In this study, manganese dioxide (MnO2) coupled with carbon nanotubes (CNT) was chosen to in situ modify carbon felt (CF) as a capacitive bioanode (CF/CNT/MnO2) to improve the power generation and energy storage of MFCs. The maximum power density of the MFC with the MnO2-CNT-modified bioanode reached 3471.6 mW m3, which was 1.96 times higher than that of the CF/CNT anode (1772.6 mW m−3). During the experiment of charging for 30 min and discharging for 30 min, the MFC with a capacitive bioanode had a total charge of 8777.1 C m−2, 2.74 times higher than that of the CF/CNT anode. The excellent electricity-producing and energy storage performance of the MFC equipped with the CF/CNT/MnO2 anode is attributed to the composite materials, which can be due to their better biocompatibility, large capacitance, and high specific surface area. This study provides a new way to improve the performance of electricity generation and energy storage of MFCs.

Tiago Amarante, T. H. R. Cunha, Claudio Laudares et al.

Beilstein Journal of Nanotechnology • 2023

In this work, a conductive ink based on microfibrillated cellulose (MFC) and multiwalled carbon nanotubes (MWCNTs) was used to produce transducers for rapid liquid identification. The transducers are simple resistive devices that can be easily fabricated by scalable printing techniques. We monitored the electrical response due to the interaction between a given liquid with the carbon nanotube–cellulose film over time. Using principal component analysis of the electrical response, we were able to extract robust data to differentiate between the liquids. We show that the proposed liquid sensor can classify different liquids, including organic solvents (acetone, chloroform, and different alcohols) and is also able to differentiate low concentrations of glycerin in water (10–100 ppm). We have also investigated the influence of two important properties of the liquids, namely dielectric constant and vapor pressure, on the transduction of the MFC-MWCNT sensors. These results were corroborated by independent heat flow measurements (thermogravimetric analysis). The proposed MFC-MWCNT sensor platform may help paving the way to rapid, inexpensive, and robust liquid analysis and identification.

Nasser A. M. Barakat, S. Gamal, Hak Yong Kim et al.

Frontiers in Chemistry • 2023

Microbial fuel cells (MFCs) offer a dual solution of generating electrical energy from organic pollutants-laden wastewater while treating it. This study focuses on enhancing MFC performance through innovative electrode design. Three-dimensional (3D) anodes, created from corncobs and mango seeds via controlled graphitization, achieved remarkable power densities. The newly developed electrode configurations were evaluated within sewage wastewater-driven MFCs without the introduction of external microorganisms or prior treatment of the wastewater. At 1,000°C and 1,100°C graphitization temperatures, corncob and mango seed anodes produced 1,963 and 2,171 mW/m2, respectively, nearly 20 times higher than conventional carbon cloth and paper anodes. An advanced cathode composed of an activated carbon-carbon nanotube composite was introduced, rivaling expensive platinum-based cathodes. By optimizing the thermal treatment temperature and carbon nanotube content of the proposed cathode, comparable or superior performance to standard Pt/C commercial cathodes was achieved. Specifically, MFCs assembled with corncob anode with the proposed and standard Pt/C cathodes reached power densities of 1,963.1 and 2,178.6 mW/m2, respectively. Similarly, when utilizing graphitized mango seeds at 1,100°C, power densities of 2,171 and 2,151 mW/m2 were achieved for the new and standard cathodes, respectively. Furthermore, in continuous operation with a flow rate of 2 L/h, impressive chemical oxygen demand (COD) removal rates of 77% and 85% were achieved with corncob and mango seed anodes, respectively. This work highlights the significance of electrode design for enhancing MFC efficiency in electricity generation and wastewater treatment.

Na Zhao, Zhaokun Ma, Huaihe Song et al.

Energy Technology • 2018

Abstract Carbon nanotube forest (CNTFs) is grown vertically on the surface of highly conductive mesophase pitch carbon fibers (Pitch‐CFs) in the chemical vapor deposition method. Then the CNTFs‐modified CFs (CNTFs‐Pitch‐CF) as an anode material is assembled into the single chamber microbial fuel cells (MFCs). Interaction between electricigens and carbon nanotubes (CNTs) is investigated. The toxicity of the CNTs can make electricigens expend energy or electrons to produce exopolysaccharid (EPS) as much as possible. Accordingly, this leads to low voltage and the long start up time for the CNTFs‐Pitch‐CF anode in the early period. When the activity of electricigens with the EPS reaches a stable state over time, the output voltage rises rapidly and the maximum power density of the CNTFs‐Pitch‐CF‐equiped MFC is increased to 1112 mW/m 2 , which is approximately 1.55‐fold higher than Pitch‐CF‐equiped MFC. And the CNTFs‐Pitch‐CF‐equiped MFC can keep high electricity generation performance of MFCs for a long time, which is 75 % larger than the Pitch‐CF‐equiped MFC. This method of the CNTFs modifying the CFs provides a new idea for increasing the electricity generation performance of the MFCs.

Ashish Yadav, Nishith Verma

Graphene-based 3D Macrostructures for Clean Energy and Environmental Applications • 2021

Like conventional fuel cells, microbial fuel cells (MFCs) offer an exciting new avenue for generating clean and sustainable electrical energy directly from wastewater and other organic wastes. In recent years, 3D graphene-based macrostructures (GBMs) have been explored as both anode and cathode materials in MFCs to enhance their application potential. 3D GBMs offer an exciting prospect towards application in MFCs owing to their high surface area, biocompatibility, electrical conductivity and chemical stability, thereby leading to an enhanced power generation. This area of research, therefore, provides tremendous opportunities to address the basic challenges of MFCs pertaining to scale-up, commercialization and low power generation in addition to providing alternatives to conventionally used expensive electrodes. The current chapter summarizes the applications of 3D GBMs in MFCs, present their current developmental status, and discuss the key scientific and technological challenges in using them to improve the performance of MFCs.

Okan Avcı, Yudum Tepeli Büyüksünetçi, Emre Erden et al.

New Journal of Chemistry • 2021

A Pseudomonas fragi ( P. fragi ) and graphene–gold hybrid nanomaterial included a carbon felt electrode (graphene–Au/CFE) bioanode was developed and optimized.

Justine E. Mink, Ramy M. Qaisi, Muhammad M. Hussain

Energy Technology • 2013

Abstract Microbial fuel cells harvest electrical energy produced by bacteria during the natural decomposition of organic matter. We report a micrometer‐sized microbial fuel cell that is able to generate nanowatt‐scale power from microliters of liquids. The sustainable design is comprised of a graphene anode, an air cathode, and a polymer‐based substrate platform for flexibility. The graphene layer was grown on a nickel thin film by using chemical vapor deposition at atmospheric pressure. Our demonstration provides a low‐cost option to generate useful power for lab‐on‐chip applications and could be promising to rapidly screen and scale up microbial fuel cells for water purification without consuming excessive power (unlike other water treatment technologies).

Raul-Cristian Roman, Radu-Emil Precup, E. Petriu et al.

Studies in Informatics and Control • 2023

: The purpose of this paper is to propose a novel controller that is based on a combination of two data-driven algorithms, namely the Fictitious Reference Iterative Tuning (FRIT) algorithm and the Model-Free Adaptive Control (MFC) algorithm while considering a particular form of MFC, that is the intelligent proportional-integral-derivative (iPID) controller. The main advantage of this combination is that the FRIT algorithm optimally tunes the parameters of the iPID controller by solving an optimization problem based on a metaheuristic African Vultures Optimization Algorithm (AVOA). The novel controller, referred to as the FRIT-iPID controller, is validated experimentally on a three-degree-of-freedom tower crane system laboratory equipment in the context of controlling the cart position, the arm angular position and the payload position for this system.

Shu Deng, Yahong Zhang, Yajun Luo et al.

International Journal of Applied Electromagnetics and Mechanics • 2025

For plate structures undergoing time-varying motion, deformation and vibration occur simultaneously due to the inertia force induced by accelerations. The difference in deformation magnitude and vibration amplitude significantly affects the accuracy and safety of the structures. Traditional control methods cannot suppress deformation and vibration synchronously, therefore, an integrated active control strategy has been developed to address this issue. The dynamic equation for the plate partially covered with Macro Fiber Composite (MFC) actuators is established. The system presents strong nonlinearity because four coupling factors are involved, such as rigid body motion coupling, deformation coupling, rigid body motion and deformation coupling, as well as electro-mechanical coupling. To simultaneously mitigate deformation and vibration, the Sliding Mode Control (SMC) algorithm is optimized using the Particle Swarm Optimization (PSO) algorithm, enabling integrated active control for the plate subjected to time-varying inertia forces. The effectiveness of the developed PSO-SMC algorithm is validated by simulations.

Alexander Vidal, Samy Wu Fung, Stanley Osher et al.

2025 American Control Conference (ACC) • 2024

Mean-field control (MFC) problems aim to find the optimal policy to control massive populations of interacting agents. These problems are crucial in areas such as economics, physics, and biology. We consider the non-local setting, where the interactions between agents are governed by a suitable kernel. For $N$ agents, the interaction cost has $\mathcal{O}(N^2)$ complexity, which can be prohibitively slow to evaluate and differentiate when $N$ is large. To this end, we propose an efficient primal-dual algorithm that utilizes basis expansions of the kernels. The basis expansions reduce the cost of computing the interactions, while the primal-dual methodology decouples the agents at the expense of solving for a moderate number of dual variables. We also demonstrate that our approach can further be structured in a multi-resolution manner, where we estimate optimal dual variables using a moderate $N$ and solve decoupled trajectory optimization problems for large $N$. We illustrate the effectiveness of our method on an optimal control of 5000 interacting quadrotors.

T. Silva, Celia Hameury, Giovanni Ferrari et al.

Volume 4B: Dynamics, Vibration, and Control • 2014

Abstract Active vibration control of a free-edge rectangular sandwich plate is proposed and tested. The experimental setup consists of a honeycomb panel having a carbon-fiber reinforced polymer (CFRP) outer skins and a polymer-paper core, subjected to an orthogonal disturbance, due to an electrodynamics exciter and controlled by Macro Fibre Composite (MFC) actuators and sensors. MFC parches consist of rectangular piezoceramic rods sandwiched between layers of adhesive, electrodes and polyamide film. The MFC actuators and sensors are controlled by a programmable digital dSPACE® controller board. The control algorithm proposed in this paper is based on the Positive Position Feedback (PPF) technique and is successfully applied with different combinations of inputs/outputs (Single Input Single Output, MultiSISO, Multi Input Multi Output) in order to control the first four normal modes. The control appears to be robust and efficient in reducing vibration in linear (small amplitude) and nonlinear (large amplitude) vibrations regimes, although the structure under investigation exhibits a relativity high modal density, i.e., four resonances in a range of about 100 Hz. The control strategy allows to effectively control each resonance both individually or simultaneously.

S. Nadjafi, G. G. Amiri, A. Hosseinzadeh et al.

SHILAP Revista de lepidopterología • 2020

In this paper, a computationally simple approach for damage localization and quantification in beam-like structures is proposed. This method is based on using modal flexibility curvature (MFC) and particle swarm optimization (PSO) algorithm. Analytical studies in the literature have shown that changes in the modal flexibility curvature can be considered as a sensitive and suitable criterion for identifying damage in the beam-like structures. Modal flexibility curvature can be calculated utilizing central difference approximation, based on entries of the modal flexibility matrix. The PSO algorithm, as a powerful optimization tool, is used to minimize the error function which is formulated as an error function between the measured modal flexibility curvatures of the damaged structure and those calculated from the analytical structure. To demonstrate the efficiency of the method, two beam-like structures under different damage scenarios are studied. In addition, the robustness of presented method is investigated when only the first several modal data are available. It is observed that the proposed approach is able to localize and quantify various damage cases only by a few lower vibrational modes and also, it is low-sensitive to measurement noise.

Qidong Li, Hehua Ju, Pengfei Xiao

IOP Conference Series: Materials Science and Engineering • 2020

In order to solve the problem of 6R manipulator kinematics, this paper analyzes the inverse kinematics solution and optimizes the solution process of forward kinematics. Aimed to the structural characteristics of the manipulator, this paper first introduces the concept of the natural invariants, establishes the Denavit-Hatenber (D-H) model based on the natural invariants and proposes a new iterative algorithm for manipulator forward kinematics. Then, the inverse kinematics based on the natural invariants is proposed, which is divided into the position inverse solution and the inverse attitude solution, and the analytical solution is obtained by analyzing the singularity of the solution. Finally, the visual simulation model is built with MFC and Coin4D. The simulation results show that the proposed forward and inverse kinematics method of the six-degree-of-freedom manipulator has small error and high efficiency, and the accuracy and real-time performance of the algorithm are verified.

Xuejing Li, Yajuan Qin, Huachun Zhou et al.

Wireless Communications and Mobile Computing • 2020

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms.

Minmin Wang, Aimin An

2020 Chinese Automation Congress (CAC) • 2020

Microbial fuel cell (MFC) has serious nonlinearity and external disturbances when operating. In order to keep the ideal output of MFC under different load disturbances, an intelligent optimization control method based on BP neural network PID(BP-NN PID) is proposed. This method uses self-learning ability to tune the parameter of the PID controller. In addition, the online real-time control of the BP-NN algorithm shows the ability of the BP-NN to handle nonlinear systems. The simulation experiment finally shows that compared with traditional PID and single neuron PID, the BP-NN PID algorithm has good dynamic response, steady-state accuracy, adaptive ability, and strong robustness, which can make the output voltage of MFC meet the requirements.

Hao Long, Arash Khalatbarisoltani, Xiaosong Hu

2022 IEEE Vehicle Power and Propulsion Conference (VPPC) • 2022

Modular fuel cell (MFC) systems, which have multiple fuel cell stacks that work independently, are becoming increasingly popular in heavy-duty transportation because they are more efficient, reliable, and easy to use (plug-and-play). This paper aims at developing a lifespan-conscious energy management strategy (EMS) for a heavy-duty modular fuel cell vehicle (MFCV) to enhance the economic performance during the whole life cycle of its components. In this work, the main decision-making problem is broken down into two more minor problems that are solved simultaneously with a decentralized optimization algorithm called the auxiliary problem principle (APP). A comparison is made between the proposed EMS and a well-known central algorithm called sequential quadratic programming (SQP). The simulation results show that a decentralized algorithm can significantly speed up convergence while raising the economic cost a little more than a centralized algorithm.

M. Gharib

Reports in Mechanical Engineering • 2020

Received October 15, 2020 Revised November 25, 2020 Accepted December 5, 2020 This paper suggests a practical approach for the development of a stable robot controller using the Quantitative Feedback Principle (QFT). Robot manipulators have a multivariable nonlinear transfer function, the implementation of the QFT method includes, first the conversion of their nonlinear plant into a group of linear and uncertain plant set, and then an ideal robust controller for each set has been designed. To demonstrate the effectiveness of our algorithm, we show the implementation of the two degrees of freedom manipulator. In the approach provided, the controller has been designed directly by specifying and optimizing the transfer function coefficients using a genetic algorithm. The consistency and limitations of the method are considered to be the restrictions of the problem in the optimization process. System stability and tracking problem are perceived to be the limitations of the system in the optimization process. Non-linear simulations on the tracking problem are carried out and the results illustrate the performance of the controllers. Finally, the controller constructed based on the QFT approach is compared with the TFC and MFC (Fuzzy) controllers and it is shown that the QFT methodology indicates a controller that has increased control efficiency.

Hassan Mehmood, T. Zafar, Khurram Kamal et al.

2024 International Conference on Robotics and Automation in Industry (ICRAI) • 2024

Microbial Fuel Cells (MFCs) are environment-friendly devices to generate energy through microbial metabolism of the chemical energy of organic compounds. Few of the critical challenges of implementing MFCs are their low power density, high internal resistance, and poor electron transport. These factors are major drawbacks to the MFCs efficiency and ability to scale up MFCs. The proposed work aims to enhance the MFC performance by optimizing the proportions of key operational parameters to overcome the implementation challenges. These parameters include Substrate Concentration $(S)$, Anodophilic Biomass $(x_{a})$, Methanogenic Biomass $(x_{m})$, and Oxidized Mediator $(M_{ox})$. The parameters are optimized using a Gradient Based Optimization (GBO). During the optimization process, it was imperative to establish a sound mathematical model that predicted MFC's biological and physical behavior. With the implementation of the GBO algorithm, the output current is enhanced by 8. 89 percent. The results shows that promising future of the proposed method.

Jinhua Jiang, Zhiyuan Gao, Hesheng Zhang et al.

Journal of Intelligent Material Systems and Structures • 2024

To reduce vibration-induced fatigue fractures of aero-engine blades, an active vibration control scheme for aero-engine blade has been developed proposed. In this paper, the Macro Fiber Composite (MFC) are used as actuators and sensors to suppress the vibration of the blade, but the position of the actuators and sensors has a great influence on the effectiveness of the blade vibration suppression. To achieve optimize suppress effect, the objective criterion for position optimization was proposed to be the absolute value normalized superposition of the strain of the first few natural frequency modes of the blade. First, a finite element analysis was conducted on the blade model. Then, the positions of the actuators and sensors were determined by using the differential evolution algorithm for optimization. The blade model with three layout strategies was constructed in ADAMS, and the ADAMS-Simulink joint simulation platform was constructed. Finally, the comparison of actuator and sensor effects with three position layout strategies is completed, the layout optimization strategy proposed in this paper achieves more significant vibration suppression with less control force was verified though the feedforward control experiments, and the effective of the proposed strategies are verified on the experimental platform for blade active vibration control.

Chenlong Wang, Baolong Zhu, Fengying Ma et al.

Electronics • 2024

The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID.

L. Fan

International Journal of Electrochemical Science • 2021

Microbial fuel cell (MFC) has attracted more and more attention as a kind of efficient and green power source. Due to its own complexity, the precise control of MFC is still difficult to achieve. The output voltage of MFC has large overshoot and shock under traditional PID control, and it is difficult to adapt to the changes in operating conditions. So, a genetic algorithm optimized fuzzy PID control is proposed to improve the controller effect and realize the constant voltage output control of the MFC. Simulation results show that compared with the traditional PID, the genetic algorithm optimized PID, and the fuzzy tuning PID, the genetic algorithm optimized fuzzy PID control shows smaller overshoot, better stability and stronger anti-interference ability. Optimizing the conventional PID through fuzzy logic and genetic algorithm is a simple, easy, low-cost but effective method to solve the problems of unstable power generation and poor anti-interference ability of MFC system.

Chong Li, Liang Shen, Jiang Shao et al.

Polymers • 2023

In order to improve the vibration suppression effect of the flexible beam system, active control based on soft piezoelectric macro-fiber composites (MFCs) consisting of polyimide (PI) sheet and lead zirconate titanate (PZT) is used to reduce the vibration. The vibration control system is composed of a flexible beam, a sensing piezoelectric MFC plate, and an actuated piezoelectric MFC plate. The dynamic coupling model of the flexible beam system is established according to the theory of structural mechanics and the piezoelectric stress equation. A linear quadratic optimal controller (LQR) is designed based on the optimal control theory. An optimization method, designed based on a differential evolution algorithm, is utilized for the selection of weighted matrix Q. Additionally, according to theoretical research, an experimental platform is built, and vibration active control experiments are carried out on piezoelectric flexible beams under conditions of instantaneous disturbance and continuous disturbance. The results show that the vibration of flexible beams is effectively suppressed under different disturbances. The amplitudes of the piezoelectric flexible beams are reduced by 94.4% and 65.4% under the conditions of instantaneous and continuous disturbances with LQR control.

Hegazy Rezk, A. Olabi, M. Abdelkareem et al.

Sustainability • 2023

Microbial fuel cells convert the chemical energy conserved in organic matter in wastewater directly to electrical energy through living microorganisms. These devices are environmentally friendly thanks to their ability to simultaneously produce electrical energy and wastewater treatment. The output power of the yeast microbial fuel cell (YMFC) depends mainly on glucose concentration and glucose/yeast ratio. Thus, the paper aims to boost the power of YMFC by identifying the best values of glucose concentration and glucose/yeast ratio. The suggested approach comprises fuzzy modelling and optimization. Fuzzy is used to build the model based on the measured data. In the optimization stage, the marine predators’ algorithm (MPA) is applied to identify the best glucose concentration values and glucose/yeast ratio corresponding to the maximum output power of YMFC. The results revealed the superiority of the combination of fuzzy and MPA compared with the response surface methodology (RSM) approach. Regarding the modelling accuracy, the coefficient of determination increased by 13.32% and 8.37%, respectively, for without methylene blue and with methylene blue compared with RSM. The integration between fuzzy and MPA succeeded in maximizing the output power from YMFC. Without MB, the power density increased by 25% and 29.3%, respectively, compared with measured data and RSM. In addition, with MB, the power density increased by 22.4% and 26%, compared with measured data and RSM.

Tongxin Xu, Ruichen Yu, Zhiyuan Gao et al.

2024 43rd Chinese Control Conference (CCC) • 2024

Sensors and actuators placements play an important role in the control performance of an active vibration control system for piezoelectric smart aero-engine blades. Incorrect placement of sensors and actuators may lead to instability in the control system. This paper proposes optimal placement methods based on the Particle Swarm Optimization (PSO) algorithm for actuators and sensors. Firstly, three sensors and actuators placement optimization schemes are proposed based on the modal analysis and transient analysis results of ANSYS. These schemes include the superposition of non-absolute normalized modal strains, the energy criterion for transient analysis, and the superposition of absolute normalized modal strains. Secondly, it is ensured that the placements of the MFC piezoelectric elements are optimized based on the Particle Swarm Optimization (PSO) Algorithm and Differential Evolution (DE) Algorithm within the boundary constraints. Finally, the convergence of the fitness function based on the PSO algorithm and the DE algorithm as well as the three schemes are comparatively analyzed and the optimal placement of the MFC is found. Simulation results show that the last scheme optimized based on the PSO algorithm has better convergence effect and can obtain the optimal placements of sensors and actuators.

Mojgan Fayyazi, Paramjotsingh Sardar, Sumit Infent Thomas et al.

Sustainability • 2023

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.

Mostafa Ghasemi, Kimia Rostami, Hamed Farahani et al.

Sustainable Energy & Fuels • 2025

The dual challenge of clean energy generation and wastewater treatment has intensified interest in microbial fuel cells (MFCs) as sustainable, bio-electrochemical systems. In this study, four low-cost cathode catalysts based...

Ruoyang Song, Xinghua Liu, Zhongbao Wei et al.

IEEE Transactions on Transportation Electrification • 2024

The safety, life expectancy and operating cost of fuel cell hybrid electric vehicle (FCHEV) are highly dependent on the power allocation among the onboard power sources. Motivated by this, this article proposes a machine learning-based multi-physical-constrained energy management strategy to improve the driving economy, thermal safety, and durability of FCHEV. In particular, the fully-continues deep deterministic policy gradient (DDPG) algorithm is exploited to optimize the power distribution of FCHEV in a real-time fashion. Within the proposed framework, the thermal and aging behaviors of the hybrid power sources are scrutinized and optimized, for the first time, to enhance the safety and life performance of FCHEV. The proposed strategy is tested under typical road missions for validation. The unexpected temperature build-up of lithium-ion battery (LIB) and the degradation of hybrid system can be well suppressed to enhance the thermal safety and life performance. Moreover, comparative results suggest that the proposed strategy can optimize the hybrid sources split collaborated with the improvement of operating performance, economy performance and operating time.

D. Robledo, L. Roleda

Journal of Microbiology & Biology Education • 2024

ABSTRACT Bioelectricity is an interdisciplinary concept that encompasses the fields of chemistry, physics, and biology. It is the scientific study of membrane transport mechanisms that govern the formation and dissipation of ion gradients. Teaching and learning across disciplines, such as bioelectricity, are known among science teachers to be challenging and complex. One of the critical problems is that only a few teaching materials and learning resources specifically support interdisciplinary teaching, especially in science. This paper described the development of an improvised microbial fuel cell (iMFC) as an alternative activity that addresses scientific concepts of cellular respiration, reduction-oxidation reaction, and electricity generation in an interdisciplinary approach. In this activity, students designed, constructed, and tested their iMFCs. The learning gains of the students were measured using parallel pretest/post-test and analyzed using descriptive statistics and dependent t-tests. The perceptions of teachers and students on using the iMFC activity in teaching-learning bioelectricity were obtained from a survey questionnaire and interviews. Results revealed that the iMFC activity significantly improved students’ learning gains in bioelectricity, for the topics cellular respiration (t(239)=45.03; P < 0.01), reduction-oxidation reaction (t(239)=39.85; P < 0.01), and electricity (t(239)=31.1; P < 0.01), with computed normalized gains of 0.45, 0.50, and 0.39, respectively. Furthermore, seven subthemes emerged from the teachers’ and students’ perceptions, namely, knowledge acquisition, student engagement, academic emotions, affordability, student satisfaction, distractions, and cleanliness. Overall results indicated that the iMFC activity can be an effective teaching material for providing an authentic learning experience in a multidisciplinary topic like bioelectricity. Future investigations on the iMFC activity and its impact on other aspects of learning, such as students’ motivation, self-efficacy, and engagement, are recommended.

Peng Yin, Xiangfu Niu, Shuo-Bin Li et al.

Nature Communications • 2024

Carbon supported PtCo intermetallic alloys are known to be one of the most promising candidates as low-platinum oxygen reduction reaction electrocatalysts for proton-exchange-membrane fuel cells. Nevertheless, the intrinsic trade-off between particle size and ordering degree of PtCo makes it challenging to simultaneously achieve a high specific activity and a large active surface area. Here, by machine-learning-accelerated screenings from the immense configuration space, we are able to statistically quantify the impact of chemical ordering on thermodynamic stability. We find that introducing of Cu/Ni into PtCo can provide additional stabilization energy by inducing Co-Cu/Ni disorder, thus facilitating the ordering process and achieveing an improved tradeoff between specific activity and active surface area. Guided by the theoretical prediction, the small sized and highly ordered ternary Pt_2CoCu and Pt_2CoNi catalysts are experimentally prepared, showing a large electrochemically active surface area of ~90 m^2 g_Pt^‒1 and a high specific activity of ~3.5 mA cm^‒2. Platinum-based intermetallic alloys are promising candidates as low-platinum oxygen reduction reaction catalysts for proton exchange membrane fuel cells. Here, the authors develop small sized and highly ordered Pt_2CoCu and Pt_2CoNi catalysts for fuel cells by machine-learning accelerated computational screening.

R. Gurjar, M. Behera

Water and Environment Journal • 2023

Volatile fatty acid (VFA)‐rich leachate generated from acidogenesis of kitchen waste in a leach bed reactor (LBR) was utilized in an earthen microbial fuel cell (EMFC) to generate electricity. Effects of organic loading rate (OLR, 5–10 g VS/L·day) and pH (5–7) on LBR enumerated optimized parameters of OLR (10 g VS/L·day) and pH (5.74) to obtain total VFA (TVFA) of 7.7 ± 0.3 g/L in the leachate, with maximum contribution from acetic acid. Leachate obtained from the LBR was fed to the EMFC with varying OLR (2–7 kg COD/m3·day). The highest power density of 0.76 W/m3 (at OLR 7 kg COD/m3·day) was obtained with higher VFA content in the leachate. A neural network based on the Levenberg–Marquard function effectively predicted chemical oxygen demand and TVFA removal. This study establishes LBR as a techno‐economic method to obtain useful substrate for EMFC. Furthermore, the response modelling of EMFC demonstrates the potential of utilizing machine learning in biological treatment.

K. Onyelowe, A. Ebid, Rosa Belén Ramos Jiménez et al.

Scientific Reports • 2025

There is an initiative driven by the carbon-neutrality nature of biochar in recent times, where various countries across Europe and North America have introduced perks to encourage the production of biochar for construction purposes. This objective aligns with the zero greenhouse emission targets set by COP27 for 2050. This research work seeks to assess the effectiveness of biochar in soils with varying grain size distributions in enhancing the soil–water characteristic curve (SWCC). This work further explores the effect of different combinations of biochar content (0 to 15 mass %) on the bioelectricity generation from biochar-improved plant microbial fuel cells (BPMFC). Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. The newly established model demonstrates a reasonable ability to predict SWCC and a cheaper technology in predicting the suction of unsaturated soils in relation to the studied bioelectric factors of the BPMFC. Overall, in this research paper, the GB, SVM and CN2 outclassed the other regression techniques in this order thereby proposing the cheapest technology with the highest performance index to predict the SWCC behavior of unsaturated soils in a BPMFC system.

M. K. Pasha, K. Munawar

International Journal of Education and Management Engineering • 2021

: Microbial Fuel Cell (MFC) is a bio-electrochemical device that generates electric current by using bacteria. MFCs are currently a topic of intense research and interest due to their ability to produce renewable energy along with added benefits such as wastewater treatment. Although the theoretical concepts and applicability of MFCs are great, their application, thus far has been limited due to the limits of power production. Current research aims to improve the efficiency as well as the upper limit of power production by MFCs. In parallel to current research, this study is designed with a similar aim to do a comprehensive data analysis on the topic of MFCs by using techniques of Artificial Intelligence. Therefore, we started this study by obtaining the relevant data through an extensive literature retrieval for developing Artificial Neural Network model. The data from the output layer was viewed by using VOSviewer software and was further subjected to analysis. The data collected through machine learning provided an insight about the optimal conditions of MFCs which would allow for maximum current production. It discusses two existing types of MFCs; namely mediator type and mediator free type of MFC. Anode respiring bacteria (ARB), also known as exoelectrogenes can be used as the mediator to transfer electrons by utilizing the substrate present at the anode. Our results suggest that different combinations of bacterium and biofilms can produce more electric current with improved stability. This study will provide an insight to improve the working capacity of MFCs. It is likely that MFCs will one day be used as a stand-alone power production method by optimizing the current production capacity. Moreover, these advancements will have a significant by utilizing MFCs for making chips and biosensors, and treating wastewater.

Adam Hess-Dunlop, Harshitha Kakani, Colleen Josephson

Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies • 2024

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

Farhad Shabani, Hemma Philamore, F. Matsuno

IEEE Access • 2021

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.

Stilianos Louca

NAR Genomics and Bioinformatics • 2025

Abstract The relationship between gene content differences and microbial taxonomic divergence remains poorly understood, and algorithms for delineating novel microbial taxa above genus level based on multiple genome similarity metrics are lacking. Addressing these gaps is important for macroevolutionary theory, biodiversity assessments, and discovery of novel taxa in metagenomes. Here, I develop machine learning classifier models, based on multiple genome similarity metrics, to determine whether any two marine bacterial and archaeal (prokaryotic) metagenome-assembled genomes (MAGs) belong to the same taxon, from the genus up to the phylum levels. Metrics include average amino acid and nucleotide identities, and fractions of shared genes within various categories, applied to 14 390 previously published non-redundant MAGs. At all taxonomic levels, the balanced accuracy (average of the true-positive and true-negative rate) of classifiers exceeded 92%, suggesting that simple genome similarity metrics serve as good taxon differentiators. Predictor selection and sensitivity analyses revealed gene categories, e.g. those involved in metabolism of cofactors and vitamins, particularly correlated to taxon divergence. Predicted taxon delineations were further used to de novo enumerate marine prokaryotic taxa. Statistical analyses of those enumerations suggest that over half of extant marine prokaryotic phyla, classes, and orders have already been recovered by genome-resolved metagenomic surveys.