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.

Experimental and validation with neural network time series model of microbial fuel cell bio-sensor for phenol detection.

Phenol is one of the most commonly known chemical compound found as a pollutant in the chemical industrial wastewater. This pollutant has potential threat for human health and environment, as it can be easily absorbed by the skin and the mucous. Here, we prepared dual chambered microbial fuel cell (MFC) sensor for the detection of phenol. Varying concentration of phenol (100 mg/l, 250 mg/l, 500 mg/l, and 1000 mg/l) was applied as a substrate to the MFC and their change in output voltage was also measured. After adding 100 mg/l, 250 mg/l, 500 mg/l, and 1000 mg/l of phenol as sole substrate to the MFC, the maximum voltage output was obtained as 360 ± 10 mV, 395 ± 8 mV, 320 ± 7 mV, 350 ± 5 mV respectively. This biosensor was operated using industrial wastewater isolated microbes as a sensing element and phenol was used as a sole substrate. The topologies of ANN were analyzed to get the best model to predict the power output of MFCs and the training algorithms were compared with their convergence rates in training and test results. Time series model was used for regression analysis to predict the future values based on previously observed values. Two types of mathematical modeling i.e. Scaled Conjugate Gradient (SCG) algorithm and Time-series model was used with 44 experimental data with varying phenol concentration and varying synthetic wastewater concentration to optimize the biosensor performance. Both SCG and time series showing the best results with R2 value 0.98802 and 0.99115.

Prediction of stenosis behaviour in artery by neural network and multiple linear regressions.

Blood flow analysis in the artery is a paramount study in the field of arterial stenosis evaluation. Studies conducted so far have reported the analysis of blood flow parameters using different techniques, but the regression analysis is not adequately used. Artificial neural network is a nonlinear and nonparametric approach. It uses back-propagation algorithm for regression analysis, which is effective as compared to statistical model that requires a higher domain of statistics for prediction. In our manuscript, twofold analyses of data are done. First phase involves the determination of blood flow parameters using physiological flow pulse generator. The second phase includes regression modelling. The inputs to the model were axial length from stenosis, radial distance, inlet velocity, mean pressure, density, viscosity, time, and degree of blockage. Output included dependent variables in the form of output as mean velocity, root-mean-square (RMS) velocity, turbulent intensity, mean frequency, RMS frequency, frequency of turbulent intensity, gate time mean, gate time RMS. The temperature, density, and viscosity conditions were kept constant for various degrees of blockages. It was followed by regression analysis of variables using conventional statistical and neural network approach. The result shows that the neural network model is more appropriate, because value of percentage of response variation of dependent variable is almost approaching unity as compared to statistical analysis.

Simultaneous bioelectricity generation from cost-effective MFC and water treatment using various wastewater samples.

Worldwide, the requirement of electrical energy has increased with an increase in population. Thus, there is a need to develop an alternative source of sustainable energy, such as microbial fuel cell (MFC). MFC is a better option of energy generation and can provide a renewable resource which utilizes wastewater into power by the help of microorganisms. MFC is one of the advanced methods for treating wastewater and simultaneously producing current and voltage. Dual-chambered MFC was prepared using two plastic boxes (500 ml) by using wastewater as an anolyte. Different types of mediators are used in MFC including methylene blue, potassium ferricyanide, and EDTA to facilitate and higher the efficiency of electron transfer from the MFC to the electrode. Maximum OCV and current output of sample 1 (Budha Talab pond water) were 0.86 V and 75.1 mA and of sample 2 (Jaypee cement plant) were 1.42 V and 122 mA. The maximum current output of sample 3 (sugar industry, sewage waste, NIT canteen) was 1.3 V. Various physiochemical parameters such as dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) were analyzed which affect the power output. The obtained result concluded that wastewater should be feed at a certain time interval to avoid the loss of substrate for organisms in the anodic chamber which lead to the death of the microorganism. Among all, sugar industry wastewater has a high potential for power generation as their physiochemical results are suitable for better power output.

Extraction and optimization of exopolysaccharide from Lactobacillus sp. using response surface methodology and artificial neural networks.

The microbial polysaccharides secreted and produced from various microbes into their extracellular environment is known as exopolysaccharide. These polysaccharides can be secreted from the microbes either in a soluble or insoluble form.Lactobacillus sp. is one of the organisms that have been found to produce exopolysaccharide. Exo-polysaccharides (EPS) have various applications such as drug delivery, antimicrobial activity, surgical implants and many more in different fields. Medium composition is one of the major aspects for the production of EPS from Lactobacillus sp., optimization of medium components can help to enhance the synthesis of EPS . In the present work, the production of exopolysaccharide with different medium composition was optimized by response surface methodology (RSM) followed by tested for fitting with artificial neural networks (ANN). Three algorithms of ANN were compared to investigate the highest yeild of EPS. The highest yeild of EPS production in RSM was achieved by the medium composition that consists of (g/L) dextrose 15, sodium dihydrogen phosphate 3, potassium dihydrogen phosphate 2.5, triammonium citrate 1.5, and, magnesium sulfate 0.25. The output of 32 sets of RSM experiments were tested for fitting with ANN with three algorithms viz. Levenberg-Marquardt Algorithm (LMA), Bayesian Regularization Algorithm (BRA) and Scaled Conjugate Gradient Algorithm (SCGA) among them LMA found to have best fit with the experiments as compared to the SCGA and BRA.