Magnetic and Electrocatalytic Properties of Transition Metal Doped MoS\u3csub\u3e2\u3c/sub\u3e Nanocrystals

In this paper, the magnetic and electrocatalytic properties of hydrothermally grown transition metal doped (10% of Co, Ni, Fe, and Mn) 2H-MoS2 nanocrystals (NCs) with a particle size 25–30 nm are reported. The pristine 2H-MoS2 NCs showed a mixture of canted anti-ferromagnetic and ferromagnetic behavior. While Co, Ni, and Fe doped MoS2 NCs revealed room temperature ferromagnetism, Mn doped MoS2 NCs showed room temperature paramagnetism, predominantly. The ground state of all the materials is found to be canted-antiferromagnetic phase. To study electrocatalytic performance for hydrogen evolution reaction, polarization curves were measured for undoped and the doped MoS2 NCs. At the overpotential of η = −300 mV, the current densities, listed from greatest to least, are FeMoS2, CoMoS2, MoS2, NiMoS2, and MnMoS2, and the order of catalytic activity found from Tafel slopes is CoMoS2 \u3e MoS2 \u3e NiMoS2 \u3e FeMoS2 \u3e MnMoS2. The increasing number of catalytically active sites in Co doped MoS2 NCs might be responsible for their superior electrocatalytic activity. The present results show that the magnetic order-disorder behavior and catalytic activity can be modulated by choosing the suitable dopants in NCs of 2D materials

Functionalized graphene sensors for real time monitoring fermentation processes:electrochemical and chemiresistive sensors

We developed a reference-less, chemiresistive, solid-state pH sensor to determine the acidification of the fermentation liquid in real time during the growth of Lactococcus lactis. One of the crucial findings of this work was that the ERGO-PA could not be used as such. It appeared that it was necessary to protect the sensor area with a Nafion coating to measure the pH in the fermentation broth. Most likely, the change in the concentration of redox-active components in the fermentation broth influences the conductivity of the ERGO-PA. Nafion formed a cation-selective membrane on top of the ERGO-PA allowing protons to diffuse to the selective layer of the sensor but not the redox-active components in the fermentation medium. We also reported a new approach to measure the dissolved oxygen concentration (DO) in a fermentation broth. The functionality of the sensor to measure DO was demonstrated during the growth of the obligate aerobic actinomycete Amycalotopsis methanolica in miniaturized 3D-printed bioreactors. For this oxygen-sensing application, the required modifications were obtained by doping hydrothermally reduced graphene oxide with nitrogen and boron atoms (N,B-HRGO). Further, these chemiresistive sensors are housed in the 3D printed bioreactor lid and used to measure pH, DO, and biomass in 3 ml fermentation broth. Additionally, the pH-sensor was equipped with a small heating element and a temperature sensor and that could be used for temperature control of the fermentation liquid. The setup was demonstrated to measure the pH, DO, temperature and biomass concentration in four parallel bioreactors

Potential of tropical filamentous cyanobacteria for low-cost bioremediation and bioproduct synthesis

Chinnathambi Velu investigated the potential of cyanobacteria for the bioremediation of ash-dam wastewater and CO₂ for biofertilizer and bioproduct synthesis. He developed cost-, water- and energy-smart biomass production algal cultivation system for the purpose he investigated. Part of his research is being implemented by farmers at Perenjori, Western Australia

A Multi-Stage Electricity Price Forecasting For Day-Ahead Markets

Forecasting hourly spot prices for real-time electricity usage is a challenging task. This thesis work investigates a series of price forecasting methods for day-ahead Iberian Electricity Markets (MIBEL). The dataset from MIBEL was used to train and test multiple forecast models. A hybrid combination of Auto Regressive Integrated Moving Average (ARIMA) and Generalized Linear Model (GLM) was proposed and its Mean Percentage Error (MAPE) values were compared against several methods. For example, ARIMA, GLM, Random forest (RF) and Support Vector Machines (SVM) methods are investigated. The results indicate a significant improvement in MAPE and correlation coefficient values for the proposed hybrid ARIMA-GLM method. Forecasting hourly spot prices for real-time electricity markets are key activities in energy trading operations. This thesis work specifically develop a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA, and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested with multiple duration periods ranging from one-week to ninety days for variables such as price, load, and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The results indicate a significant improvement in the Mean Absolute Percentage Error (MAPE) values compared to other present approaches. To reduce the prediction error, three types of variable selection techniques such as Relative importance using Linear Regression (LR), Multivariate Adaptive Regression Splines (MARS), and Random forest (RF) were used. Four datasets (Three months, Six months, weekday, and weekend) were used to validate the performance of the model. Three different set of variables (17, 4, 2) were used in this study. At last, three common variables selected from these feature selection approaches were tested with all these datasets. Considerable reduction in MAPE for both three and six-month dataset were achieved by these variable selection approaches. In addition, the work also investigate the application of a multi-layered deep neural network to the Iberian electric market (MIBEL) price forecasting task. A 3-month and 6-month of energy data are used to train the proposed model. The 3-month and 6-month period is treated as a historical dataset to train and predict the price for day-ahead markets. The network structure is implemented using Googleâs machine learning TensorFlow platform. Activation function such as Rectifier linear unit (ReLU) were tested to achieve a better Mean Absolute percentage error (MAPE) considering the weekday and weekend variations