A Novel Poisoned Water Detection Method Using Smartphone Embedded Wi-Fi Technology and Machine Learning Algorithms

Water is a necessary fluid to the human body and automatic checking of its quality and cleanness is an ongoing area of research. One such approach is to present the liquid to various types of signals and make the amount of signal attenuation an indication of the liquid category. In this article, we have utilized the Wi-Fi signal to distinguish clean water from poisoned water via training different machine learning algorithms. The Wi-Fi access points (WAPs) signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then Channel-State-Information CSI measures are extracted and converted into feature vectors to be used as input for machine learning classification algorithms. The measured amplitude and phase of the CSI data are selected as input features into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results show that the model is adequate to differentiate poison water from clean water with a classification accuracy of 89% when LSTM is applied, while 92% classification accuracy is achieved when the AdaBoost-Ensemble classifier is applied

In Situ Synthesis of Reduced Graphene Oxide and Gold Nanocomposites for Nanoelectronics and Biosensing

In this study, an in situ chemical synthesis approach has been developed to prepare graphene–Au nanocomposites from chemically reduced graphene oxide (rGO) in aqueous media. UV–Vis absorption, atomic force microscopy, scanning electron microscopy, transmission electron microscopy, and Raman spectroscopy were used to demonstrate the successful attachment of Au nanoparticles to graphene sheets. Configured as field-effect transistors (FETs), the as-synthesized single-layered rGO-Au nanocomposites exhibit higher hole mobility and conductance when compared to the rGO sheets, promising its applications in nanoelectronics. Furthermore, we demonstrate that the rGO-Au FETs are able to label-freely detect DNA hybridization with high sensitivity, indicating its potentials in nanoelectronic biosensing

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

Chitosan-carboxylic acid grafted multifunctional magnetic nanocomposite as a novel adsorbent for effective removal of methylene blue dye from aqueous environment

In this work, magnetite nanoparticle decorated graphene oxide (MGO) is modified with triethylenetetramine (TETA), which is supported by maleated chitosan (MACS), named MGO@TETA@MACS. The novel magnetic nanohybrid is successfully fabricated via an in situ coprecipitation method, which is for methylene blue (MB) uptake from liquid phase environment. XRD, FTIR, TGA, nitrogen isotherm, SEM, and Zeta potential experiments were utilized to explore the MGO@TETA@MACS nanocomposite. The mean particle size and surface area of MGO@TETA@MACS were measured to be 11.5 nm and 128.04 m2·g−1, respectively. The nonlinear kinetic and isotherm models were utilized to evaluate the adsorption equilibrium. Data analysis revealed that the uptake kinetics fits the PFO model while the adsorption equilibrium follows the Langmuir isotherm. The maximum adsorption capacity of MGO@TETA@MACS nanocomposite, estimated from Langmuir isotherm, was 247.37 mg/g at the ambient temperature. Thermodynamic calculations confirmed the exothermic and spontaneous nature of the MB decontamination.</p