Synthesis, characterisation and evaluation on the performance of ferrofluid for microplastic removal from synthetic and actual wastewater

Synthesis of ferrofluid without the addition of stabilizing agents or surfactants is an innovation of new method for microplastic removal. This study focuses on the ability of several types of oils as carriers and how they may improve the removal efficiency of the microplastic. The method is relatively low cost, simple and sustainable. The formation of ferrofluid involved the mixing of oil and iron oxide powder. The experimental work was commenced by adding 2 mm polyethylene terephthalate (PET) microplastics into synthetic ferrofluid. Then, the removal efficiency of microplastics was examined by varying the elements of ferrofluid based on three specific parameters, namely type of oil, volume of oil and dosage of iron oxide to obtain a standard formulation of the optimum results. Overall findings of the study indicated that the optimum formulation for ferrofluid preparation was at a ratio of 1:2.5 (volume of oil: dosage of magnetite) using lubricating oil which has successfully removed 99% of microplastic from water media. Subsequently, the physical and chemical properties of the prepared ferrofluid were also analysed using scanning electron microscope (SEM) and Fourier transform infrared (FTIR) spectroscopy. Performance evaluation of the prepared ferrofluid on actual wastewater (laundry wastewater) revealed that 64% of microplastics were removed after treatment

Classification of interstitial lung disease patterns with topological texture features

Topological texture features were compared in their ability to classify morphological patterns known as 'honeycombing' that are considered indicative for the presence of fibrotic interstitial lung diseases in high-resolution computed tomography (HRCT) images. For 14 patients with known occurrence of honey-combing, a stack of 70 axial, lung kernel reconstructed images were acquired from HRCT chest exams. A set of 241 regions of interest of both healthy and pathological (89) lung tissue were identified by an experienced radiologist. Texture features were extracted using six properties calculated from gray-level co-occurrence matrices (GLCM), Minkowski Dimensions (MDs), and three Minkowski Functionals (MFs, e.g. MF.euler). A k-nearest-neighbor (k-NN) classifier and a Multilayer Radial Basis Functions Network (RBFN) were optimized in a 10-fold cross-validation for each texture vector, and the classification accuracy was calculated on independent test sets as a quantitative measure of automated tissue characterization. A Wilcoxon signed-rank test was used to compare two accuracy distributions and the significance thresholds were adjusted for multiple comparisons by the Bonferroni correction. The best classification results were obtained by the MF features, which performed significantly better than all the standard GLCM and MD features (p < 0.005) for both classifiers. The highest accuracy was found for MF.euler (97.5%, 96.6%; for the k-NN and RBFN classifier, respectively). The best standard texture features were the GLCM features 'homogeneity' (91.8%, 87.2%) and 'absolute value' (90.2%, 88.5%). The results indicate that advanced topological texture features can provide superior classification performance in computer-assisted diagnosis of interstitial lung diseases when compared to standard texture analysis methods.Comment: 8 pages, 5 figures, Proceedings SPIE Medical Imaging 201