Separation of copper and zinc from waste acidic mine effluents of Rio Tinto area

A study of removal of Copper and Zinc from acidic mine effluents by means of chelating ion exchange resins has been carried out. The effluents having pH ranging from 1.7 to 1.9, contain mainly iron, copper and zinc and some other elements at much lower concentrations. The study includes chemical characterisation of effluents samples, the assessment of chelating resins and their selectivity for the separation of copper and zinc. Chelating resins containing either carboxylic, phosphoric or/and amine groups have been employed. The results obtained are correlated to the specific metal-complexing group attached to the polymer matrix. Resins containing carboxylate groups were found to provide most selective copper/zinc separations. A conceptual flowsheet of the separation process based on the data obtained for treating the effluents with different resins is presented.     

Lignocellulosic Biomass Waste-Derived Cellulose Nanocrystals and Carbon Nanomaterials: A Review

Rapid population and economic growth, excessive use of fossil fuels, and climate change have contributed to a serious turn towards environmental management and sustainability. The agricultural sector is a big contributor to (lignocellulosic) waste, which accumulates in landfills and ultimately gets burned, polluting the environment. In response to the current climate-change crisis, policymakers and researchers are, respectively, encouraging and seeking ways of creating value-added products from generated waste. Recently, agricultural waste has been regularly appearing in articles communicating the production of a range of carbon and polymeric materials worldwide. The extraction of cellulose nanocrystals (CNCs) and carbon quantum dots (CQDs) from biomass waste partially occupies some of the waste-recycling and management space. Further, the new materials generated from this waste promise to be effective and competitive in emerging markets. This short review summarizes recent work in the area of CNCs and CQDs synthesised from biomass waste. Synthesis methods, properties, and prospective application of these materials are summarized. Current challenges and the benefits of using biomass waste are also discussed.     

NAD+ Precursors Repair Mitochondrial Function in Diabetes and Prevent Experimental Diabetic Neuropathy

Axon degeneration in diabetic peripheral neuropathy (DPN) is associated with impaired NAD⁺ metabolism. We tested whether the administration of NAD⁺ precursors, nicotinamide mononucleotide (NMN) or nicotinamide riboside (NR), prevents DPN in models of Type 1 and Type 2 diabetes. NMN was administered to streptozotocin (STZ)-induced diabetic rats and STZ-induced diabetic mice by intraperitoneal injection at 50 or 100 mg/kg on alternate days for 2 months. mice The were fed with a high fat diet (HFD) for 2 months with or without added NR at 150 or 300 mg/kg for 2 months. The administration of NMN to STZ-induced diabetic rats or mice or dietary addition of NR to HFD-fed mice improved sensory function, normalized sciatic and tail nerve conduction velocities, and prevented loss of intraepidermal nerve fibers in skin samples from the hind-paw. In adult dorsal root ganglion (DRG) neurons isolated from HFD-fed mice, there was a decrease in NAD⁺ levels and mitochondrial maximum reserve capacity. These impairments were normalized in isolated DRG neurons from NR-treated mice. The results indicate that the correction of NAD⁺ depletion in DRG may be sufficient to prevent DPN but does not significantly affect glucose tolerance, insulin levels, or insulin resistance.     

Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The new method aims to avoid generating noise and control imbalances between and within classes. Various experiments have been carried out with six classifiers and four oversampling methods. Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.     

Magnetic Polaron States in Photoluminescent Carbon Dots Enable Hydrogen Peroxide Photoproduction (Small 32/2023)

Photoactivation of aspartic acid-based carbon dots (Asp-CDs) induces the generation of spin-separated species, including electron/hole (e⁻/h⁺) polarons and spin-coupled triplet states, as uniquely confirmed by the light-induced electron paramagnetic resonance spectroscopy. The relative population of the e⁻/h⁺ pairs and triplet species depends on the solvent polarity, featuring a substantial stabilization of the triplet state in a non-polar environment (benzene). The electronic properties of the photoexcited Asp-CDs emerge from their spatial organization being interpreted as multi-layer assemblies containing a hydrophobic carbonaceous core and a hydrophilic oxygen and nitrogen functionalized surface. The system properties are dissected theoretically by density functional theory in combination with molecular dynamics simulations on quasi-spherical assemblies of size-variant flakelike model systems, revealing the importance of size dependence and interlayer effects. The formation of the spin-separated states in Asp-CDs enables the photoproduction of hydrogen peroxide (H₂O₂) from water and water/2-propanol mixture via a water oxidation reaction.     

Natural functionalisation of a traditional textile “Ehram”

This study is focused on the natural functionalisation of a traditional woven fabric called “Ehram”. Which is a wool based woven fabric constituted from wool yarns that are originally coloured and used in different cities of the Eastern Anatolian Region, Turkey. Because these fabrics were originally coloured (pigmented), the study aimed to functionalise them with minimal colour changes from their original colour. For this aim; lavender (Lavandula angustifolia) which is also a natural source of pleasant odour was used in finishing of Ehram. The colour changes by the application of the lavender on Ehram and the benefits that come out from the use of lavender were then analysed and presented in the study. In this respect, antibacterial activities (against Staphylococcus aureus and Escherichia coli) of dyed samples were tested within the scope of the study. In addition, colour measurements (K/S and CIE L*a*b* C* and h^o) and FTIR (Fourier-transform infrared) microspectroscopy analyses of dyed fabric samples were made and microscope images were taken. It was observed that lavender with a copper-based mordanting agent can present antibacterial activity against S. aureus and E. coli in Ehram.     

Topping of naturally dyed wool fabrics with different natural dye sources

Wool fabrics can be dyed with synthetic dyes and even with natural dyes. To present a different aspect to the coloration of wool, the current study was focused on a topping process (ie, the dyeing of wool that was already dyed) with different natural dye sources. For this purpose, the fabrics that were already dyed with a natural dye source were once again dyed with two different natural dyes. In bottom dyeing (bottoming), fabrics were dyed with hops (Humulus lupulus L.) in the presence of different mordanting agents. Then the dyed, washed and dried samples were once again dyed (topping) with two different natural dye sources. For topping, powdered madder and acorn were tested in direct dyeing of wool samples. Finally, the colour changes were analysed with the use of a spectrophotometer. The study demonstrates that such a process (ie, bottoming with hops in the presence of different mordanting agents and then topping with madder or acorn) can be a way of obtaining different shades and colours with sufficient/good fastness values by natural dyeing.     

Improving Brain Tumor Classification with Deep Learning UsingSyntheticData

Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By comparing models trained on two different datasets, we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network (CycleGAN) on the generalization of DL. One model is trained only on the original dataset, while the other is trained on the combined dataset of synthetic and original images. Synthetic data generated by CycleGAN improved the best accuracy values for glioma, meningioma, and pituitary tumor classes from 0.9633, 0.9569, and 0.9904 to 0.9968, 0.9920, and 0.9952, respectively. The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature. Additionally, except for pixel-level and affine transform data augmentation, synthetic data has been generated in the figshare brain dataset for the first time.