Integration of Membrane Bioreactor and Reverse Osmosis for Textile Wastewater Treatment and Reclamation: A Pilot-Scale Study

Membrane bioreactor (MBR) technology, a combination of traditional activated sludge and membrane filtration, has been widely used for industrial wastewater treatment and reclamation. This paper highlights a pilot-scale MBR system treating textile wastewater from a textile factory in Taiwan. Over 7 months of continuous operation, the average MBR influent chemical oxygen demand (COD) is 332 mg/L, and the average effluent COD is 38 mg/L, which results in approximately 88% COD removal. A reverse osmosis (RO) module is installed after 2 months of MBR operation and uses the MBR permeate as its influent. The RO produces pure water with average COD, conductivity, and color of 7 mg/L, 16 μS/cm, and 7 Pt-Co, respectively. The RO permeate is suitable for reuse in manufacturing processes, and the RO membrane shows stable performance with TMP, which is less than or equal to 0.5 kg/cm2 during the test. The study demonstrates the great feasibility of MBR combined with RO for treating and reclaiming textile wastewater

Investigation of factors affecting the accumulation of vinyl chloride in polyvinyl chloride piping used in drinking water distribution systems

Plastic piping made of polyvinyl chloride (PVC), and chlorinated PVC (CPVC), is being increasingly used for drinking water distribution lines. Given the formulation of the material from vinyl chloride (VC), there has been concern that the VC (a confirmed human carcinogen) can leach from the plastic piping into drinking water. PVC/CPVC pipe reactors in the laboratory and tap samples collected from consumers homes (n = 15) revealed vinyl chloride accumulation in the tens of ng/L range after a few days and hundreds of ng/L after two years. While these levels did not exceed the EPA’s maximum contaminant level (MCL) of 2 μg/L, many readings that simulated stagnation times in homes (overnight) exceeded the MCL-Goal of 0 μg/L. Considerable differences in VC levels were seen across different manufacturers, while aging and biofilm effects were generally small. Preliminary evidence suggests that VC may accumulate not only via chemical leaching from the plastic piping, but also as a disinfection byproduct (DBP) via a chlorine-dependent reaction. This is supported from studies with CPVC pipe reactors where chlorinated reactors accumulated more VC than dechlorinated reactors, copper pipe reactors that accumulated VC in chlorinated reactors and not in dechlorinated reactors, and field samples where VC levels were the same before and after flushing the lines where PVC/CPVC fittings were contributing. Free chlorine residual tests suggest that VC may be formed as a secondary, rather than primary, DBP. Further research and additional studies need to be conducted in order to elucidate reaction mechanisms and tease apart relative contributions of VC accumulation from PVC/CPVC piping and chlorine-dependent reactions

Set voltage distribution stabilized by constructing an oxygen reservoir in resistive random access memory

In this letter, the instability mechanism of RRAM was investigated, and a technique was developed to stabilize the distribution of high resistance state (HRS) and better concentrate the SET voltage. In previous research, we found that an interface-type switching characteristic was observed on the I-V curve beneath the filament-type switching behavior, owing to the oxygen accumulation effect. In this letter, this interface-type switching characteristic is used to fit the natural distribution of HRS for an analysis of the instability mechanism. According to the results, the reason for the HRS distribution is the accumulation of extra oxygen ions which are left over from a lower degree of oxygen and oxygen vacancy recombination during the reset process. We propose a solution which creates an extra oxygen reservoir by changing the surface topography of the electrode to store the surplus oxygen ions from the reset process, eliminating the accumulation effect, and indeed improving stability. Please click Additional Files below to see the full abstract

Inhibition of Serine Protease Activity Protects Against High Fat Diet-Induced Inflammation and Insulin Resistance.

Recent evidence suggests that enhanced protease-mediated inflammation may promote insulin resistance and result in diabetes. This study tested the hypothesis that serine protease plays a pivotal role in type 2 diabetes, and inhibition of serine protease activity prevents hyperglycemia in diabetic animals by modulating insulin signaling pathway. We conducted a single-center, cross-sectional study with 30 healthy controls and 57 patients with type 2 diabetes to compare plasma protease activities and inflammation marker between groups. Correlations of plasma total and serine protease activities with variables were calculated. In an in-vivo study, LDLR-/- mice were divided into normal chow diet, high-fat diet (HFD), and HFD with selective serine protease inhibition groups to examine the differences of obesity, blood glucose level, insulin resistance and serine protease activity among groups. Compared with controls, diabetic patients had significantly increased plasma total protease, serine protease activities, and also elevated inflammatory cytokines. Plasma serine protease activity was positively correlated with body mass index, hemoglobin A1c, homeostasis model assessment-insulin resistance index (HOMA-IR), tumor necrosis factor-α, and negatively with adiponectin concentration. In the animal study, administration of HFD progressively increased body weight, fasting glucose level, HOMA-IR, and upregulated serine protease activity. Furthermore, in-vivo serine protease inhibition significantly suppressed systemic inflammation, reduced fasting glucose level, and improved insulin resistance, and these effects probably mediated by modulating insulin receptor and cytokine expression in visceral adipose tissue. Our findings support the serine protease may play an important role in type 2 diabetes and suggest a rationale for a therapeutic strategy targeting serine protease for clinical prevention of type 2 diabetes

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Enhanced Antifungal Bioactivity of Coptis Rhizome Prepared by Ultrafining Technology

The aim of this study was to identify and quantify the bioactive constituents in the methanol extracts of Coptis Rhizome prepared by ultrafining technology. The indicator compound was identified by spectroscopic method and its purity was determined by HPLC. Moreover, the crude extracts and indicator compound were examined for their ability to inhibit the growth of Rhizoctonia solani Kühn AG-4 on potato dextrose agar plates. The indicator compound is a potential candidate as a new plant derived pesticide to control Rhizoctonia damping-off in vegetable seedlings. In addition, the extracts of Coptis Rhizome prepared by ultrafining technology displayed higher contents of indicator compound; they not only improve their bioactivity but also reduce the amount of the pharmaceuticals required and, thereby, decrease the environmental degradation associated with the harvesting of the raw products