Optical Fibers by Reactive Extrusion of Butyl Methacrylate

Summary: A process for the production of poly(butyl methacrylate) optical fibers by reactive extrusion is developed. The reactive system is adapted to the reduced reaction time in the extruder combining concepts based on the free volume theory and a kinetic model for the mass polymerization of butyl methacrylate. A kinetic model is proposed and the reaction evolution is simulated at different temperatures and initiator concentrations. This allows the choice of reaction conditions adequate for reactive extrusion technical limitations. Reactive extrusion experiments are carried out in a twin‐screw extruder and the effect of the different kinetic and process conditions on the reaction are analyzed in relation to the residence time distribution measured by an UV fluorescence method. Some optical properties of poly(butyl methacrylate) fibers are reported.

Conversion versus the position along the screw length.     

FTIR evaluation of functional groups involved in the formation of haloacetic acids during the chlorination of raw water

This work investigated the formation potential of haloacetic acid (HAA) compounds in the raw water for the Bangkhen water treatment plant (Bangkok, Thailand). The resin adsorption technique (with three different types of resins, i.e. DAX-8, AG-MP-50 and WA-10) was employed to characterize the organic content in the raw water into six fractions, i.e. hydrophobic neutral (HPON), hydrophobic acid (HPOA), hydrophobic base (HPOB), hydrophilic neutral (HPIN), hydrophilic acid (HPIA) and hydrophilic base (HPIB). Hydrophilic species appeared to be the predominant organic species in this water source (approximately 60%) with the neutral fraction being the most abundant (approximately 40%). Hydrophobic species, on the other hand, played the most important role in the formation of haloacetic acids as they contributed to as much as approximately 56% of total HAA formation potential. Among the three hydrophobic species, the hydrophobic base exhibited the highest specific HAA formation with 208mugHAAs/mg of dissolved organic carbon (DOC). Each organic fraction was examined for its associated functional groups by Fourier transform infrared (FTIR). The investigation of the formation of HAAs was achieved by tracking the changes in the FTIR results of the same water sample before and after the chlorination reaction. Based on the results obtained from this study, carboxylic acids, ketone, amide, amino acids and aromatic characteristic organics seemed to be the main precursors to HAA formation.     

The effect of partially stabilized zirconia on the biological properties of HA/HDPE composites in vitro

The effect of partially stabilized zirconia (PSZ) on the biological properties of the hyroxyapatite - high density polyethylene (HA/HDPE) composites was studied by investigating the simultaneous effect of hydroxyapatite and PSZ volume fractions on the in vitro response of human osteoblast cells. The biocompatibility of composite samples with different volume fraction of HA and PSZ powders was assessed by proliferation, alkaline phosphatase (ALP) and cell attachment assays on the osteoblast cell line (G-292) in different time periods. The effect of composites on the behavior of G-292 cells was compared with those of HDPE and TPS (Tissue Culture Poly Styrene as negative control) samples. Results showed a higher proliferation rate of G-292 cells in the presence of composite samples as compared to the HDPE sample after 7 and 14 days of incubation period. ALP production rate in all composite samples was higher than HDPE and TPS samples. The number of adhered cells on the composite samples was higher than the number adhered on the HDPE and TPS samples after the above mentioned incubation periods. These findings indicates that the addition of PSZ does not have any adverse affect on the biocompatibility of HA/HDPE composites. In fact in some experiments PSZ added HA/HDPE composites performed better in proliferation, differentiation and attachment of osteoblastic cells.     

Chemical treatment of sludge: in-depth study on toxic metal removal efficiency, dewatering ability and fertilizing property preservation

The presence of toxic metals in municipal sewage sludge restricts the application of this biomass in agricultural area. A chemical leaching process using a combination of inorganic acid and two oxidants has been developed for sludge decontamination. The present study investigated the effects of the concentrations of sulphuric acid (H2SO4), hydrogen peroxide and ferric chloride on metal solubilization from sewage sludge, as well as preservation of fertilizing properties (nutrient content) and dewatering ability of the treated sludge. The analysis of the results from batch leaching tests has allowed to define the optimal conditions for the reagent concentrations, which are 56 kg Fe3+ tonne(-1) of dry sludge solids (tds), 8 kg H2O2 tds(-1), and enough H2SO4 to reach a pH between 2.0 and 2.5 but less than 142 kg H2SO4 tds(-1). Finally, under these conditions, oxidoreduction potential values are found to be between 450 and 475 mV.     

Symbolic interpretation of artificial neural networks

Hybrid intelligent systems that combine knowledge-based and artificial neural network systems typically have four phases, involving domain knowledge representation, mapping of this knowledge into an initial connectionist architecture, network training and rule extraction, respectively. The final phase is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule extraction techniques. The first technique extracts a set of binary rules from any type of neural network. The other two techniques are specific to feedforward networks, with a single hidden layer of sigmoidal units. Technique 2 extracts partial rules that represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and universal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches     

Analysis matching of 5 GHz operating planar antenna EM characteristics between rigorous raw model,simulation and measurements

Journal of Computational Electronics - One of the challenges in antenna analysis is achieving concordance between the results of a purely theoretical numerical method application and commercial...     

Flexible Vanadium Dioxide Photodetectors for Visible to Longwave Infrared Detection at Room Temperature

Flexible optoelectronics is a rapidly growing field, with a wide range of potential applications. From wearable sensors to bendable solar cells, curved displays, and curved focal plane arrays, the possibilities are endless. The criticality of flexible photodetectors for many of these applications is acknowledged, however, devices that are demonstrated thus far are limited in their spectral range. In this study, flexible photodetectors are demonstrated using a VO_x nanoparticle ink, with an extremely broad operating wavelength range of 0.4 to 20 µm. This ink is synthesized using a simple and scalable wet-chemical process. These photodetectors operate at room temperature and exhibit minimal variance in performance even when bent at angles of up to 100 ° at a bend radius of 6.4 mm. In addition, rigorous strain testing of 100 bend and release cycles revealed a photoresponse with a standard deviation of only 0.55%. This combination of mechanical flexibility, wide spectral response, and ease of fabrication makes these devices highly desirable for a wide range of applications, including low-cost wearable sensors and hyperspectral imaging systems.     

A systematic literature review for network intrusion detection system (IDS)

International Journal of Information Security - With the recent increase in internet usage, the number of important, sensitive, confidential individual and corporate data passing through internet...     

Preparation and characterization of silica nanoparticles as an efficient carrier for two bio-detergents based enzymes

Bio-detergents are new bio-friendly formulas that contain biobased ingredients, including enzymes. In the present study, alkaline protease and α-amylase were immobilized via physisorption onto silica nanoparticles (SNPs). The derivatized SNPs served as major components of a prepared bio-detergent. Alkaline protease was produced by the recombinant Bacillus subtilis cells that carry the protease genes on a multiple-copy plasmid, while α-amylase was commercially purchased. SNPs were prepared by the sol–gel method and well-characterized through the Brunauer–Emmett–Teller (BET) method, scanning electron microscopy (SEM), transmission electron microscopy (TEM), zeta potential, X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. The adsorption capacity of the SNPs was determined via colorimetry through the adsorption of methylene blue dye (MB), with approximately 97% adsorption achieved under the conditions employed. The Langmuir isotherm well-described the adsorption of MB on SNPs. High immobilization yield for the enzymes was obtained, and the storage stability of SNP-alkaline protease and SNP-α-amylase was good, reaching 65% and 85% of their initial activities after 6 weeks of storage at 4°C, respectively. The immobilized enzymes could be reused for 7 cycles. Additionally, the immobilized enzymes retained residual activity to a greater extent than free enzymes in simulated basic detergent solutions. SNPs containing adsorbed alkaline protease and α-amylase were mixed with a basic detergent solution, and the washing efficiency of some proteinous and starchy stains was examined through Hunter Lab spectrophotometry. The latter experiments demonstrated that the immobilized enzymes performed well during the washing process.     

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.