A novel adaptive neuro-fuzzy inference system model to predict the intrinsic mechanical properties of various cellulosic fibers for better green composites

In this work a novel adaptive neuro-fuzzy inference system model has been developed for the prediction of the intrinsic mechanical properties of various cellulosic natural fibers to enhance their selection for better green composite materials. The model combined modeling function of the fuzzy inference system with the learning capability of the artificial neural network. The developed model was built up based on experimental mechanical properties of various cellulosic fiber types commonly used for natural fiber reinforced composites, and the rules have been generated directly from the experimental data. The developed model was capable of predicting all of Young's modulus, ultimate tensile strength, and elongation at break properties from only two intrinsic properties of fibers namely; cellulose and moisture content. The adaptive neural fuzzy inference system (ANFIS) structure included five layers to realize the establishment and calculation of each model. The system architecture included the fuzzy input layer, product layer, normalized layer, de-fuzzy layer and total output layer. Results have been revealed that the model’s predictions were highly in agreement with other experimentally gained properties when compared with experimental results for verifying the approach. The accuracy of the developed model would enhance predicting other cellulosic fiber properties to develop better natural fiber composites in the near future.

     

Development of ANFIS technique for estimation of CO2 solubility in amino acids and study on impact of input parameters

ANFIS (Adaptive neuro fuzzy inference system) modeling of CO₂ capture using chemical absorbent was carried out in this study to correlate the solubility of CO₂ to the solvent and operational parameters. In the ANFIS model, the input parameters including temperature, pressure, and physio-chemical properties of the solvent were considered, while the loading of CO₂ in the absorbent was considered as the sole target output to be predicted by the model. Indeed, we developed a machine learning based model for predicting the CO₂ loading capacity in amino acid salt solutions as the chemical absorbent of carbon dioxide. This model uses a metaheuristic optimized ANFIS based on a wide range of amino acids. This study's novel part is the use of Differential Evolution (DE) and Firefly Algorithm (FA) metaheuristics in order to solve hyper-parameter tuning of ANFIS as an optimization problem based on differential evolution. Accordingly, the optimized ANFIS model has an R² score of 0.9520 for the test data and a score of 0.9841 for the training data. This indicates that the proposed model is both general and accurate in terms of its predictions for CO₂ loading in amino acid salt solutions. The MAPE and RMSE error rates are also 1.17E-01, respectively, while the MAPE error rate is 1.14E-01.     

Modelling and measuring the thermal conductivity of multi-metallic Zn/Cu nanofluid

A metallic nanofluid is a suspension of metallic nanoparticles in a base fluid. Multi-metallic nanoparticles are a combination of two or more types of metallic particles. Such multi-metallic nanoparticles were suspended in water using an ultrasonic vibrator for different total volume fractions and different ratios of metallic/metallic nanoparticles. A transient hot wire setup was built to measure the thermal conductivity of the nanofluid at different temperatures. The experimental results were in good agreement with the results in the literature. Then, the experimental results were used as input data for an adaptive neural fuzzy inference system (ANFIS) to predict the thermal conductivity of the multi-metallic nanofluid. The maximum deviation between the ANFIS results and experimental measurements was 1 %. The predicted results and the experimental data were compared with other models. The ANFIS model was found to have good ability to predict the thermal conductivity of the multi-metallic nanofluid over the range of the experimental results.     

Analysis of trace‐level nitrated polycyclic aromatic hydrocarbons in water samples by solid‐phase microextraction with gas chromatography and mass spectrometry

A solid‐phase microextraction coupled with gas chromatography and mass spectrometry method has been developed for the determination of ten nitrated polycyclic aromatic hydrocarbons in water samples. Five different kinds of commerical fibers were used to compare the extraction efficiency, including 65 μm polydimethylsiloxane/divinylbenzene, 100 μm polydimethylsiloxane, 30 μm polydimethylsiloxane, 7 μm polydimethylsiloxane, and 85 μm polyacrylate fibers. Five factors were also selected to optimize conditions, including extraction temperature, time, stirring speed, salt concentration, and headspace volume. Taguchi design was applied to design the experiments and obtain the best parameters. The results show that 65 μm polydimethylsiloxane/divinylbenzene fiber directly immersed into aqueous solution for 35 min at 55°C with a constant stirring rate of 1150 rpm were the optimal conditions. Under these conditions, the limits of quantification were 0.007–0.063 μg/L, and the relative standard deviation based on six replicates ranged from 2.8 to 9.5%. The spiked recoveries ranged from 69.1 to 110.1%. Intra‐ and inter day relative standard deviations at three concentration levels were less than 12%, and the recoveries were 66.4–111.5%. The proposed method is reliable for analyzing nitrated polycyclic aromatic hydrocarbons in different water samples.     

Comparative analysis of adaptive neuro-fuzzy inference system (ANFIS) and RSRM models to predict DBP (trihalomethanes) levels in the water treatment plant

Since disinfection by-products are a growing concern, it is important to know their quantities in water treatment plants before they are released to the public. As a result, there is a requirement for constant monitoring of disinfection by-products (DBPs), which can have major consequences for human health and productivity. Consequently, in previous studies, several models for predicting disinfection byproduct formation in drinking water have been developed which were either linear/log-linear, hybrid or neural network (radial basis function). In this study, an adaptive neuro-fuzzy inference system (ANFIS) is proposed for predicting trihalomethane levels in real distribution systems. To train and verify the proposed model, 24 sets of data were used, including THMs levels (TCM, BDCM, DBCM and T-THM levels) and five parameters (pH, temperature, UVA₂₅₄, residual chlorine, and dissolved organic carbon). As compared to response surface modeling (RSRM) coefficient of determination, R² is between 0.727 < R² < 0.886, average absolute deviation, AAD = 4.07–10.99 %), MAE = 0.01 – 0.978, and RMSE = 0.017 – 1.449. Further, ANFIS for THMs (T-THMs, TCM, BDCM, and DBCM) prediction consistently show higher regression coefficients between 0.956 < R² < 0.989, average absolute deviation, AAD = 0.350 – 1.977 %), MAE = 0.002 – 0.133, and RMSE = 0.007 – 0.401, Consequently, based on the statistical indices obtained, ANFIS, on the other hand, proved to be effective for predicting the formation of THMs, and thus allowed improved DBPs monitoring in water treatment systems.     

High-speed on-demand 3D printed bioresorbable vascular scaffolds

Recent development of the high-resolution Micro-Continuous Liquid Interface Production (μCLIP) process has enabled 3D printing of biomedical devices with micron-scale precision. Despite our recent success in demonstrating fabrication of bioresorbable vascular scaffolds (BVS) via μCLIP, key technical obstacles remain. Specifically, achieving comparable radial stiffness to nitinol stents required strut thickness of 400 μm. Such large struts would negatively affect blood flow through smaller coronary vessels. Low printing speed also made the process impractical for potential on-demand fabrication of patient-specific BVSs. Lack of a systematic optimization strategy capturing the sophisticated process-materials-performance dependencies impedes development of on-demand fabrication of BVSs and other biomedical devices. Herein, we developed a systematic method to optimize the entangled process parameters, such as materials strength/stiffness, exposure dosage, and fabrication speed. A dedicated speed working curve method was developed to calibrate the μCLIP process, which allowed experimental determination of dimensionally-accurate fabrication parameters. Composition of the citric acid-based bioresorbable ink (B-Ink?) was optimized to maximize BVS radial stiffness, allowing scaffold struts at clinically-relevant sizes. Through the described dual optimization, we have successfully fabricated BVSs with radial stiffness comparable to nitinol stents and strut thickness of 150 μm, which is comparable to the ABSORB GT1BVS. Fabrication of 2-cm long BVS with 5 μm, 10 μm, and 15 μm layer slicing can now be accomplished within 26.5, 15.3, and 11.3 min, respectively. The reported process optimization methods and high-speed, high-resolution 3D printing capability demonstrate a promising solution for on-demand fabrication of patient-specific biomedical devices.     

Rapid electrophoretic monitoring of the anaesthetic ketamine and its metabolite norketamine in rat blood using a contactless conductivity detector to study the pharmacokinetics

A method of capillary electrophoresis with contactless conductivity detection has been developed for non‐enantioselective monitoring the anaesthetic ketamine and its main metabolite norketamine. The separation is performed in a 15 μm capillary with an overall length of 31.5 cm and length to detector of 18 cm; inner surface of the capillary is covered with a commercial coating solution to reduce the electroosmotic flow. In an optimised background electrolyte with composition 2 M acetic acid + 1% v/v coating solution under application of a high voltage of 30 kV, the migration time is 97.1 s for ketamine and 95.8 s for norketamine, with an electrophoretic resolution of 1.2. The attained detection limit was 83 ng/mL (0.3 μmol/L) for ketamine and 75 ng/mL (0.3 μmol/L) for norketamine; the number of theoretic plates for separation of an equimolar model mixture with a concentration of 2 μg/mL was 683 500 plates/m for ketamine and 695 400 plates/m for norketamine. Laboratory preparation of rat blood plasma is based on mixing 10 μL of plasma with 30 μL of acidified acetonitrile, followed by centrifugation. A pharmacokinetic study demonstrated an exponential decrease in the plasma concentration of ketamine after intravenous application and much slower kinetics for intraperitoneal application.     

Synthesis of carbon nanotube‐based nanocomposite and application for wastewater treatment by ultrasonicated adsorption process

The sorption of methylene blue (MB) and basic yellow 28 (BY28) dyes in water on Ag@ZnO/MWCNT (Ag‐doped ZnO loaded on multiwall carbon nanotubes) nanocomposite is investigated in a batch process, optimizing starting initial dye concentration, sonication time and adsorbent mass. Isotherms and kinetic behaviours of MB and BY28 adsorption onto Ag@ZnO/MWCNT were explained by extended Freundlich and pseudo‐second‐order kinetic models. Ag@ZnO/MWCNT was synthesized and characterized using X‐ray diffraction, energy‐dispersive X‐ray spectroscopy, field emission scanning electron microscopy and Brunauer–Emmett–Teller analysis. According to the experimental data, adaptive neuro‐fuzzy inference system (ANFIS), generalized regression neural network (GRNN), backpropagation neural network (BPNN), radial basic function neural network (RBFNN) and response surface methodology (RSM) were developed, and applied to forecast the removal performance of the sorbent. The influence of process variables (i.e. sonication time, initial dye concentration, adsorbent mass) on the removal of MB and BY28 was considered by central composite rotatable design of RSM, GRNN, ANFIS, BPNN and RBFNN. The performances of the developed ANFIS, GRNN, BPNN and RBFNN models were compared with RSM mathematical models in terms of the root mean square error, coefficient of determination, absolute average deviation and mean absolute error. The coefficients of determination calculated from the validation data for ANFIS, GRNN, BPNN, RBFNN and RSM models were 0.9999, 0.9997, 0.9883, 0.9898 and 0.9608 for MB and 0.9997, 0.9990, 0.9859, 0.9895 and 0.9593 for BY28 dye, respectively. The ANFIS model was found to be more precise compared to the other models. However, the GRNN method is much easier than the ANFIS method and needs less time for analysis. So, it has potential in chemometrics and it is feasible that the GRNN algorithm could be applied to model real systems. The monolayer adsorption capacity of MB and BY28 was 292.20 and 287.02 mg g^?1, respectively.     

Understanding wax screen-printing: A novel patterning process for microfluidic cloth-based analytical devices

In this work, we first introduce the fabrication of microfluidic cloth-based analytical devices (μCADs) using a wax screen-printing approach that is suitable for simple, inexpensive, rapid, low-energy-consumption and high-throughput preparation of cloth-based analytical devices. We have carried out a detailed study on the wax screen-printing of μCADs and have obtained some interesting results. Firstly, an analytical model is established for the spreading of molten wax in cloth. Secondly, a new wax screen-printing process has been proposed for fabricating μCADs, where the melting of wax into the cloth is much faster (∼5 s) and the heating temperature is much lower (75 °C). Thirdly, the experimental results show that the patterning effects of the proposed wax screen-printing method depend to a certain extent on types of screens, wax melting temperatures and melting time. Under optimized conditions, the minimum printing width of hydrophobic wax barrier and hydrophilic channel is 100 μm and 1.9 mm, respectively. Importantly, the developed analytical model is also well validated by these experiments. Fourthly, the μCADs fabricated by the presented wax screen-printing method are used to perform a proof-of-concept assay of glucose or protein in artificial urine with rapid high-throughput detection taking place on a 48-chamber cloth-based device and being performed by a visual readout. Overall, the developed cloth-based wax screen-printing and arrayed μCADs should provide a new research direction in the development of advanced sensor arrays for detection of a series of analytes relevant to many diverse applications.     

Ultrasound assisted alkaline hydrolysis of poly(ethylene terephthalate) in presence of phase transfer catalyst

Alkaline hydrolysis of poly(ethylene terephthalate) (PET) flakes from waste packaged drinking water bottles was carried out with and without influence of ultrasound waves rated 20 kHz frequency and 190 W power. Alkali used for hydrolysis was 10% NaOH (w/w). Tetrabutyl ammonium iodide (TBAI) was used as phase transfer catalyst (PTC) to enhance rate of hydrolysis. The experiment yields terephthalic acid (TA) and ethylene glycol as products of hydrolysis. Minimum time required for ultrasound assisted (UA) reaction and without ultrasound assistance (WUA) reaction to complete was investigated and compared. PTC: PET ratio = 0.03:1 w/w, temperature (90 °C) and NaOH concentration (10% w/w) were kept constant. All reactions were carried out at atmospheric pressure. For UA reaction, time required for 100% conversion of PET was found to be 45 min. For WUA reaction, the time required for 100% conversion of PET was found to be more than 65 min. Yield of TA was found to be >99% on the basis of moles of repeating units of PET fed. Melting point of product was found nearly equal to standard TA. Product TA was confirmed by comparing Fourier-transform infrared spectroscopy (FTIR) spectra of product with that of standard TA. Ratio of PTC to PET was fine-tuned for UA reaction keeping reaction time constant at 45 min.     

Quantitation of Terfenadine,Pseudoephedrine Hydrochloride,and Ibuprofen in a Liquid Animal Dosing Formulation Using High Performance Liquid Chromatography

Abstract

Stability-indicating assay methods based on high performance liquid chromatography have been developed for the quantitation of terfenadine, pseudoephedrine hydrochloride, and ibuprofen when combined in an aqueous 0.5% w/v Tween 20 and 0.5% methylcellulose animal dosing formulation. Because of the diversity of this drug mixture two separate chromatographic systems were required for the assays. A reversed phase system using a 3-μm Spherisorb 0DS-2 column was used to assay for terfenadine and ibuprofen. An ion-exchange system using a 10-μm Partisil SCX column was used to assay for pseudoephedrine hydrochloride. The methods are accurate and precise with relative standard deviations over the concentration ranges of interest of 2% or less.     

Feasibility of applying microsecond-pulse glow discharge time of flight mass spectrometry in surface depth analysis

This work evaluates the possibilities of applying microsecond-pulse glow discharge time of flight mass spectrometry (μs-pulse GD-TOFMS) in surface depth analysis. Investigations have been done for effects of discharge pressure on sputtered depth profiles as well as on topographies under μs-pulse GD mode; and also for influences of discharge current and discharge frequency on characteristics of sputtered surface. Sputtering rates of several pure metals under μs-pulse GD and dc-GD modes were studied and compared. The estimated erosion rates are 1.27, 2.90 and 5.18 nm s⁻¹ for pure Fe, Cu and Zn layer, respectively. Depth profiling were conducted for a technical Zn–Fe layer (about 10 μm) and for a Fe–Cu layer (about 1 μm) by μs-pulse GD. A simple model was developed and utilized to convert ion intensity into element concentration, and the experimental results were presented and discussed. Preliminary results show that μs-pulse GD-TOFMS has a promising future in the area of surface depth analysis, especially in the depth analysis of thin layers and of their cross-sections.     

Pseudotransient method for modeling of electrochemical machining

The electrochemical machining is aimed at the fabrication of parts of prescribed shape and dimensions by the anodic metal dissolution using tool-electrodes of various types. The task of the theory of electrochemical machining is to calculate the shape and dimensions of the workpiece depending on the shape of tool-electrode and operation conditions. In this work, a pseudotransient method of modeling, which is new for the steady-state electrochemical machining, is developed. In this method, first, the initial approximation of workpiece surface is prescribed; in the course of modeling, it shifts in the normal direction at a rate proportional to the residual of steady-state condition. The proposed method requires substantially lower computational cost than the non-steady-state method and can be used for the tool-electrodes of arbitrary shape.     

Effects of disordered hemispherical micropatterns on Staphylococcus epidermidis biofilm formation

Surfaces which have physical patterns in the scale of bacteria cells have been shown to influence the microorganism's adhesion and biofilm formation characteristics. Layer-by-layer self-assembly was utilized to create disordered hemispherical patterns on poly(dimethylsiloxane) with a feature size of 0.5 μm, 1.0 μm and 2.0 μm. The effects of pattern size on the retention and biofilm formation of Staphylococcus epidermidis were examined as a function of culture time. The 1.0 μm pattern significantly reduced biofilm surface coverage by ～30% after 5 h of culture in comparison to that on an unpatterned surface while the effect of the 0.5 and 2.0 μm patterns was negligible. On the 1.0 μm surface, bacteria initially adhered on the unpatterned areas of the disordered surface and subsequently developed into biofilms by spreading across the unpatterned areas while avoiding those covered by the pattern. The results suggest that the size of surface patterns is an important factor in altering bacteria adhesion and biofilm formation characteristics.     

Prediction of the mass gain during high temperature oxidation of aluminized nanostructured nickel using adaptive neuro-fuzzy inference system

In this paper, the applicability of ANFIS as an accurate model for the prediction of the mass gain during high temperature oxidation using experimental data obtained for aluminized nanostructured (NS) nickel is presented. For developing the model, exposure time and temperature are taken as input and the mass gain as output. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the network. We have compared the proposed ANFIS model with experimental data. The predicted data are found to be in good agreement with the experimental data with mean relative error less than 1.1%. Therefore, we can use ANFIS model to predict the performances of thermal systems in engineering applications, such as modeling the mass gain for NS materials.     

Membrane emulsification using membranes of regular pore spacing: Droplet size and uniformity in the presence of surface shear

During membrane emulsification it is shown that the size of the drops formed at the membrane surface may increase with increasing dispersed phase injection rate through the membrane, or it may decrease, depending on the prevailing conditions. This is illustrated using a paddle stirrer positioned above flat disc membranes with regular arrays of pores of 20 μm diameter and spacing between the pores of 80 and 200 μm. In the former case an additional mechanism for drop detachment is the push-off force, which is determined by the geometry of the drops as they deform at the membrane surface. When dispersing sunflower oil in to aqueous solutions containing Tween 20, drop sizes between 60 and 200 μm were produced, and in the case of the membrane when the push-off force was working the Coefficient of Variation of the drops formed was below 10%. The push-off force may be added to the shear-drag force to predict drop detachment. For the 200 μm pore spaced membrane this force is much less prominent than the 80 μm spaced membrane. The capillary-shear model has been modified to include this push-off force. The experimental study required the use of very low dispersed phase injection rates as well as very high rates. Hence, two different types of pumps were used to provide these: a peristaltic and syringe pumps. A small study comparing the drop size, and size distributions, showed that the pump type did not influence the drops produced by the membrane emulsification process.     

Mutagenicity of silver nanoparticles synthesized with curcumin (Cur-AgNPs)

IntroductionSilver nanoparticles (AgNPs) are of particular interest for their antibacterial properties and are produced by the action of reducing agents on silver ions. Curcumin from Curcuma longa (Zingiberaceae) has been used as a precursor for obtaining biogenic AgNPs, to act as a potential drug.ObjectivesThis study aimed to evaluate the toxicity of AgNPs synthesized with curcumin (Cur-AgNPs 0.081 mg/mL, ~130 nm) through the Salmonella/microsome (Ames test), one of the first required assays for evaluating toxicity.MethodsThe study design was experimental and in vitro. After defining the preliminary toxicity, the mutagenicity was assessed in a concentration range of 0.0010–0.0081 mg/plate Cur-AgNPs using histidine negative (His−) Salmonella Typhimurium strains TA97a, TA98, TA100, and TA102, with (+S9) and without metabolic activation (−S9), in triplicate. Assays were monitored by positive and negative controls. The results were statistically analyzed by Salanal software with p < 0.05 values considered significant.ResultsThe data obtained in the absence of metabolic activation showed that Cur-AgNPs is not mutagenic, but when exposed to the presence of S9, Cur-AgNPs became mutagenic to TA98 and TA100 strains, showing the significance of metabolizer enzymes to activate Cur-AgNPs on these bacteria, which recovered their abilities in synthesizing histidine (His+).ConclusionCur-AgNPs is mutagenic in the presence (+S9), but not in the absence (−S9) of metabolic activation, being able to act as indirect mutagens potentially to organisms that share the same genotype vulnerabilities found in TA98 and TA100 strains to cause a frameshift and base-pair substitution mutations, respectively.     

Development of a single-run liquid chromatography-mass spectrometry analysis for the detection of 11 multiclass contaminants of emerging concern using a direct filtration method

In toxicological analysis, the analytical validation method is important to assess the exact risk of contaminants of emerging concern in the environment. Syringe filters are mainly used to remove impurities from sample solutions. However, the loss of analyte to the syringe filter could be considerable, causing an underestimate of the analyte concentrations. The current study develops and validates simultaneous liquid chromatography-mass spectrometry analysis using a direct filtration method to detect four groups of contaminants of emerging concern. The adsorption of the analyte onto three different matrices and six types of syringe filters is reported. The lowest adsorption of analytes was observed in methanol (16.72%), followed by deionized water (48.19%) and filtered surface lake water (48.94%). Irrespective of the type of the matrices, the lowest average adsorption by the syringe filter was observed in the 0.45 μm polypropylene membrane (15.15%), followed by the 0.20 μm polypropylene membrane (16.10%), the 0.20 μm regenerated cellulose (16.15%), the 0.20 μm polytetrafluoroethylene membrane (47.38%), the 0.45 μm nylon membrane (64.87%) and the 0.20 μm nylon membrane (71.30%). In conclusion, the recommended syringe filter membranes for contaminants of emerging concern analysis are polypropylene membranes and regenerated cellulose, regardless of the matrix used.