Factorial Design Matrix Research Articles

Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example

BackgroundBrain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21–78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23–65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13–48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12).ResultsRegional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11).ConclusionsAs expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation.

Development of a methodology for phosphorus determination in soils, water, and biofertilizer using digital image analysis - DIA

A new methodology based on Digital Image Analysis (DIA) was developed to determine the phosphorus content in different matrices of soil, aqueous standards, and biofertilizers. P was extracted by the Mehlich-1 method (0.05 mol L−1 HCl + 0.0125 mol L−1 H2SO4), with modifications, where the concentrations of complexing agent ammonium molybdate (AMC), bismuth subcarbonate (SBC) and ascorbic acid mass (AAM) were studied for the formation of the blue phosphomolybdic complex. A 23 factorial design and Dohelert matrix were applied for the study, in addition to the optimization of the variables. A light box was developed to optimize the conditions for obtaining images through smartphones, such as exposure to artificial and natural light and the resolution of smartphone cameras. Images were processed using ImageJ and Chemostat to obtain RGB values. Responses were expressed in terms of the logarithmic function of the ratio between the blank signal and the measured signal Log (R0/R), which showed the best sensitivity with good linear correlation (R2 = 0.9795). The methodology was successfully applied to soil, aqueous standards, and biofertilizer samples. The results did not differ statistically from the values found in the application of the conventional spectrophotometric methodology for α = 0.05. The limits of detection and quantification found in the DIA were, respectively, 1.60 and 2.30 mg dm−3. Addition and recovery tests on aqueous standards, resulted in recoveries between 94 and 102%. The developed methodology is suitable for the determination of P in different matrices such as soil, aqueous standards, and biofertilizers using smartphones.

Elucidating the impact of cognitive and behavioral responses to web banner-ad frequency

ABSTRACT The impact of banner advertisement frequency on consumers’ cognitive (i.e., brand recall) and behavioral responses (i.e., click-through) is investigated, and a (4 × 2) between-participants factorial design matrix is developed. Applying logistic regression to a sample of 200 respondents, it was found that frequency of exposure is a powerful psychological cue affecting users’ cognition and behavior. Results indicated that the impact of banner advertisement frequency on both click-through and brand recall generates a response curve similar to a U-shaped curve; however, the effect varies in strength for different types of banner ads. Finally, we discuss theoretical and managerial implications of our research and identify future research directions, and explore the application of advanced AI techniques such as Deep Neural Networks (DNN) in banner-ad frequency.

Studies on effect of wire EDM process parameters on machining characteristics of Inconel 825 plate

Wire-cut Electrical Discharge Machining (WEDM) is an emerging engineering process in the field of micro parts manufacturing to fabricate the complex shapes of micro products. This machining method has been magnificently employed in machining various materials starting from Al metal, a highly ductile material to a very high hard material like ceramics and Inconel’s. Inconel and Ti-based alloys treasure trove wider applications in these present industries. Those parts are producing difficulty in several machining processes and create difficult interactions with machining process parameters. Inconel 825 is a Ni-based strength metal and temperature resistant. Conventional machining of these results in large amounts of cutting forces, more strain-hardened and toughened chips, and extreme tool wear. In this work, an effort is made to study the influence of WEDM process parameters such as Ton, Toff and WF on the output variables such as the Cutting Width of Wire-cut EDM for Inconel 825. Preliminary trials were conducted by varying the process parameters in order to design a process parameter window. Factorial design matrix has been developed with L9 numbers of experiments and the corresponding output/responses such as Kerf Width and surface roughness (Ra) were measured and recorded. The responses have been used to measure the Ra and kerf values of all the specimens after cutting by Wire cut EDM machine. Use of ANOVA using identifies the influenced process parameter on machining (cutting) Inconel 825 output responses Kerf Width and surface roughness.

Characterizing Groundwater Turbidity Reduction by Using a Magnetic Biocarbon Adsorbent Composite (MBAC): Process Optimization

The usage of groundwater as drinking water source in many parts of Kelantan encourages the research and development of various cost-effective alternative adsorbent material for turbidity reduction and drinking water purification. The preparation, characterization, and use of a magnetic biocarbon adsorbent composite (MBAC) is introduced in this study as an option to treat turbid groundwater. In contrast to commercial activated carbon (CAC), peak shifts and peaks denoting Fe-O bending were observed in the FTIR spectrum of MBAC. The adsorption process for turbidity reduction by MBAC and CAC was investigated. A factorial design matrix consisting of four parameters were tabulated, namely, adsorbent dosage (0.02, 0.04, and 0.06 g), agitation time (15, 30, and 60 min), agitation rate (150, 200, and 250 rpm), and two adsorbent particle size ranges (M: 300 < x ≤ 500, and Q: ≤ 45 μm). The predictive model was validated with 0.04 g MBAC of ≤ 45 μm in particle size, agitated at 150 rpm, for 48 min, that attained 98.46% turbidity removal efficiency with a final NTU reading of 0.40. Conversely, CAC removed 88.19% for a final NTU reading of 3.07. Overall, the iron oxide impregnated biocarbon composite showed better turbidity reduction capability compared to CAC. The findings of this work support the potential application of MBAC as an alternative adsorbent for the treatment of groundwater sourced drinking water.

Comparative photo-oxidative degradation of etodolac, febuxostat and imatinib mesylate by UV-C/H2O2 and UV-C/S2O82− processes: Modeling, treatment optimization and biodegradability enhancement

The pharmaceutical contamination in aquatic environment has arisen increasing concern due to its potentially chronic toxicity. In recent years, HO° and SO4°- based advanced oxidation processes (AOPs) have been widely applied in water and wastewater treatments due to their highly efficiency on contaminant removal. Here, the response surface modeling (RSM) was used to investigate the degradation of three typical pharmaceuticals (i.e., etodolac (ETD), febuxostat (FBU) and imatinib mesylate (IMT)) by UV/H2O2 and UV/S2O82− processes. Based on the multiple regression analysis on full factorial design matrix and calculated reaction rate constants, the RSM was built. The experimental rate constants under optimal conditions were quite close to those obtained from the model, implying the good fit of the RSM. In addition, the RSM results indicated that UV/S2O82− process was less sensitive to pH in comparison to the UV/H2O2 process on target contaminant removal. Finally, it showed that UV/S2O82− process was superior to the UV/H2O2 process to on the enhancement of target contaminant biodegradability.

Effect of process parameters on surface composite fabrication of newly commercialized Mg-ZE41 alloy by friction stir processing (FSP): microstructural development and microhardness

PurposeAltering the microstructure and developing the surface metal matrix composites (MMCs) in a solid-state by friction stir processing (FSP) has been on trend for the past decade. The microstructural modification increases the material properties, which are structure sensitive. The microstructural evolution is highly influenced by the selection of process parameters in FSP. In this study, the effect of process parameters on the microstructure evolution and microhardness of the fabrication of surface MMCs of newly commercialized Mg-ZE41 alloy by the incorporation of different reinforcement particles such as ZrO2, CeO2 and Al2O3 is investigated.Design/methodology/approachBy making use of Taguchi’s design of experimentation, which recognizes the crucial factors and ascertain their effect on the properties of the material, the optimization of process parameters for this study was done using MATLAB-14 software. The parameters were adopted along with the levels throughout the FSP for the fabrication of different surface MMCs. For each reinforcement particle, two factors at three levels each had L9 factorial design were used to analyse the effect of these factors on the processing result (microstructure, grain refinement and hardness). The two different factors used in this study are tool rotational speed (TRS) and tool traverse speed (TTS) as a part of the full factorial design matrix for different surface MMCs.FindingsAmong all combinations, TRS of 1500 rpm and TTS of 20 mm/min. for ZE41-ZrO2 MMCs and ZE41-CeO2 MMCs were observed as optimum to produce defect-free processed zone along with the high level of grain refinement and hardness, whereas for ZE41-Al2O3 the same was obtained at 1500 rpm TRS and 10 mm/min TTS.Originality/valueIn this paper, the role of process parameters in the development of surface MMCs on newly commercialized Mg-ZE41 alloy by FSP is investigated. The effect of TRS and TTS on microstructure evolution, grain refinement and microhardness is analysed. Hence, in this study, the optimum parameters for the fabrication of surface MMCs of Mg-ZE41 alloy have thus been established.

Sensitivity analysis of injection parameters in steam assisted gravity drainage under geological uncertainty

Steam-assisted gravity drainage (SAGD) is a challenging enhanced oil recovery technique that requires proper parameterization in terms of well geometry and steam injection parameters as these parameters are highly dependent on the reservoir geology and fluid characteristics (e.g., net pay variation, water saturation). Injection rate and pressure, temperature, steam quality, lean zone, gas zone, water saturation, and wellbore hydraulics are among the most important engineering parameters of SAGD. This work focuses in the sensitivity analysis under geological uncertainty of some operational parameters, such as: injection rate, temperature, steam quality and pressure. To reach this objective we build a realistic reservoir model with multiple geological settings and descriptions. Moreover, two complementary approaches were studied: i) a deterministic sensitivity analysis relying on a factorial design matrix and ii) a stochastic analysis using particle swarm optimization. There are several key geological uncertainties in SAGD, such as net pay variation, inclined heterolithic stratification (IHS) presence, high water saturation in reservoirs, bottom water issues, and gas cap problems. This work focuses on the role of porosity and permeability in oil production, as these impact these geological properties. To reach this objective, two specific groups of ten different porosity and permeability pairs of geostatistical realizations were generated using Direct Sequence Simulation and Co-simulation. Group 1 used the same variogram model for 10 random seeds; and Group 2 used a combination of values that adopted a simultaneous and a random variation, which were assigned to the seed and to the variogram. We opted by a reservoir model that mimics an anticlinal reservoir, as it proved to be a determining factor for the low levels of Net Present Value (NPV) when compared to other geological scenarios. Within the four most commonly studied parameters in SAGD projects, the association of deterministic versus stochastic multi-objective analysis allowed to accurately validate injection rate as the most relevant parameter. Regarding the geological uncertainties, Group 1 showed that for a certain porosity distribution and permeability orientation, the random origin of these distribution maps eventually attributed a higher NPV uncertain. On the other hand, in Group 2, the simultaneous spatial and seed variation provided less uncertainty associated to NPV. • Sensitivity analysis under geological uncertainty of SAGD operational parameters. • Impact of small-scale variability of petrophysical properties in SAGD performance. • Deeper understanding of relevant injection parameters that influence the NPV and CSOR. • Comparison between deterministic statistical analysis and stochastic optimization.

Heat transfer studies of Al2O3/water-ethylene glycol nanofluid using factorial design analysis

The experimental study of the heat transfer coefficient of nanofluid plays a significant role in improving the heat transfer rate of the heat exchanger. A natural convection apparatus was used to study heat transfer in the suspension of Al2O3 nanoparticles in a water-ethylene glycol mixture base fluid. The effects of the heat input, the nanoparticle volume fraction, and the base fluid concentration on the heat transfer coefficient were studied using a 23 full factorial design matrix (16 experimental runs) and the MINITAB Design software. The levels for the heat input, nanoparticle volume fraction, and base fluid concentration were 10 and 100 W, 0.1 and 1 vol.%, and 30 and 50 vol.%, respectively. The residual, contour, 3D surface plots, and Pareto chart were drawn from the experimental results. The observed heat transfer coefficient showed the highest enhancement with the high level of the nanoparticle volume fraction and a moderate enhancement with the high level of heat input, and a slight enhancement with the base fluid concentration.

Factorial Analysis Quantifies the Effects of Pediatric Discharge Bundle on Hospital Readmission.

Factorial design of a natural experiment was used to quantify the benefit of individual and combined bundle elements from a 4-element discharge transition bundle (checklist, teach-back, handoff to outpatient providers, and postdischarge phone call) on 30-day readmission rates (RRs). A 24 factorial design matrix of 4 bundle element combinations was developed by using patient data (N = 7725) collected from January 2014 to December 2017 from 4 hospitals. Patients were classified into 3 clinical risk groups (CRGs): no chronic disease (CRG1), single chronic condition (CRG2), and complex chronic condition (CRG3). Estimated main effects of each bundle element and their interactions were evaluated by using Study-It software. Because of variation in subgroup size, important effects from the factorial analysis were determined by using weighted effect estimates. RR in CRG1 was 3.5% (n = 4003), 4.1% in CRG2 (n = 1936), and 17.6% in CRG3 (n = 1786). Across the 3 CRGs, the number of subjects in the factorial groupings ranged from 16 to 674. The single most effective element in reducing RR was the checklist in CRG1 and CRG2 (reducing RR by 1.3% and 3.0%) and teach-back in CRG3 (by 4.7%) The combination of teach-back plus a checklist had the greatest effect on reducing RR in CRG3 by 5.3%. The effect of bundle elements varied across risk groups, indicating that transition needs may vary on the basis of population. The combined use of teach-back plus a checklist had the greatest impact on reducing RR for medically complex patients.

Thermal conductivity analysis of Al2O3/water-ethylene glycol nanofluid by using factorial design of experiments in a natural convection heat transfer apparatus

Thermal conductivity of a heat transfer fluid plays a significant role in improving the heat transfer performance of a heat exchanger. In this work, experiments were performed in a natural convection heat transfer apparatus by mixing homogenized Al2O3 nanoparticles in a base fluid of water-ethylene glycol mixtures. The effects of heat input, nanoparticle volume content in the base fluid, and ethylene-glycol volume content in the base fluid on thermal conductivity of the nanofluid were analyzed. Based on results obtained by MINITAB? design software (factorial design matrix), 16 experimental runs were performed with the lower and higher levels of input factors. The levels for heat input were 10 and 100 W; for nanoparticle volume content in the base fluid 0.1 and 1 vol.% and for the base fluid composition 30 and 50 vol.% of ethylene glycol in water. From the obtained experimental results, a Pareto chart, normal probability plot, contour plot and surface plot were drawn. Based on the results, a new correlation was proposed, and predictions were compared with the experimental results. From the study, the maximum thermal conductivity value 0.49 W m-1 K-1 was observed at a nanoparticle volume content in the base fluid of 1.0 vol.%, ethylene glycol volume content in the base fluid of 30 vol.% and heat input of 100 W.

Effect of process parameters on petal height and heat generated during bushing formation in form drilling of Al8011

The objective of this study is to identify the most effective parameter among Speed(V), Feed(f), Thickness(d) & Diameter of the tool(D), which give a cylindrical shaped bushing without significant radial fracture or petal formation and also to study the effect of Heat generated during bushing formation in form drilling of Aluminum 8011 work piece coated with Magnesium powder. The contemporary work uses conical Tungsten carbide (WC) tool for form drilling of Aluminum 8011work piece with the help of CNC JV-55 machining centre. Eighteen experiments have been carried out based on the mixed factorial design matrix developed under Taguchi. Process parameters considered are Speed(V), Feed(f), Thickness(d), Diameter of the tool(D) and obtained responses are Torque(T), Heat generation(Q), Tool contact time with work material(t), Temperature(ϕ), Force applied(F), petal height(h), diametrical error(ε). The influential parameter affecting heat generated in form drilling is investigated by using ANOVA and same is confirmed by confirmation test of Taguchi method of analysis. The experimental results show that the process variable speed influence is more followed by feed. It is observed that Mg powder, work piece thickness, tool diameter, speed and feed affects the petal height formation by 2.98, 0.77. 85.85, 1.83 and 1.68 percentage respectively. Among all the process variables tool diameter influence is more, after the tool diameter the influencing parameter is Mg powder.

Optimization of the Effect of Pineapple By-Products Enhanced in Bromelain by Hydrostatic Pressure on the Texture and Overall Quality of Silverside Beef Cut.

Dehydrated pineapple by-products enriched in bromelain using a hydrostatic pressure treatment (225 MPa, 8.5 min) were added in marinades to improve beef properties. The steaks from the silverside cut (2 ± 0.5 cm thickness and weight 270 ± 50 g), characterized as harder and cheaper, were immersed in marinades that were added to dehydrated and pressurized pineapple by-products that corresponded to a bromelain concentration of 0–20 mg tyrosine, 100 g−1 meat, and 0–24 h time, according to the central composite factorial design matrix. Samples were characterized in terms of marination yield, pH, color, and histology. Subsequently, samples were cooked in a water-bath (80 °C, 15 min), stabilized (4 °C, 24 h), and measured for cooking loss, pH, color, hardness, and histology. Marinades (12–24 h) and bromelain concentration (10–20 mg tyrosine.100 g−1 meat) reduced pH and hardness, increased marination yield, and resulted in a lighter color. Although refrigeration was not an optimal temperature for bromelain activity, meat hardness decreased (41%). Thus, the use of pineapple by-products in brine allowed for the valorization of lower commercial value steak cuts.

Effect of moderate hydrostatic pressures on the enzymatic activity and bioactive composition of pineapple by‐products

AbstractThe application of abiotic stresses by moderate hydrostatic pressures (MHP) is still underdeveloped. Abiotic stresses allow activating the enzymatic complexes inducing the synthesis of de novo bioactive compounds. Pineapple by‐products are rich in bromelain and bioactive compounds that can be enhanced through abiotic stresses. The aim of this study was to evaluate the effect of MHP on the enzymatic activity of pineapple by‐products. Pineapple by‐products were submitted to MHP (50–400 MPa between 1 and 15 min) according to a central composite factorial design matrix. Samples were stored at 5 ± 1°C for 24 hr, to allow enzymatic activity to occur. Enzymatic and antioxidant activities and total phenolic compounds (TPC) were quantified. MHP promoted a 262% increase in the phenylalanine ammonia‐lyase activity and 36% increase in TPC, in shell samples. In core the activity of bromelain increased 350%. These results pinpoint the potential to increase the value of pineapple by‐products by enhancing the amounts of bioactive compounds through MHP application.Practical applicationAbiotic stresses can enhance enzyme activity, inducing the synthesis of bioactive compounds in living tissues. Hydrostatic pressure is an innovative nonthermal process that can be used to stabilize or increase enzymes' activity present in by‐products generated in the minimally processed fruit and vegetables industry. Moderate hydrostatic pressure (MHP) act as abiotic stress inducing de novo phenols synthesis and enhancing bromelain activity. After treatment, enriched material could be stabilized and then blended with foods and beverages to improve nutraceutical properties and help in the prevention and treatment of chronic diseases. The study demonstrates that MHP (150–250 MPa) applied to the pineapple core and pineapple shell produce a phenolic and bromelain rich product.

Welding parameters prediction for arbitrary layer height in robotic wire and arc additive manufacturing

In wire and arc additive manufacturing, the weld bead geometry determined the slicing layer height, which was decided by the welding parameters. Generally, the determination of the welding parameters relied on empirical and experimental data through the trial-and-error methods that incur considerable time and cost. To obtain the proper welding process parameters according to the desired single bead geometry and layer height, a full factorial experimental design matrix was applied to collect the original data of welding parameters and bead geometrical variables. A forward artificial neural network (FANN) was built to predict the bead geometry form the welding parameters. Then, a closed-loop iteration method combined a genetic algorithm (GA) and the FANN model (FANN-GA) was developed to search for the most optimal welding process parameters in accordance with the selected bead geometrical variables. The results confirmed that the FANN-GA model has a good performance on the backward prediction of the welding process parameters compared with the direct backward artificial neural network (BANN). Several groups of single layer multi-bead and multi-layer multi-bead experiment were performed to testify the proposed method, and the relative error between the desired and actual layer height was small. The proposed method makes it possible to fabricate the component with an arbitrary desired layer height, and could be used in the adaptive slicing additive manufacturing or surface coating.

Investigation of biodegradable polyaspartate as an effective chelant for washing of lead from soil: response surface methodology approach

ABSTRACT In this work, a biodegradable chelant (polyaspartate) was synthesised by using L-aspartic via polysuccinimide and used for removal of lead from polluted soil. The interactive effects of operating parameters, polyaspartate dose, metal concentration, polyaspartate/soil ratio, pH and extraction time, were examined via 35 factorial face-centred central composite design matrix and response surface methodology. The examined responses for the two models developed were (i) concentration of extracted Pb in polyaspartate solution and (ii) residual concentration of Pb in the treated soil. The optimal removal efficiency was 90%, which yields 18 mg/L of extracted Pb and 50 mg/kg of residual Pb in the treated soil, at conditions of pH 3, extraction time of 6 h under highest level of initial Pb concentration (500 mg/kg), polymer/soil ratio of 25 and polyaspartate dose of 36 mmol/L. However, at the lowest level of polymer/soil ratio of 5, the maximum removal efficiency dwindled to 59% with higher residual Pb and extracted Pb concentrations of 205 mg/kg and 59 mg/L, respectively. The results showed that higher soil washing efficiency was linked to lower initial pH, increase in initial lead ions concentration and polyaspartate/soil ratio. The study signifies eco-friendly polyaspartate as an efficient chelant for remediation of Pb polluted soil via washing technique.

On-line solid phase extraction system using an ion imprinted polymer based on dithizone chelating for selective preconcentration and determination of mercury(II) in natural waters by CV AFS

In the present work is described a new on-line solid-phase extraction system using a novel ion imprinted polymer as adsorbent for the preconcentration and determination of mercury(II) in natural water samples by Cold Vapor Atomic Fluorescence Spectrometry (CV AFS). The ion imprinted polymer was prepared by the improved bulk polymerization technique, occurring in only 3 h, based on mercury(II)-dithizone chelate, using methacrylic acid as monomer, ethylene glycol dimethacrylate as cross-linking reagent, 2,2′‑azobisisobutyronitrile as radical initiator, and dimethyl sulfoxide and acetonitrile as porogen solvents. The obtained polymer was characterized by Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Thermogravimetry (TG), Derivative Thermogravimetry (DTG) and X-ray Diffraction (XRD). The on-line system was mounted by the easy coupling the mini-column packed with the Hg2+-IIP sorbent to the cold vapor system of the spectrometer. The optimum experimental conditions for the on-line preconcentration system were established employing multivariate methodology (two-level full factorial design and Doehlert matrix). Under the optimized conditions, limits of detection and quantification of 0.02 and 0.06 μg L−1, and preconcentration factor of 29 were obtained for the determination of mercury using the developed on-line system, considering a sample volume of 50 mL. Precision expressed as a relative standard deviation (RSD%) was of 5.2% (n = 7) for a standard solution with mercury(II) concentration of 0.5 μg L−1. Then, the on-line system was applied for the selective preconcentration and determination of mercury (II) in four natural water samples, and addition/recovery tests were performed to evaluate the accuracy of the method. All analyzed samples presented Hg2+ concentrations below the limit of quantification of the proposed method and the recovery values found were in range from 90 to 105%.

Cellulose-coated CoFe2O4 nanoparticles as an adsorbent for extraction and preconcentration of bioactive compounds in vinegars

In this work, an extraction and preconcentration flow system using mini-column packed with cellulose-coated CoFe2O4 was developed for the determination of bioactive compounds in vinegar samples by high-performance liquid chromatography with diode array detector (HPLC-DAD). The CoFe2O4 nanoparticles were synthetized by a sonochemical method and coated with cellulose biopolymer. Both synthetized nanoparticles and nanocomposites were characterized employing the techniques of X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Thermogravimetry (TG) and Derivative Thermogravimetry (DTG). The flow system was optimized employing two-level full factorial design and Doehlert matrix, in which the established optimum conditions were: adsorbent mass of 254 mg, eluent concentration of 92% (v/v methanol/water) and sampling flow rate of 2.0 mL min−1. In these conditions, the flow system allows the determination of 12 bioactive compounds with limits of detection and quantification that varied in the range of 0.0009–0.091 mg L−1 and of 0.0028–0.276 mg L−1, respectively. Enrichment factors (EF) were in the range of 2.31–63.72. The proposed flow system was applied for the analysis of 24 vinegar samples of brands locally available in Salvador City, Bahia, Brazil.

Simultaneous Voltammetric Determination of Cd2+, Pb2+, and Hg2+ Ions Using Aminosepiolite-Coated Glassy Carbon Electrode: Optimization of Detection Parameters via Response Surface Methodology

Adsorptive stripping voltammetry using a glassy carbon electrode modified by a film of aminosepiolite was utilized for simultaneous pre-concentration and trace detection of cadmium, lead, and mercury ions in aqueous solution. The modified sepiolite exploited as electrode material was obtained by grafting on its surface of [(3-(2-aminoethylamino)propyl)]trimethoxysilane (AEPTMS). The results demonstrated that the amine groups on sepiolite efficiently affected the voltammetric detection of heavy metals. The full factorial design matrix and response surface methodology were applied in designing experiments, to determine the optimal conditions and to evaluate their mutual interactions. The high values of adjusted R2 obtained of the fitted model show that the experiments data were well explained by the model, which then allowed to acquire optimum parameters for the electroanalysis and detection of the analytes by differential pulse voltammetry. At pH 6.5 of accumulating medium, with an electrolysis potential of –0.9 V and in the concentration range of 10−8 M to 10−9 M, calibration plots were obtained. The limits of detection (3Sd/m) were 8.689 × 10−10 M, 8.197 × 10−10 M, and 8.099 × 10−10 M, respectively, for Cd2+, Pb2+, and Hg2+ ions. The interference effect of several cations and anions on the response of the analytes was also evaluated, and finally, the sensor was applied to the simultaneous detection of metal ions in tap water with satisfactory recovery rates.

Single and Multi-Response Optimization and Validation of Mechanical Properties in Dissimilar Friction Stir Welded AA2219-T87 and AA7075-T73 Alloys Using T-GRA

A study of dissimilar friction stir welding of two aerospace aluminum alloys AA2219-T87 and AA7075-T73 is carried out with five friction stir welding (FSW) parameters namely rotational speed, welding speed, tool profile, tilt angle and position of material. The welding was performed as per the L16 Orthogonal array formed by Taguchi mixed factorial design matrix. Taguchi and Grey Relational Analysis was used for single and multiple response optimizations respectively for optimizing tensile strength, yield strength and percentage elongation. Tensile testing was carried out and analysis was done for getting the optimum setting parameters level. Then the results for L16 was ranked and the optimized parameter set was predicted to be A1B1C2D1E1. Further experimental validation weld was performed and the results correlate with the predicted value. A joint efficiency of 71.8% which is even higher than the predicted values was obtained. Microstructural and hardness studies were performed on the validated welds. Fractography was done to understand the mode of failure occurs in the weld region and most fracture occurs with an offset towards AA2219 side. The presence of intermetallic is found in EDS analysis and XRD further validates the intermetallic composition.