Full Factorial Experimental Design Research Articles

In vitro experimental study on effect of fiber denier, yarn doubling, and specialty treatments on physical and thermal behaviors of knitted sports socks

Knitted sports socks remain in continuous scuff with wearers’ feet and shoes during activities. Such continuous physical contact governs severe pills on socks surface, which is unpleasant for both the wearer and the working life of socks. Hence it becomes necessary to take measures for controlling fabric deterioration, that is, anti-pilling treatments. Though pilling performance is enhanced; however, thermal comfort characteristics are compromised through the treatments. Simultaneous acquirement of both pilling and thermal characteristics is an area of interest. Fiber denier and yarn doubling techniques are among the influential factors in the solution to the problem. Specimens have been developed using four different fiber deniers, two yarn doubling conditions, and two specialty wet treatments under a full factorial experimental design. Results showed a significant variation in pilling behaviors and thermal characteristics before and after treatments in the statistical analysis, predicting the possible stages, that is, a combination of experimental factors for desired characteristics attainment.

Erosion and wear analysis of fly ash filled GFRP composite

The present work involves the solid particle erosion behaviour and the dry sliding wear characteristics of glass fiber reinforced polymer (GFRP) matrix composite using fly ash as filler material is investigated. Five different composite samples (with 0, 5, 10, 15 and 20 wt.% of filler) are fabricated using the traditional hand lay-up method and characterized for their density and hardness properties. Subsequently, the erosion and wear behaviour of the presently prepared composite samples are investigated on air erosion tester and pin-on-disc tribometer, respectively. For the erosion test experiments are performed according to a full-factorial design of experiment (DOE) by considering the hardness of the composite and the impingement angle as the controllable parameters. For conducting the dry sliding wear test, the type of composite, load and speed are taken as input parameters and the experiment is carried out as per Box-Behnken DOE of Response Surface Methodology (RSM). The composite with 15 wt.% of filler depicted the highest hardness value. The wt.% of filler is found to have a significant effect on the erosion and wear behaviour with moderate filler (10 wt.%) providing the optimum result in both cases.

ИССЛЕДОВАНИЕ СТРУКТУРЫ И МЕХАНИЧЕСКИХ СВОЙСТВ ОБРАЗЦОВ, ПОЛУЧЕННЫХ МЕТОДОМ СЕЛЕКТИВНОГО ЛАЗЕРНОГО СПЛАВЛЕНИЯ ИЗ МЕТАЛЛИЧЕСКОГО ПОРОШКА ЖАРОПРОЧНОГО СПЛАВА ВЖ-159 (ХН58МБЮ)

This paper presents the results of determining the optimal parameters of selective laser melting (SLM) of the metal powder of the high-temperature alloy VZh-159 (KhN58MBYu). Empirically and using mathematical methods for planning and analyzing experiments to determine the effect of technological parameters of fusion on the tensile strength of the material, rational SLM modes have been established, namely, laser emission power, hatching, scanning speed. The laser power P (X1) and scanning speed v (X2) were chosen as the main variable parameters of the full-factor experimental design, and the tensile strength σB, MPa (Y), was presented as a dependent variable on the selected factors. The mechanical properties of the samples produced by the SLM were determined, which also depended on the volumetric energy density, which is equal to the ratio of the laser power to the product of the scanning speed, hatching, and layer thickness. During the experiment, it was possible to obtain an acceptable quality indicator - mechanical properties, for a certain range of technological parameters of SLM. A study of the microstructure of the material of produced samples of the VZh-159 (KhN58MBYu) heat-resistant alloy was carried out, and chemical and granulometric analysis of the initial powder was carried out.

Synthesis of biochar-CoFe2O4 nanocomposite for adsorption of methylparaben from wastewater under full factorial experimental design.

The presence of endocrine-disrupting chemicals in municipal wastewater has emerged as a threat to human health and the environment. Therefore, this study aimed to develop biochar-cobalt ferrite (BCF) nanocomposite for the removal of methylparaben from water under the full factorial experimental design of 4 factors with 3 levels (34). The biochar-CoFe2O4 nanocomposite was developed by co-precipitation method from cobalt ferrite and biochar of Eucalyptus tree bark. Adsorbent surface morphology and functional and elemental composition were carried out by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and energy-dispersive X-ray spectroscopy (EDS) techniques which showed the presence of cracks with a rough surface, reasonable surface chemical composition, and many chemical functional groups, respectively. The experimental and predicted adsorption efficiencies ranged from 25.3 to 85.6% and 21.8 to 80.3%, respectively. The maximum adsorption performance (85.6%) reduced the methylparaben concentration from 27.5 to 4.0mg/L at the optimum condition of adsorbent dose of 55mg/100mL, pH 6, contact time 90min, and the initial methylparaben concentration of 27.5mg/L. However, the adsorbent dose was the most influential main factor whereas the least influential was the interaction between solution pH and contact time under the regression model. The model also showed that 69% methylparaben removal was described by the regression model. The experimental data best fitted with the Freundlich model indicate multilayer adsorption which is the implication of physisorption. The sorption mechanism is attributed to Vander Waals forces, H-bonding, and dipole interaction. This BCF nanocomposite adsorbent appears to be promising for the removal of methylparaben from wastewater, but a further optimization process is essential to boost the treatment performance.

An enhanced solution approach for multi-period discrete manufacturing product assembly inventory routing problem with supply failure risks and customer demand uncertainty

PurposeThis study primarily aims to develop and solve an enhanced optimisation model for an assembly product multi-period inbound inventory routing problem (IRP). The many-to-one (inbound) model considers the bill of materials (BOM), supply failure risks (SFR) and customer demand uncertainty. The secondary objective is to study the influence of potential time-dependent model variables on the overall supply network costs based on a full factorial design of experiments (DOE).Design/methodology/approachA five-step solution approach is proposed to derive the optimal inventory levels, best sourcing strategy and vehicle route plans for a multi-period discrete manufacturing product assembly IRP. The proposed approach considers an optimal risk mitigation strategy by considering less risk-prone suppliers to deliver the required components in a specific period. A mixed-integer linear programming formulation was solved to derive the optimal supply network costs.FindingsThe simulation results indicate that lower demand variation, lower component price and higher supply capacity can provide superior cost performance for an inbound supply network. The results also demonstrate that increasing supply capacity does not necessarily decrease product shortages. However, when demand variation is high, product shortages are reduced at the expense of the supply network cost.Research limitations/implicationsA two-echelon supply network for a single assembled discrete product with homogeneous vehicle fleet availability was considered in this study.Originality/valueThe proposed multi-period inbound IRP model considers realistic SFR, customer demand uncertainties and product assembly requirements based on a specific BOM. The mathematical model includes various practical aspects, such as supply capacity constraints, supplier management costs and target service-level requirements. A sensitivity analysis based on a full factorial DOE provides new insights that can aid practitioners in real-life decision-making.

From Simulations to Accelerated Testing: Design of Experiments for Accelerated Load Testing of a Wind Turbine Drivetrain Based on Aeroelastic Multibody Simulation Data

The trend of increasing the power output and nominal load capacities of wind turbines (WT) over time has been driving the construction of testing facilities with increasing load capacities for testing WT drivetrain components prior to field deployment. Due to the high investment and operational costs of such facilities, a need exists to design accelerated tests that cover load situations corresponding to expected field conditions while maintaining high time-efficiency. This investigation addresses this need by presenting a methodology to achieve the following goals. Firstly, identifying ranges and combinations of WT 6-degree of freedom (6-DOF) rotor loads is to be expected in the field. This is achieved using aeroelastic multibody simulations (MBS) of an MBS WT model being subjected to simulated wind fields covering the design load cases outlined in the IEC 61400-1 standard and by analyzing the simulated time-series data to design accelerated tests that efficiently and realistically cover the design space of the variables, e.g., 6-DOF rotor loads, to be applied during WT drivetrain testing. The designed tests are to take place on a purpose-built test rig that allows for the application and control of the 6-DOF drivetrain input loads and rotational speed. Using the proposed method, accelerated tests were designed that efficiently cover load combinations within the realistic regions of the design space. A comparison with a full factorial design of experiments shows a significant (95+ %) reduction in total test time as well as the ability of the proposed method to help to avoid unsustainable and unrealistic load conditions within the design space that could result in costly, unintended drivetrain failures during testing.

Dissolution Enhancement of Indomethacin through Binary and Ternary Solid dispersions and Inclusion Complexes – A Comparative Evaluation

Dissolution of drugs is a prerequisite in the development of their oral dosage forms with satisfactory bioavailability. In this investigation, solid dispersion of the model drug indomethacin in gelucire (50/13) and inclusion complexation in sulfobutyl ether beta cyclodextrin are employed to enhance the dissolution. Fast dissolving products (F1-F8) of indomethacin are prepared by different methods such as solvent evaporation, freeze drying and kneading and comparatively evaluated. The ternary and binary systems of the products are prepared and evaluated for their efficiency in increasing the dissolution. The dissolution efficiency values (DE30) were found to be higher for solid dispersions (73.34%) and inclusion complexes (53.12%) than the pure drug indomethacin (3.32%). The x ray diffraction and differential scanning calorimetry studies revealed that the crystalline drug is converted into an amorphous form in the dispersions and inclusion complexes. Soluplus and gelucire are employed as ternary components in the solid dispersions and inclusion complexes respectively and are found to be resulting in ternary systems which were found to be superior to the binary systems. A 22 full factorial experimental design in order to investigate the combined influence of method of preparation and nature of the product. It was observed that the method of preparation and nature of the product (a ternary system or a binary system) will influence the extent of improvement of dissolution of the drug.

Additive manufacturing and investigation of shape memory properties of polylactic acid/thermoplastic polyurethane blend

The fused filament fabrication (FFF) process is one of the most widely used additive manufacturing (AM) methods in producing parts with relatively complex geometry due to its low cost and high capability. Using this blend along with the 3D printing method to make complex shapes can have a high potential in medical applications such as stents, so the goal of the following study is to investigate the shape memory properties of Polylactic Acid/Thermoplastic polyurethane (PLA/TPU) blend. The printed samples with 100/0, 80/20, and 60/40 PLA/TPU composition ratios were comprehensively evaluated based on their structural, thermal, and shape memory properties. A differential scanning calorimetry (DSC) test was conducted to determine the glass transition temperatures and crystallization temperature zone of the blends. A full factorial design of experiments was employed to investigate the effect of three variables called composition ratio, printing angle, and filling percentage on shape memory behavior. Using the three-point flexural test, the shape memory behavior of printed samples with composition ratios 100/0,80/20, 60/40, and filling percentages of 50, 75, and 100%, along with printing angles of zero, ±45, and 90°, was evaluated and measured. The annealing process increased the force required to change the shape and crystallization percentage, such that not being processed, no crystals were observed in the X-ray diffraction (XRD) analysis of the samples, while after being annealed, up to 38% of the crystals were formed. Increasing the percentage of TPU and temperature led to the softening of the samples, reducing the strength and elastic modulus, as well. Enhancing the percentage of TPU also decreases the hydrophilic properties of the samples. The results showed that the best shape memory performance is related to the 20 weight percentage of TPU composition, and the maximum recovery force ratio belongs to the sample with such a composition, zero printing angles, and 75% filling percentage, which equals 81.56%, while it equals 74% using compression molding (CM) in all three production blends.

Processability of mesoporous materials in fused deposition modeling for drug delivery of a model thermolabile drug

The incorporation of drug-loaded mesoporous materials in dosage forms prepared with fused deposition modeling (FDM) has shown the potential to solve challenges relating to additive manufacturing techniques, such as the stability of poorly-soluble drugs in the amorphous state. However, the addition of these non-melting mesoporous materials significantly affects the mechanical properties of the filament used in FDM, which in turn affects the printability of the feedstock material. Therefore, in this study a full-factorial experimental design was utilized to investigate different processing parameters of the hot melt extrusion process, their effect on various mechanical properties and the potential correlation with the filaments' printability. The thermolabile, poorly-soluble drug ibuprofen was utilized as a model drug to assess the potential of two mesoporous materials, Mesoporous Magnesium Carbonate (MMC) and a silica-based material (MCM-41), to thermally protect the loaded drug. Factorial and principal components analysis displayed a correlation between non-printable MCM-41 filaments and their mechanical properties where printable filaments had a maximum stress >7.5 MPa and a Young's modulus >83 MPa. For MMC samples there was no clear correlation, which was in large part attributed to the filaments' inconsistencies and imperfections. Finally, both mesoporous materials displayed a thermal protective feature, as the decomposition due to the thermal degradation of a significant portion of the thermolabile drug was shifted to higher temperatures post-loading. This highlights the potential capability of such a system to be implemented for thermosensitive drugs in FDM applications.

Performance comparison between internally cooled tools and flood cooling during grey cast iron turning

High heat generation with the consequent increase in temperature is still a limiting factor for productivity and quality in machining processes, mainly because it strongly affects the tool's life and the quality of the workpiece. Cutting fluids in abundance (CFA), often named flood cooling, is the standard machining cooling technique used industrially. However, CFA poses significant environmental hazards while incurring mounting manufacturing costs. To address this, the current paper focuses on a comprehensive performance evaluation of a novel internally cooled tool (ICT) while machining grey cast iron - GCI. Designed and built in-house, the ICT system can internally circulate the coolant through a specifically modified insert, effectively removing the heat from the interface. The machining tests followed a full factorial Design of Experiment- DoE (23) with two quantitative input variables, the depth of cut (doc) (1.0 and 2.0 mm) and the cutting speed (vc) (100 and 150 m/min) and one qualitative variable, the cutting atmosphere (ICT or CFA). The feed rate was maintained constant at 0.1 mm/rev. The response variables were cutting force, tool wear mechanisms, and surface integrity in terms of roughness, microhardness, and microstructure. The main results indicated that, compared to CFA, ICT increased cutting force by 42 %. ICT also increased peak microhardness by 22 % at 500 μm from the top surface. It also induced grain modification at 4 mm depth, indicating that ICT caused a work-hardening effect in the subsurface. These results were an indication that ICT could effectively take heat away at the cutting zone. Statistical analysis on surface roughness showed that the significant variables were the cutting speed and its interaction with the atmosphere, where increasing the cutting speed was the dominant parameter. ICT reduced the roughness at higher cutting speeds, as opposed to CFA. The predominant wear mechanisms were adhesion and abrasion, along with plastic deformation for both ICT and CFA. Finally, the ICT system showed to be a promising eco-friendly technique with high cooling capacity, presenting some advantages compared to CFA, with similar machining performance.

Analysis of material variation in the design of knitted sportswear compression stockings to escort thermo-physiological comfort using linear regression

Compression sportswear is specialized clothing chiefly designed for the athletes muscle fatigue reduction. Compression stockings are widely used in sportswear to support muscles and control excessive body vibrations. Compression/interface pressure between the wearer and stocking’s surface is considerable in compression sportswear efficacy. However, thermo-physiological comfort is also influential while preparing summer sportswear. The research investigates the effect of fibers type and the inlay yarns number of covering filaments on compression, air permeability, water vapor permeability, and thermal resistance of sportswear compression stockings. Three different fibers, sorbtek (channelized polyester), nylon 6, and micro Nylon (polyamide-6), have been used as main yarns. Also, three distinct levels of covering yarn number of filaments have been utilized. Data have been statistically analyzed using full factorial design of experiment and regression analysis to predict fiber type and the number of covering yarn filaments for achieving desired comfort properties without compromising compression characteristics.

Transformer oil vs. kerosene: Selective collectors for C-matter flotation from a double refractory gold ore

A study was performed to evaluate the effect of collector type on the flotation of carbonaceous matter (C-matter) from a refractory gold ore, with the objective of improving gold recovery through separate treatment of the flotation concentrate and tailings. Transformer oil and kerosene were investigated as C-matter collectors in a single rougher flotation stage. Both collectors were tested at varying dosages, with emulsification (‘emulsion’) and without emulsification (‘normal’) using a 2-level full factorial Design of Experiments (DOE). The kerosene emulsion at 500 g t-1 resulted in the highest C-matter recovery (66%) with a C-matter grade of 4.38% (∼2.4 times upgrade), showing improved flotation performance compared with normal kerosene. Normal transformer oil at 500 g t-1 resulted in a C-matter grade of 4.65% with 61% C-matter recovery, and emulsion of transformer oil had little effect on flotation performance. The finer droplet size in the kerosene emulsion resulted in a faster flotation rate, leading to higher C-matter recovery than both transformer and normal kerosene. However, FT-IR analysis demonstrated that the interaction of transformer oil with C-matter was different than kerosene, forming a stronger attachment to C-matter surfaces. The flotation concentrate obtained under the most promising conditions (kerosene emulsion 500 g t-1) contained 51 % of gold, while 49 % of the gold reported to the flotation tailings. The gold recovery models predicted that gold recovery from (1) roasting/cyanidation of the flotation concentrate and, (2) pressure oxidation/thiosulfate leaching of the flotation tailings, has the potential to improve the overall gold recovery from 44% to 60% (compared with pressure oxidation/thiosulfate leaching only). In its first application as a collector in C-matter flotation, transformer oil showed great potential despite the highly disseminated, complex nature of the ore. The results suggest that transformer oil may be applicable in other flotation systems, beyond the gold industry, including agglomerate flotation and the flotation of carbonaceous battery materials, such as graphite, from both primary and secondary resources.

Aquatic toxicity of tire microplastics on marine and freshwater organisms: An in silico approach

Tire wear particles are a notable source of tire microplastics (TMPs) in the environment. However, only a few reports have focused on the aquatic toxicity effects of composite TMPs and their additives and the mechanistic analysis at the microscopic level. Therefore, this paper study the toxic effects of tire microplastics and their additives on zebrafish based on theoretical chemical calculation method (Taguchi orthogonal experiment method, full factorial experimental design, molecular docking, and molecular dynamics computational technique). We designed five kinds of proportioning schemes of tire rubber polymers and additive components (64 groups in each). The compound toxicity effects of the tire rubber polymers and their additives on zebrafish were simulated and calculated. The simulation results indicated styrene butadiene rubber had the most significant toxic effect on zebrafish. Subsequently, taking the composition ratio scheme of styrene butadiene rubber with the lowest biotoxicity effect as an example, we analyzed the main effects, second-order interactions, and third-order interactions of styrene butadiene rubber polymer and its additive combination in terms of biotoxicity using the fixed effects model. The toxic effects (developmental toxicity, neurotoxicity, and reproductive toxicity) of styrene butadiene rubber on marine and freshwater organisms could be drastically alleviated by adjusting the ratio of rubber additives. The analysis of the interaction between amino acid residues and non-bonds during the docking process of styrene butadiene rubber and toxic receptors revealed the interaction mechanisms between the styrene butadiene rubber polymer and its additives and between the additive molecules. Hydrophobic interaction was found to be the key factor for the binding of styrene butadiene rubber additives to nonpolar amino acids in the receptor proteins. Our findings are expected to provide theoretical support for identifying and regulating the toxicity characteristics of rubber TMPs and to aid in proposing a strategy to alleviate the toxic effects on aquatic organisms.

Enhanced large-scale production of recombinant phytase in E. coli DH5 α: Medium components optimization and thermodynamic studies

Medium components and their solubility for improvement of recombinant phytase production in Escherichia coli DH5α. was optimized and reported in this study. Solubility study was supported by the computational works conducted using COSMO-RS, a quick tool to predict the thermophysical and chemical properties of fluid mixture. Solubility concept through thermodynamic calculation was introduced to study the interaction between medium components with water and selected medium components with each other. Based on the σ-profile and σ-potential, together with Gibbs free energy, a less soluble medium component showed a negative effect on phytase production due to the formation of insoluble metal-ion phytate complexes. Highest phytase production was obtained under optimum conditions of as 30 g/L yeast extracts, 1 g/L (NH4)2HPO4, 3 g/L MgSO4·7H2O, 0.3 g/L FeSO4·7H2O and 0.1 % (v/v) glycerol. Both experimental and computational works were conducted to investigate the interaction between medium components. The melting point and phase appearance of the mixture were studied. From experimental results, the mixtures were seemed to be unstable due to its higher melting point compared to individual medium components. The analysis from Gibbs free energy reflected the total immiscibility between medium components. It was concluded that medium components worked individually to promote the phytase production. Yeast extract, MgSO4·7H2O and FeSO4·7H2O were selected for optimization by central composite design, whereas other factors were maintained at their optimal level. Statistical analysis showed that the optimum media containing 34.06 g/L yeast extract, 3.6 g/L MgSO4·7H2O and 0.32 g/L FeSO4·7H2O gave the maximum phytase production of 118.91 U/mL. The optimization of process condition such as temperature, agitation speed and seed age were further investigated by employing Full Factorial Experimental (FUFE) Design after fixing the media composition. The maximum phytase production of 200 U/mL was obtained at optimum condition (temperature 37 °C, agitation speed 500 rpm and seed age 1.55 at OD600nm). The phytase production in a larger scale was conducted by submerged fermentation in 2 L and 30 L bioreactor. In large scale production, constant tip speed suited the best for scale-up strategy comparing to the constant power number. The optimum process condition improved the phytase production and plasmid stability.

Final surface modification for better wear resistance of ceramic coating on cast AlSi10Mg alloy

Using the design of experiment (DOE) method and the micro-arc oxidation (MAO) technique, ceramic layers on AlSi10Mg alloy were systematically prepared to design optimal process conditions for achieving the best tribological properties of the ceramic layers. The lowest concentrations of the applied 6 g/l NaOH and 12 g/l Na 2 SiO 3 resulted in the preparation of uniform MAO layers with the lowest rated parameters R a , R z and thickness achieved under micro-arc discharge conditions at 500 V and 60 min. With the increasing thickness of the coatings, there was an increase of Si in the MAO coating. Full factorial DOE was used to optimize the tribological properties in a polyalphaolefin (PAO) environment at 80 °C. The most significant influence for the preparation of abrasion-resistant layers for the investigated factors was identified on the AlSi10Mg alloy by the NaOH content in the electrolyte. The friction coefficients of MAO coatings reached an average value of 0.15. Aero-lap polishing technology was applied for increased wear resistance requirements to eliminate the deficiencies of MAO coatings, leading to decrease wear track by almost double compared to polished silumin. Removal of the outer MAO layer by polishing led to a reduction in the high corrosion resistance of the MAO coating, demonstrating the influence of the outer layer not only on the tribological properties but also on the corrosion resistance of MAO coatings.

Multi-objective optimization of detergent pre-formulations using machine learning techniques

The right combination of surfactants and stabilizers in the detergent formulations plays a significant role in their cleaning performance. However, it becomes a complex optimization problem when the formulation is composed of multiple ingredients and the solution has to be optimized for competing performance metrics. In recent times, machine learning techniques have been used extensively to study such processes. In this research, a detergent pre-formulation has been designed using an aqueous solution of Tween-20, Ethanol and 1-Octanol. To determine the optimal values of the ingredients of the formulations, supervised machine learning models were developed and optimized for the Ross Miles Index 30 ml (RMI 30) and cleaning time (CT). A full factorial experimental design was performed and three regression models based on linear, 2FI and Quadratic designs were developed respectively for RMI30 and CT. ANOVA analysis of trained models reported an optimal p-value of 0.0018 for RMI 30 and less than 0.0001 for CT. The optimal values for RMI30 and CT obtained through regression models are 72.32 ml and 17.67 s. For multi-objective optimization, grey relational analysis was performed. Two pairs of optimal values corresponding to Rank 1 were recorded as 88.9 ml, 20 s (RMI30, CT); and 81.2 ml, 14 s (RMI30, CT) respectively. As a result, the optimal combination of Tween-20, Ethanol and 1-Octanol for maximizing the RMI30 and minimizing the CT are reported. The obtained optimal values were experimentally validated.

Microstructural and Statistical Investigation on Dissimilar Friction Stir Spot Welding of Austenitic and Ferritic Stainless-Steels

The increasing use of austenitic and ferritic stainless steel sheets, especially in the power plant industry, highlights the importance of research into the mechanical and metallurgical properties of these joints. Friction stir spot welding of these sheets was investigated using full-factorial experimental design. Considering the three main parameters of rotational speed, plunge depth and dwell time, each of which had three levels, led to 27 tests. The highest strength (6400 N) was related to sample number 8 with parameters 900 rpm, 2.7 mm and 20 s and this sample was selected for microstructural studies. The metallurgical properties of this sample were investigated and the microstructures of heat affected zone, thermos-mechanically affected zone and stir zone were extracted. Thermomechanical changes during the process and especially dynamic recrystallization in the stir zone due to heat application and severe plastic deformation by the process reduce the grain size, which in turn increases the bond strength. Using analysis of variance, it was found the direct effect of rotational speed and plunge depth on the lap shear strength is significant. The interaction effect of rotational speed and plunge depth and also, rotational speed and dwell time on the response were significant.

Implementation of Experimental Design Techniques to Optimize Immunoglobulins Detection with Ultrasensitive Sandwich Immunoassays (Adv. Sensor Res. 1/2022)

Inside Front Cover Ultrasensitive Sandwich Immunoassays In article number 2200009, Eleonora Macchia and co-workers study a full factorial experimental design that is undertaken to optimize a sandwich chemiluminescent immunoassay, leading to a reduced experimental effort and increased quality of the information obtained with respect to one-variable-at-a-time approaches. The optimization leads to an immunoglobulin M limit-of-detection of 4 fM, reducing the involved antibody concentrations by one order of magnitude and dramatically reducing the assay costs.

Optimization of EDM Machinability of Hastelloy C22 Super Alloys

In this study, machinability tests were carried out on a corrosion-resistant superalloy subjected to shallow (SCT) and deep cryogenic treatment (DCT) via electrical discharge machining (EDM), and the effect of the cryogenic treatment types applied to the material on the EDM processing performance was investigated. Experimental parameters, including pulse-on time (300, 400 and 500 μs), peak current (A) (6 and 10 A) and material types (untreated and treated with SCT and DCT), were used to construct the full factorial experimental design. The resulting average surface roughness (Ra) and material removal rate (MRR) results were optimized using the Taguchi L18 method. According to the Taguchi-based gray relational analysis, the optimal parameters for both Ra and MRR were determined as cryogenic treatment, pulse-on time and peak current, respectively. The response table obtained using the Taguchi method showed the most effective factors as A1BlC3 for Ra and A2B2C1 for MRR values. According to the ANOVA results for determining parameters affecting performance, peak current was the most effective factor for average surface roughness and MRR, at 74.79% and 86.43%, respectively. When examined in terms of Taguchi-gray relational degrees, the optimal parameters for both Ra and MRR were observed in the experiment performed with the SCT sample at a peak current of 6 A and 300 μs pulse-on time.

Full factorial optimization of α-aminophosphonates synthesis using diphenylphosphinic acid as efficient organocatalyst

Diphenylphosphinic acid was used as an efficient and simple catalyst for the synthesis of the α-aminophosphonates by multicomponent Kabachnik-Fields reaction in one pot of aromatic aldehyde, aniline and diethylphosphite. Three physicochemical factors including catalyst amount, reaction time and medium temperature were optimized using a full factorial experiment design (FFD). Additionally, a quadratic polynomial regression model was applied for the analysis of the experimental data at a confidence level of 95% with p-values < 0.05. The high signification effect of the reaction time and the medium temperature on the α-aminophosphonates synthesis were confirmed by the statistical analysis. Besides, the diphenylphosphinic acid amount showed an effect on the reaction yield. ANOVA exhibited that the coefficient determination of this model up to 99.25%. This eco-friendly procedure was extended for the preparation of series of the α-aminophosphonates in ethanol as green solvent, giving the desired products with high chemical yields up to 90%.