Full Factorial Experimental Design Research Articles

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

The friction stir spot welding (FSSW) process is a novel technique that overcomes the limitation of resistance spot welding. Recently, FSSW used for welding of polymers which are difficult to be joined by traditional welding processes. The demand for Acrylonitrile Butadiene Styrene (ABS) for industrial applications has increased in recent years. However, to employ this technique the challenge is to get optimal FSSW parameters setting to achieve the best weld strength during the welding of ABS sheets. To achieve this, in the present work, full factorial experimental design layout was employed to investigate the effect of process parameters on weld strength i.e., ultimate tensile strength (UTS) and percentage elongation during FSSW of ABS-ABS sheet in butt configuration. To predict the UTS and percentage elongation, machine learning regression namely, linear, polynomial, support vector machine, and decision tree was used. Further, the study includes the identification of the fracture patterns post tensile test specimens based on the topography of the fracture surface under scanning electron microscopy. It was found that plunge depth is the most significant parameter followed by spindle speed and dwell time. The optimal setting of process parameters i.e., spindle speed of 1000 rpm, plunge depth of 1 mm, and dwell time of 40 s resulted in maximum UTS of 7.849 MPa. The maximum value of percentage elongation obtained was 5 at the parameter setting of spindle speed of 1000 rpm, plunge depth of 0.8 mm, and dwell time of 40 s. Polynomial regression outperformed in the prediction of UTS and percentage elongation with an R-square of 0.99.

Finite element analysis of irregular porous scaffold for bone tissue engineering

The structure of natural bone, particularly the trabecular structure, is irregular in the microscopic structure. Some related research had demonstrated that irregular porous structures like cancellous were better for cell growth. The correlations between irregular porous structure design parameters and porosity, average pore size, and associated mechanical properties are also complicated. This research uses finite element analysis to analyze irregular porous scaffold models of PLA/HA composite mechanical characteristics. The factors for the design parameters investigated in this study are strut diameters (SD), composite materials (M), and loading orientation (D). A full factorial design of experiments (FFD) was used to determine the optimal mechanical parameters of structure scaffold design. The results of our study illustrated that composite materials and strut diameter are essential factors for compressive strength and elastic modulus. Still, the strut diameter was the most critical variable in this simulation study. This study found that variation of loading direction at all irregular porous scaffolds in this research does little to affect compressive strength and elastic modulus. The optimization method showed that the desired results are obtained with the Z loading direction, 30% hydroxyapatite composition, and 0.65 mm strut diameter. Maximum compressive strength and elastic modulus were calculated to be 25.93 MPa and 1295.24 MPa, respectively. These results suggest that the bone scaffold elastic modulus and compressive strength are comparable to the trabecular bone but less than cortical bone.

Experimental Analysis of the Influence of Heat Treatments on the Flexibility of NiTi Alloy for Endodontic Instruments Manufacturing.

The flexibility of NiTi based endodontic files is improved by heat treatment, leading to lower risk of failure, ledges, and canal transportation during the preparation of curved root canals. The aim of this study is to investigate and clearly highlight the influence of every parameter of heat treatment on the flexibility of NiTi wires and thus of endodontic instruments. A full factorial Design of Experiment (DoE) and a designed bending-torsion bench following the ISO 3630-1 standard were used for this investigation. Temperature, holding time, and cooling method were selected as contributing factors, while maximum bending moment, hysteresis size, and stiffness during martensitic transformation were selected as outputs. Regression analysis was performed to estimate the relationship between contributing and output variables to assess how the experimentation fits with the model. The experimental results showed that wires heated at 425 °C for 30 min are more flexible. Moreover, heat treatment temperature is the most critical factor influencing the flexibility and hysteresis size of the NiTi wire followed by the holding time, while the cooling method has a negligible effect. The regression analysis showed that the model is effective at predicting the relationship between contributing factors, bending moment response, and hysteresis size.

Rapid and inexpensive voltammetric detection of ochratoxin A in wheat matrices

Produced as toxic metabolites by fungi, mycotoxins, such as ochratoxin A (OTA), contaminate grain and animal feed and cause great economic losses. Herein, we report the fabrication of an electrochemical sensor consisting of an inexpensive and label-free carbon black-graphite paste electrode (CB-G-CPE), which was fully optimized to detect OTA in durum wheat matrices using differential pulse voltammetry (DPV). The effect of carbon paste composition, electrolyte pH and DPV parameters were studied to determine the optimum conditions for the electroanalytical determination of OTA. Full factorial and central composite experimental designs (FFD and CCD) were used to optimize DPV parameters, namely pulse width, pulse height, step height and step time. The developed electrochemical sensor successfully detected OTA with detection and quantification limits equal to 57.2 nM (0.023 µg mL−1) and 190.6 nM (0.077 µg mL−1), respectively. The accuracy and precision of the presented CB-G-CPE was used to successfully quantify OTA in real wheat matrices. This study presents an inexpensive and user-friendly method with potential applications in grain quality control.

Developing and modelling of sustainable protein extraction using ammonium hydroxide – A recoverable and reusable solvent

Plant protein demand is growing as more people adopt a healthier and sustainable lifestyle. Since plant proteins are cell wall protected, extraction process that breaks cell-wall barrier is needed to obtain protein. However, the conventional extraction methods use non-reusable and non-recoverable solvents. Therefore, the goal of this research is to develop and model sustainable protein extraction using NH4OH—a recoverable and reusable solvent. Soybean protein was extracted at a temperature of 55 °C, varying particle sizes (>1, 0.425 – 1, 0.25–0.425, and<0.25 mm), times (0, 3, 6, 12, and 48 h), NH4OH concentrations (1%, 2.5%, 5%, 10% and 15%) and solid loading (1:10) using a full factorial experimental design. The empirical kinetic models for soybean protein extraction were analyzed with parabolic diffusion model, So & MacDonald’s model, hyperbolic, and first order model. The highest protein yield of 59% was obtained at 0.425 – 1 mm particle size, 12 h, and 1% NH4OH. Amine concentrations of extracted proteins were poorly correlated with protein yield evincing that extraction conditions to obtain the high yield did not cause protein hydrolysis. Three out of the four models gave a good fit. The best fit was So & MacDonald’s model, with a minimum root mean square error (RMSE) of 0.14 and maximum coefficient of linear correlation (R) of 0.89. In conclusion, NH4OH is a viable and sustainable solvent to extract plant protein and can be utilized for industrial plant protein production.

Boron removal from wastewater by direct contact membrane distillation system using two-level factorial design

Numerous industries use boron and its compounds in various applications. In this study, the removal of boron from an aqueous solution by direct contact membrane distillation (DCMD) system. A 23 full factorial experimental design was implemented to determine the optimum values and the degree of importance of the parameters. The experiments were performed at low and high levels of temperature, initial pH, and feed boron concentration. Design variables were evaluated in terms of individual, binary, and triple effects. At the 95% confidence level, the temperature–initial pH interaction was found as the most significant factor. The maximum boron removal efficiency can be achieved either at a low level of temperature (30 °C) and pH (4.0), or a high level of temperature (50°C) and pH (11) combinations at 20 mg/L boron concentration with desirability of 0.957.

Experimental study of wax-material support structure for fused deposition modeling printed parts with overhanging sections

Additive manufacturing of PLA parts using wax-based filament as a support structure was investigated by implementing the 2-level 3-factors full factorial design of experiments (DoE). The aim of the experiments was to identify significant FDM factors that affect the surface roughness of the parent part in the PLA/Wax interfacial region. In addition, the important factors influencing bonding strength between PLA and wax were identified using the same full factorial DoE. The printing parameters chosen for the analysis are printing temperature, nozzle speed, and the pattern type at the interfacial area. The findings of the study show that the wax-based filament can be effectively used as support material with the condition that the PLA printing temperature is at its lowest value and the nozzle speed is close to 40 mm/s. With the optimal printing condition, the surface roughness value (Ra) can reach as low as 18 μm value and the bonding strength of 0.27 MPa at the interfacial area. The results of the study have revealed the capability of FDM technology to join materials having considerably dissimilar properties.

Selection of infill parameters considering their effects on operational and mechanical performance for material extrusion of CFR-PEEK

PurposeAs newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.Design/methodology/approachA full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.FindingsThe results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.Originality/valueThe findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

Machine learning-aided characterization of microbubbles for venturi bubble generator

The characterization of microbubbles for venturi tube is important for the associated industrial applications, but still challenging due to the coupling effects of numerous operating factors. Here, we report a machine learning (ML)-aided approach for predicting the characteristics of microbubbles generated by venturi tube. Full factorial design of experiments (DOE) was first carried out, followed by the image post-processing to obtain multi-dimensional dataset. After data cleaning, MLP (Multi-Layer Perception), random forest (RF) and Catboost models were trained to correlate the Sauter mean diameter (ds) to five operating features, namely, throat-to-outlet ratio β, divergent angle θ, gas-to-liquid ratio α, gas Reynolds number Reg and liquid Reynolds number Rel. All three ML models provide excellent predictability on ds, while the Catboost model displays the best extrapolation performance in three investigated scenarios. Internal importance analysis shows that the throat size and Reg play the greatest and least influence on ds, respectively. We also explored the mathematical fitting approach based on obtained experimental dataset. The results show that ML models deliver improved predictive performance over mathematical model, but the latter provides better mechanistic interpretability. This work demonstrates the great potential of ML in the gas–liquid multiphase flow.

A Comparative Review of Air Drones (UAVs) and Delivery Bots (SUGVs) for Automated Last Mile Home Delivery

Background: UAVs (Unmanned Aerial Vehicles) and SUGVs (Sidewalk Unmanned Ground Vehicles) are two prominent options to revolutionize last mile home delivery. However, there is no literature yet addressing a comprehensive assessment of them. To bridge this research gap, this paper aimed to compare UAVs to SUGVs in the context of urban parcel delivery from a practical, conceptual, technological, commercial, and environmental perspective. Methodology: Based on structured literature and web research, this paper provided a comparative status quo review of these two delivery concepts. We introduced a parameter-based cost calculus model to estimate the costs per shipment for each technology. To detect the key cost drivers, we applied a one-way sensitivity analysis, as well as a “full factorial design of experiment” approach. Results: These key cost drivers for both operations are the “number of vehicles per operator” and the “average beeline service radius”. From today’s commercial point of view, our model indicated better profitability of SUGVs. However, technical and regulatory developments may render different results in the future. As SUGVs emit significantly less noise than UAVs, we assume that SUGVs have an additional advantage for usage in autonomous urban last mile delivery from a resident’s perspective. Conclusions: Both key cost drivers will significantly influence the commercial viability of unmanned home delivery services. Safety and security aspects will determine regulatory rules on “number of vehicles per operator”. To increase the “average beeline service radius”, UAVs could profit from mothership delivery concepts while SUGV delivery may co-use existing public transport infrastructure.

Analysis of nitinol actuator response under controlled conductive heating regimes

In the last few decades, Nitinol (NiTi) actuators have created a massive impact at the commercial level due to their application in various engineering and medical fields. In this paper, an experimental analysis study is presented on commercially manufactured nitinol tubes for performance enhancement. As received tubes were super-elastic at room temperature with Af temperature of 1.7°C. The nitinol tubes were heat treated at 500°C for different time ranging from 30 min to 60 min to raise the Af temperature. Metallography was performed on pristine and heat-treated samples to analyse the changes in the physical properties. XRD analysis revealed the crystalline structure present in the tubes (as received and heat treated) was nitinol cubic (110) while nitinol cubic (211) at room temperature. Moreover, dilatometry was performed which showed thermal expansion coefficients very close as noted in the literature as 11.4x10-6/°C. In the last section of this paper, the actuation force of the tubes was experimentally measured and analysed using different springs attached to the tubes connected to a conductive heating stage. A full factorial Design of Experiments (DoE) was used based on factors of time, temperature, and spring constant. For a surface temperature of 125°C and a spring constant of 2.39 kN/m, 131 N force was attained from the tube. The maximum actuation force of 145 N was obeserved for surface temperature of 145°C at an exposure time of 60 s with k = 2.39 kN/m.

Triazine Herbicides Risk Management Strategies on Environmental and Human Health Aspects Using In-Silico Methods

As an effective herbicide, 1, 3, 5-Triazine herbicides (S-THs) are used widely in the pesticide market. However, due to their chemical properties, S-THs severely threaten the environment and human health (e.g., human lung cytotoxicity). In this study, molecular docking, Analytic Hierarchy Process-Technique for Order Preference by Similarity to the Ideal Solution (AHP-TOPSIS), and a three-dimensional quantitative structure-active relationship (3D-QSAR) model were used to design S-TH substitutes with high herbicidal functionality, high microbial degradability, and low human lung cytotoxicity. We discovered a substitute, Derivative-5, with excellent overall performance. Furthermore, Taguchi orthogonal experiments, full factorial design of experiments, and the molecular dynamics method were used to identify three chemicals (namely, the coexistence of aspartic acid, alanine, and glycine) that could promote the degradation of S-THs in maize cropping fields. Finally, density functional theory (DFT), Estimation Programs Interface (EPI), pharmacokinetic, and toxicokinetic methods were used to further verify the high microbial degradability, favorable aquatic environment, and human health friendliness of Derivative 5. This study provided a new direction for further optimizations of novel pesticide chemicals.

Investigations on the Surface Integrity of Ti6Al4V under Modified Dielectric(s)-Based Electric Discharge Machining Using Cryogenically Treated Electrodes

The surface integrity of machined components is considered to be an important part of the quality matrix for high-performance applications in the aviation industry. Therefore, close attention is given to the components made up of hard-to-cut materials such as Ti6Al4V, which face processability challenges. In this regard, among the non-conventional machining processes, electric discharge machining is widely preferred for cutting Ti6Al4V. In this study, the potentiality of cryogenic-treated tool electrodes (graphite and aluminum) with unmodified (kerosene) and modified (kerosene with Span 20, Span 60, and Span 80) dielectrics are comprehensively investigated. A three-phased experimentation framework is deployed based on the following process parameters, i.e., surfactant type, surfactant concentration, electrode material, and pulse ON:OFF time. Thorough statistical analyses are performed based on the full factorial design of experiments, and the results are characterized by process physics. It is found that the cutting mechanism is highly dependent on the surfactants, especially their hydrophilic–lipophilic balance in the dielectric. The desirability-based process optimization results show that the unmodified process (with kerosene) produced relatively higher roughness values of 7.5 µm and 5.8 µm for aluminum and graphite electrodes, respectively. However, the modified process (with surfactant) resulted in a lower degree of roughness on the workpiece. The graphite electrode using S-60 with a resulting Ra of 1.73 µm outperformed the aluminum electrode using S-20, yielding an Ra of 3.4 µm. The S-60 surfactant resulted in minimum roughness on the workpiece with the fewest surface defects at the 25 g/L concentration, 50:25 µSec pulse ON:OFF time, and with the cryogenically treated graphite electrode.

Experimental Study and Modeling of Beer Dealcoholization via Reverse Osmosis

The goals of the present investigation are to study and to model pale lager beer dealcoholization via reverse osmosis (RO). Samples were dealcoholized at a temperature of 15 ± 1 °C. An Alfa Laval RO99 membrane with a 0.05 m2 surface was used. The flux values were measured during the separations. The ethanol content, extract content, bitterness, color, pH, turbidity, and dynamic viscosity of beer and permeate samples were measured. The initial flux values were determined using linear regression. The initial ethanol flux (JEtOH 0) values were calculated from the initial flux values and the ethanol content values. A 2P full factorial experimental design was applied, and the factors were as follows: transmembrane pressure (TMP): 10, 20, 30 bar; retentate flow rate (Q): 120, 180, 240 L/h; JEtOH 0 was considered as the response. The effect sizes of the significant parameters were calculated. The global maximum of the objective function was found using a self-developed Grid Search code. The changes in the analytical parameters were appropriate. The TMP had a significant effect, while the Q had no significant effect on the JEtOH 0. The effect size of the TMP was 1.20. The optimal value of the factor amounted to TMP = 30 bar. The predicted JEtOH 0 under the above conditions was 121.965 g/m2 h.

Physical and Functional Characteristics of Extrudates Prepared from Quinoa Enriched with Goji Berry

In this research, the possibilities of applying the extrusion process to produce functional food from quinoa enriched with goji berries were studied. The physical (expansion ratio, bulk density, hardness, and specific mechanical energy) and functional (water solubility index, water absorption index) characteristics were determined and optimized by the response surface methodology. Extrudates were produced using a laboratory single-screw extruder. The full factorial experimental design was used (N = 22) with three complementary center points to show the interactions of the amount of goji berry (1, 3, and 5%) and the feed moisture content (13, 16, and 19%) of the mixture on the physical and functional characteristics. Increasing moisture content from 13 to 19% resulted in extrudates with a lower expansion ratio, water absorption index, water solubility index, specific mechanical energy, and higher density and hardness. Increasing the amount of goji berries from 1 to 5% led to a decrease in expansion ratio, water absorption index, and hardness, and an increase in density, water solubility index, and specific mechanical energy. Optimal extrusion conditions for production of extrudates from quinoa enriched with goji berry were 16.3% feed moisture content and 1.32% goji berry’s amount.

Experimental Design and Multiple Response Optimization for the Extraction and Quantitation of Thirty-Four Priority Organic Micropollutants in Tomatoes through the QuEChERS Approach

The chemical contamination in fruit and vegetables represents a challenging analytical issue, with tomatoes deserving to be investigated as they are fundamental components of the Mediterranean diet. Polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs contamination is of serious concern, due to particulate deposition and to uptake from contaminated soils and water. However, time-consuming, non-simultaneous and/or non-eco-friendly extraction procedures are typically used to investigate organic contamination in tomatoes, with nitro-PAHs that have not yet been studied. Based on these premises, this work reports the development of a QuEChERS-based approach, coupled with gas chromatography/mass spectrometry, for the simultaneous determination of 16 PAHs, 14 PCBs and 4 nitro-PAHs in three tomato cultivars. The effect of dichloromethane, cyclohexane and acetone, as well as of four clean-up phases were studied through the advanced combination of full factorial experimental design and multiple response optimization approaches. The final protocol, based on cyclohexane extraction followed by a double purification step with primary secondary amine and octadecyl silica and a sulfuric acid oxidation, led to 60–120% recoveries (RSD% &lt; 15%). Good repeatability (inter-day precision &lt;15%) and negligible matrix effect (&lt;16%) were confirmed and the protocol was applied to the analysis of real tomato samples purchased in a local market.

Indirect measurement method of ultrasonic bone cutting force based on anti-node vibration displacement

The measurement method of ultrasonic bone cutting force is an urgent problem to be solved in the application of ultrasonic osteotome in robot assisted orthopaedic surgery. It is of great significance to realize bone layer perception and cutting force control in the process of surgery. In this paper, an indirect measurement method of ultrasonic bone cutting force based on anti-node vibration displacement is proposed for the first time. Firstly, a one-dimensional numerical model for the interaction between ultrasonic osteotome and bone tissue is established. The model consists of two modules. The first module is the electromechanical model of ultrasonic osteotome, including piezoelectric transducer model and stepped horn model. The second module is the load model simulating ultrasonic bone cutting, which can be expressed by the Kelvin-Voigt model. The influence of load on the vibration characteristics of ultrasonic osteotome was studied by a one-dimensional numerical model. A full factorial design of experiment is used to collect the necessary data for developing and validating the models. Then, the indirect measurement model of ultrasonic bone cutting force is developed by using grey wolf optimized BP (GWO-BP) neural network algorithm. Taking the effective value x1rms and vibration frequency fx1 of anti-node vibration displacement as inputs, the effective value Frms of cutting force is output. It is found that the predicted effective value of cutting force is in good agreement with the corresponding numerical calculated effective value of cutting force. Finally, the continuous system of ultrasonic osteotome and bone tissue interaction is simulated by finite element method, the discrete system of ultrasonic osteotome and bone tissue interaction is simulated by physical method, and the experimental platform for indirect measurement of cutting force is built. The corresponding relationship between cutting force and anti-node vibration displacement is illustrated, which further verifies the effectiveness and feasibility of the indirect measurement method of ultrasonic bone cutting force based on anti-node vibration displacement.

Collective influence and optimization of 1-hexanol, fuel injection timing, and EGR to control toxic emissions from a light-duty agricultural diesel engine fueled with diesel/waste cooking oil methyl ester blends

This study attempts to utilize a ternary blend comprising diesel, biodiesel, and 1-hexanol in a direct injection (DI) diesel engine. A response surface methodology (RSM) based optimization with the full factorial experimental design was used to optimize the fuel injection timing and exhaust gas recirculation (EGR) with an objective to maximize the performance of the engine with minimum emissions. Three injection timings and three EGR rates were used. Multiple regression models developed using RSM for the responses were found to be statistically significant. Interactive effects between injection timing and EGR on responses for the blends were studied. From a desirability approach, a HX20 blend (diesel 50 v/v% + biodiesel 30 v/v% + 1-hexanol 20 v/v%) injected at lesser fuel injection timing and EGR rate delivered optimum emission and performance characteristics. Confirmatory tests validated the models to be adequate. With reference to diesel, at optimum conditions, there was a significant reduction in nitrogen oxides (NOx) emission with a marginal increase in smoke, hydrocarbon (HC) and carbon monoxide (CO) emissions. Also, it was found that there was minimal loss in brake thermal efficiency (BTE) of the engine. With respect to waste cooking oil methyl ester operation, the blend reduced nitrogen oxides (NOx), smoke, carbon monoxide (CO) and hydrocarbon (HC) emissions significantly with marginal loss in BTE.

DLP of Translucent Alumina: In-Depth Investigation on Slurry Development and Debinding Regimes

Exploring the feasibility of producing near-net-shape components with advanced properties and geometrical features via 3D printing has incrementally become the research focus of various studies. Digital light processing (DLP) technology can manufacture complex-structured components for various technical applications. The aims of this research were to investigate Al2O3 ceramic slurry preparation procedures to identify the ideal components to add to an in-house-developed ceramic slurry, to determine the optimal DLP printing parameters and conditions while understanding their effect on the green part properties and to evaluate the appropriate debinding regime to achieve fully dense crack-free fired parts capable of exhibiting translucent behaviours. The slurry obtained from the ball-milled powder at 800 rpm for 1 h, together with 2 wt.% BYK-145 as a dispersant and the highest achievable solid loading of 85 wt.%, showed the desired rheological and photopolymerisation properties. Full-factorial design of experiments (DOE) was employed to study the impact of the printing parameters on the density and the dimensions of the samples. Different debinding regimes were investigated and it was proven that the lowest debinding heat rate (0.2 °C/min) and longer holding times helped to reduce defects and promote densification (&gt;99.0%), providing optimal grounds to obtain translucent fired parts.

Microextraction by packed sorbent and high-performance liquid chromatography for determination of benznidazole in human plasma

Benznidazole is the main drug used in Chagas disease and its determination in plasma samples is useful in several situations. Hence, robust and accurate bioanalytical methods are needed. In this context, sample preparation deserves special attention, as it is the most error-prone, labor-intensive and time-consuming step. Microextraction by packed sorbent (MEPS) is a miniaturized technique, developed to minimize the use of hazardous solvents and sample amount. In this context, this study aimed to develop and validate a MEPS coupled to high performance liquid chromatography method for the analysis of benznidazole in human plasma. MEPS optimization was performed by a 24 full factorial experimental design, which resulted in about 25 % of recovery. The best condition was achieved when 500 µL of plasma,10 draw-eject cycles, sample volume drawn of 100 µL, and desorption with three times of 50 µL of acetonitrile were used. The chromatographic separation was performed with a C18 (150 × 4.5 mm, 5 µm) column. The mobile phase was composed of water:acetonitrile (60:40) at a flow rate of 1.0 mL min−1. The developed method was validated and proved to be selective, precise, accurate, robust and linear in the range from 0.5 to 6.0 µg mL−1. The method was applied to three healthy volunteers that made use of benznidazole tablets and showed to be adequate to assess this drug in plasma samples.