Experimental Techniques Research Articles

An Experimental Device with Different Widths for Wound Healing Assay

Introduction: Cellular wound healing assay is an important experimental technique for detecting cell migration in vitro. Scratching on monolayer cells using a pipette tip is commonly used. However, it is difficult to guarantee the scratch with the same width, and the initial scratch width has a large impact on the experimental results for different treatment factors or different cell types. To optimize this assay for diverse experimental requirements, we developed an experimental device capable of generating scratches with variable widths. Methods: Our device offers the flexibility of selecting among four widths to create cell scratches, enabling the choice of an optimal initial scratch width for specific cell types and experimental conditions. Results: This device produced straight, clean wounds with precise widths. Comparing cell growth in the four width wounds, Hepa1-6 and HUMSCs showed the greatest difference in 0.6 cm wound, 143B at 0.9 cm wound and urine-derived stem cells at 1.2 cm wound were significantly different, which suggests that the width of the wounds has a huge impact on the experimental results. Compared to other wound inserts on the market, our device is more efficient and economical. Conclusion: This versatile and practical device provides a valuable solution for studying cell migration, facilitating a deeper understanding of cellular behaviors and the development of therapeutic strategies.

Assessing the effect of deep eutectic solvents on α-chymotrypsin thermal stability and activity.

Optimizing the liquid reaction phase holds significant potential for enhancing the efficiency of biocatalytic pro- cesses since it determines reaction equilibrium and kinetics. This study investigates the influence of the addition of deep eutectic solvents on the stability and activity of α-chymotrypsin, a proteolytic enzyme with industrial rele- vance. Deep eutectic solvents, composed of choline chloride or betaine mixed with glycerol or sorbitol, were added in the reaction phase at various concentrations. Experimental techniques, including kinetic and fluorometry, were employed to assess the α-chymotrypsin activity, thermal stability, and unfolding reversibility. Atomistic molecular dynamics simulations were also conducted to assess the interactions and provide molecular-level insights between α-chymotrypsin and the solvent. The results showed that among all studied mixtures, adding choline chloride + sorbitol improved thermal stability up to 18 °C and reaction kinetic efficiency up to two-fold upon adding choline chloride + glycerol. Notably, the choline chloride + sorbitol system exhibited the most substantial stabilization effect, attributed to the surface preferential accumulation of sorbitol, as corroborated by the computational anal- yses. This work highlights the potential of tailoring liquid reaction phase of α-chymotrypsin catalyzed reaction using neoteric solventsto enhance α-chymotrypsin performance and stability in industrial applications.

Novel dopant-free ferromagnetic Mott-like insulator and high-energy correlated-plasmons in unconventional strongly correlated s band of low-dimensional gold

Ferromagnetic insulators and plasmons have attracted a lot of interest due to their rich fundamental science and applications. Recent research efforts have been made to find dopant-free ferromagnetic insulators and unconventional plasmons independently both in strongly correlated electron systems. However, our understanding of them is still lacking. Existing dopant-free ferromagnetic insulator materials are mostly limited to complex d- or f-systems with extremely low Curie temperature, low-symmetry structure, and strict growth conditions on specific substrates, limiting their compatibility with industrial applications. Unconventional plasmon is, on the other hand, a quasiparticle that originates from the collective excitation of correlated-charges, yet they are rarely explored, particularly in ferromagnetic insulator materials. Herewith, we present a novel, room temperature dopant-free ferromagnetic Mott-like insulator with a high-symmetry structure in unconventional strongly correlated s band of low-dimensional highly oriented single-crystal gold quantum dots (HOSG-QDs) on MgO(001). Interestingly, HOSG-QDs show new high-energy correlated-plasmons with low-plasmonics-loss. With a series of state-of-the-art experimental techniques, we find that the Mott-insulating state is tunable with surprisingly strong spin-splitting and spin polarization accompanied by strong s–s transitions, disappearance of Drude response, and generating new Mott-like gap. Supported with a series of theoretical calculations, the interplay of quantum confinement, many-body electronic correlations, and hybridizations tunes electron–electron correlations in s band and determines the ferromagnetism, Mott-like insulator, and high-energy correlated-plasmons. Our result shows a new class of room temperature dopant-free ferromagnetic Mott-like insulator and high-energy correlated-plasmons with low-loss in strongly correlated s band and opens unexplored applications of low-dimensional gold in spin field-effect transistors and plasmonics.

Restoring protein glycosylation with GlycoShape

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape (https://glycoshape.org), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons.

On the Nature of HOPG-Supported Pt1Ti2O7 and its Decomposition of a Nerve Agent Simulant: A Cluster Model of a Single Atom Catalyst Active Site.

Chemical weapons, including hyper lethal nerve agents, are a persistently looming threat across the modern geopolitical landscape. There is a pressing need for the design and development of improved protective materials, which can be substantially aided by the cultivation of a fundamental molecular-level understanding of candidate systems and the corresponding decomposition chemistry. The emergence of the exciting new class of single atom catalyst (SAC) materials has enhanced the prospect of subnanoscale design tailoring in the hopes of optimizing activity and selectivity for a variety of chemical applications. Here, we apply our recently developed experimental technique for modeling the active sites of such SAC materials through the preparation of surface supported size-selected single metal-atom doped metal oxide clusters. The propensity for an SAC cluster model system for Pt1/TiO2 materials, Pt1Ti2O7 supported on highly oriented pyrolytic graphite (HOPG), to adsorb and decompose nerve agent simulant dimethyl methylphosphonate (DMMP) was investigated through a combination of temperature-programmed desorption/reaction (TPD/R) and X-ray photoelectron spectroscopy (XPS). XPS measurements of the as-prepared Pt1Ti2O7 clusters supported the successful isolation of single Pt atoms in clusters monodispersed across the HOPG surface. TPD/R experiments showed that the reactivity exhibited by the Pt1Ti2O7 clusters was distinct from that of Ti2O7 clusters lacking the single Pt atom. It was found that DMMP decomposed over Pt1Ti2O7 upon heating to as low as room temperature, and higher temperature treatments evolved exclusively H2O, CO, and H2, while decomposition over Ti2O7 evolved only methanol and formaldehyde at elevated temperatures. This indicated the promotion of C-H and PO-C bond cleavage within DMMP due to the presence of single Pt atoms in the clusters. Further, the Pt1Ti2O7 clusters were found to desorb P-containing decomposition species, preventing active site poisoning; however, a change of reactivity reflecting that of Ti2O7 was observed following a single TPD/R cycle. This suggested the encapsulation of active Pt sites by titanium oxide during high temperature treatment and is thus an issue deserving of serious attention in the study of Pt1/Ti2O7 SAC materials.

Supercritical Extraction of Ylang Ylang (Cananga odorata) Essential Oil at the Near-Critical Region

The flowers of the ylang ylang tree contain an essential oil which is utilized in high-quality perfumes. The traditional mode of extraction is by steam distillation but it has been shown that the more modern supercritical fluid extraction (SFE) using carbon dioxide has potential for replacing steam distillation. This technology, however, generally operates under high pressures, up to 500 bar. The work described in this paper examines the possibility of using carbon dioxide at much lower pressures, close to the critical point, i.e., 75 bar and 30 °C. Two series of experiments were therefore carried out under such conditions, the first using carbon dioxide alone and the second utilizing ethanol as a co-solvent, the conditions being chosen by applying the Design of Experiments (DOE) technique over ranges of pressure from 80 to 120 bar and temperatures from 35 to 50 °C. Extraction curves are presented which show the rates of extraction to be significantly increased by the use of the co-solvent, with the measured values being 0.74% to 0.97% with no co-solvent addition, increasing to 0.92% to 1.16% with co-solvent addition. These rates are, however, lower than the rates previously reported at higher pressures, i.e., 0.9 to 1.8%. Better quality oils are, however, produced compared to those at higher pressures, with the major components being benzene benzoate, benzene salicylate, cubebene, and benzyl acetate. It is recommended that an economic study be carried out to evaluate whether it is feasible to utilize this process commercially.

Deciphering Surface Ligand Density of Colloidal Semiconductor Nanocrystals: Shape Matters.

Surface chemistry of colloidal semiconductor nanocrystals (NCs) is of paramount importance because it profoundly impacts their physical and chemical properties, processing, and performance. Herein, we report the effect of the shape of ZnS NCs in terms of nanodots, nanorods, and nanoplatelets (NPL) on the surface ligand density (LD) of the commonly used oleylamine (OLA) ligand by combining three experimental quantification techniques (e.g., thermogravimetric analysis-differential scanning calorimetry, 1H nuclear magnetic resonance spectroscopy, and inductively coupled plasma-optical emission spectrometry) with the semiempirical molecular dynamics (MD) simulations. Consistent results on the surface LD derived by the aforementioned three independent techniques were obtained, presenting an ascending order of LDdots < LDrods < LDNPLs. MD simulations reveal that the highest LD for ZnS NPLs can be attributed to their extremely flat and uniform surfaces with regular distribution of surface Zn atoms for the OLA molecules to achieve parallel and tight stacking, while for ZnS nanodots and nanorods, their surfaces may have staggered arrangement and multisteps, making it unlikely for the OLA ligand to adopt the tight ligand stacking mode. The finding revealed in this work not only sheds light on the constitution of the molecule ligand shell of NCs, which is helpful for their rational morphology control, but also provides an additional and important knob for tuning their chemical functionality.

Enhancing auxetic gradient structures for hip joint implants to optimize stress shielding reduction

This study investigates the design and optimization of a porous hip implant to mitigate stress shielding. Initially, the focus was on determining the elastic modulus of a three-dimensional auxetic structure, primarily in the y-direction. Various methods—numerical, analytical, and experimental—were used to assess the elastic properties. Additive manufacturing was employed to create samples, which were then tested for their elastic properties through compression testing. The results revealed a strong correlation between the elastic modulus values obtained from simulations and experimental tests in the y-direction. To further enhance the implant’s performance and reduce stress shielding at the implant-bone interface, a gradient structure was introduced. This gradient design progressively increases the elastic modulus away from the bone contact surfaces, aligning closely with the bone’s modulus at the interface. The elastic modulus of this gradient structure was computed using Abaqus software and validated through analytical methods in MATLAB, with a minimal 4.8% difference between the two approaches, demonstrating high agreement. The application of a genetic algorithm enabled the creation of a porous hip implant tailored to minimize stress shielding throughout its structure. This innovative approach, integrating numerical, analytical, and experimental techniques with gradient structures, holds promise for improving hip implant performance and enhancing patient outcomes by reducing stress-shielding complications.

Stochastic modelling of uncertainty in Chilean radiata pine wood beams subject to lateral torsional buckling

ABSTRACT This paper aims to investigate the effect of the variability in the mechanical and cross-section properties of Chilean radiata pine on the lateral buckling strength of slender wooden beams. The study is conducted numerically through finite element modeling and using linear eigenvalue analysis. The numerical model is validated using experimental results available in the literature. Two approaches are employed in the analysis, firstly a deterministic approach using design of experiments (DOE) techniques to establish the relevance of the parameters, and secondly a stochastic one, through Monte-Carlo simulation to investigate the impact of each parameter on the lateral buckling resistance. Various distribution functions are considered to investigate the effect of uncertainties in the material parameters and their impact on the critical buckling moments. Moreover, Sobol indices indicated that from the total variability of the distribution of the critical moment, an average of 50% of the variability is attributed to the shear modulus G LR , 37% to the longitudinal elastic modulus E L , and a negligible percentage of the grain angle F .

Experimental and numerical investigation of the cooling performance of a solar chimney integrated with a humidification system

Solar chimneys are efficient systems for natural ventilation based on thermal principles. However, in regions with high temperatures it may not serve to ensure a comfortable indoor environment. To overcome this challenge, integrating air humidification into the ventilation process becomes necessary. The present study investigates the feasibility of integrating a solar chimney with a humidification setup for evaporative cooling in the climate of Kirkuk, Iraq. This investigation utilizes both experimental and numerical techniques. Numerical simulations carried out using a finite volume approach and validated against the experimental data. The experimental outcomes highlight the effectiveness of evaporative cooling in lowering outdoor air temperatures entering the room from months May to August in the range 5°C–10°C. A parametric analysis of the system indicates that increasing water supply can enhance the rate of evaporative cooling and lower the room temperature while elevating relative humidity. Additionally, higher humidity diminishes air buoyancy, leading to a significant decrease in air changes per hour (ACH) rate. The findings also show that both air changes per hour and water consumption reach their peak at a chimney height of 0.5 m. Moreover, a direct correlation is observed between chimney width and both ACH and water consumption. Expanding the chimney width results in higher ACH and increased water consumption for evaporative cooling. Artificial neural network (ANN) model demonstrates robust predictive accuracy, evidenced by high regression coefficients of 0.9566 for ACH and 0.9505 for room temperature correlated from computational fluid dynamics (CFD) results. This underscores ANN model effectiveness in forecasting system performance under varying input conditions, providing valuable insights for different scenarios and configurations of the system.

TLR4/TRIF/Caspase-8/Caspase-1 Pathway in Choroidal Endothelial Cells Promotes Choroidal Neovascularization

Purpose The purpose of this study was to investigate the role and mechanism of caspase-8 in the development of choroidal neovascularization induced by age-related macular degeneration, with the aim of identifying a potential therapeutic target for neovascular age-related macular degeneration. Methods Mouse models of laser photocoagulation-induced choroidal neovascularization and hypoxic human choroidal endothelial cells were utilized to examine the involvement of caspase-8 in choroidal neovascularization development. The toll-like receptor 4/TIR domain-containing adaptor molecule 1/caspase-8 pathway was explored in hypoxic human choroidal endothelial cells to elucidate its contribution to pathological angiogenesis. Various experimental techniques, including inhibition assays and immunoblotting analysis, were employed to assess the effects and mechanisms of caspase-8 activation. Results Inhibition of caspase-8 demonstrated attenuated choroidal neovascularization development in mice subjected to laser photocoagulation. Activation of the toll-like receptor 4/TIR domain-containing adaptor molecule 1/caspase-8 pathway was observed in hypoxic human choroidal endothelial cells. Upon activation by the toll-like receptor 4/TIR domain-containing adaptor molecule 1 axis, caspase-8 directly cleaved caspase-1, leading to the cleavage of interleukin-1β and interleukin-18 by caspase-1. Consequently, activation of interleukin-1β and interleukin-18 through the toll-like receptor 4/TIR domain-containing adaptor molecule 1/caspase-8/caspase-1 pathway promoted the proliferative, migratory, and tube-forming abilities of hypoxic human choroidal endothelial cells. Conclusion The findings of this study indicate that caspase-8 plays a crucial role in promoting choroidal neovascularization by activating interleukin-1β and interleukin-18 through the toll-like receptor 4/TIR domain-containing adaptor molecule 1/caspase-8/caspase-1 pathway in choroidal endothelial cells. Therefore, targeting caspase-8 may hold promise as a therapeutic approach for neovascular age-related macular degeneration.

Recovery of samarium and cobalt/iron oxide from SmCo magnets through acid baking and water leaching

AbstractRare earth elements (REEs) and cobalt (Co) are listed as critical raw materials because of their importance in global industrial production growth, high supply risk, and economic significance. The recovery of Co and REEs from secondary resources is therefore proposed as a key countermeasure to address this concern. In this study, a straightforward process that integrates acid baking and water leaching is proposed for the recovery of samarium (Sm) and Co from scrap SmCo magnets. Firstly, the chemical composition of SmCo magnets is revealed by ICP-OES and XRF. The Taguchi experimental design technique is employed to optimize nitric acid baking and water leaching. Based on the thermal decomposition behavior of Co, Fe, and Sm, the acid baking temperature is studied for the conversion of metal nitrates, excluding REEs nitrates, into metal oxides. The optimal conditions for acid baking and water leaching are identified, and a reactor for the pilot-scale acid baking process is proposed. The optimum parameters are tested with the proposed reactor.

Research on Regulation Method of Variable-Air-Volume Air Conditioning System with “Personal Space”

In large public facilities, such as airport terminals or open-plan office spaces, the HVAC system typically consumes substantial amounts of energy. However, individuals often gather at specific areas while other zones are occupied by transient or occasional users. To minimize operational energy usage, this paper aims to reduce thermal comfort demands in non-targeted areas. This paper introduces a method for regulating the thermal environment around occupants exclusively in the variable-air-volume (VAV) air conditioning running mode. The investigation utilizes Airpak modeling and experimental verification techniques. Additionally, an analysis of temperature field and velocity field distributions within the room under the “personal space” operation mode is presented. The results suggest that adjusting the numbers of air vents, openings, airflow velocities, and air supply orientations can establish a comfortable thermal environment for inhabitants and reduce the overall ADPI value. The combined air supply mode leads to a 16.7% reduction in power usage compared to traditional full-space operation.

A reliable combustion strategy for a throttling-assisted supersonic combustor with flight Mach 7

As the flight Mach number increases, scramjet engines struggle to achieve stabilized combustion. In the present investigation, a reliable combustion strategy is developed for a kerosene-fueled supersonic combustor. Assisted by air throttling, this enables the combustor to operate efficiently at a flight Mach number of 7. Various experimental measurement techniques are used to capture the combustion process and flow characteristics. A three-dimensional numerical method based on the Reynolds-averaged Navier–Stokes equations is employed to analyse the effects of air throttling on the non-reacting and reacting flow fields. The whole combustion test is effectively divided into five processes covering the establishment of non-reacting flow, ignition, flame stabilization, and flameout. Analysis of the experimental and numerical flow fields indicates that, as the origin of the initial flame in the combustor, the aerodynamic compression induced by air throttling promotes the kerosene/air sub-mixing region. The main mixing process is enhanced by the arrangement of ramps and cavity, which contribute to effective multi-channel injection. Two combustion modes are observed, namely combined cavity shear-layer/recirculation stabilized combustion and jet-wake stabilized combustion. In the reacting flow field, the additional injection of throttling gas improves the thrust augmentation at the cost of reduced combustion intensity. The outlet combustion efficiency and total pressure recovery coefficient are found to decrease by 8.49% and 28.79%, respectively.

Predicting protein complexes in protein interaction networks using Mapper and graph convolution networks

Protein complexes are groups of interacting proteins that are central to multiple biological processes. Studying protein complexes can enhance our understanding of cellular functions and malfunctions and thus support the development of effective disease treatments. High-throughput experimental techniques allow the generation of large-scale protein-protein interaction datasets. Accordingly, various computational approaches to predict protein complexes from protein-protein interactions were presented in the literature. They are typically based on networks in which nodes and edges represent proteins and their interactions, respectively. State-of-the-art approaches mainly rely on clustering static networks to identify complexes. However, since protein interactions are highly dynamic in nature, recent approaches seek to model such dynamics by typically integrating gene expression data and identifying protein complexes accordingly. We propose MComplex, a method that utilizes time-series gene expression with interaction data to generate a temporal network which is passed to a generative adversarial network whose generator is a graph convolutional network. This creates embeddings which are then analyzed using a modified graph-based version of the Mapper algorithm to predict corresponding protein complexes. We test our approach on multiple benchmark datasets and compare identified complexes against gold-standard protein complex datasets. Our results show that MComplex outperforms existing methods in several evaluation aspects, namely recall and maximum matching ratio as well as a composite score covering aggregated evaluation measures. The code and data are available for free download from https://github.com/LeonardoDaou/MComplex.

Capturing the random mechanical behaviour of granular materials: a comprehensive stochastic discrete-element method study

This research pioneers a stochastic discrete-element method (DEM) by integrating the probability density evolution method (PDEM), offering a novel approach to connect particle-scale property uncertainties, specifically inter-particle friction coefficient (μ) and particle shear modulus (Gp), with macro-scale soil behaviour. Through 1100 DEM simulations, this study reveals that, for uniform particle size distribution, the uncertainty in μ substantially affects large-strain soil behaviour, with its effect being associated with packing density and soil state. The uncertainty effect of μ remains pronounced at the critical state, while the packing density effect diminishes. Stress distribution appears insensitive to uncertainty of μ, rather suggesting a predominant influence of particle size distributions. In contrast, the uncertainty effect of μ becomes negligible on small-strain behaviour, demonstrating a limited effect on small-strain stiffness. Uncertainty in Gp presents limited effects on large-strain behaviour, including stress ratios and dilatancy. At small strains, Gp shows a significant impact on stiffness, diverging from minimal influence identified for μ. This study presents a framework that integrates experimental techniques to study particle-scale uncertainty propagation, enhancing predictions of macro-scale soil behaviour. This approach could be beneficial for precise multi-scale simulations, incorporating particle-level uncertainties in engineering-scale models, thereby improving geotechnical practice predictability.

Metal Nanowire‐Induced Enhancement of Surface Emission in Luminescent Glass

AbstractLuminescent glass, as an important component of photonic materials, is extensively utilized in applications such as lasers and optical fiber amplifiers, which demand high quantum efficiency. To further enhance the surface emission performance of luminescent glass, this study introduces a novel approach by employing custom‐made metal nanowires, applying them as coatings on the surface of Erbium‐doped glass, rather than embedding traditional metal nanoparticles. Utilizing experimental techniques and Finite‐Difference Time‐Domain (FDTD) simulations, it is demonstrated that the surface‐emission from Erbium‐doped glass coated with silver nanowires or copper nanowire‐graphene hybrids is significantly increased, showing an enhancement rate of up to 117.27% compared to intrinsic glass. Extended FDTD analysis reveals that variations in the size and density of the nanowires can optimize emission characteristics, thereby improving the emission performance of luminescent glass. The enhancement of the local electromagnetic field at the interface underscores the efficacy of metal nanowires in boosting photonic materials. This study not only highlights the practicality of metal nanowires in photonics but also introduces a rapid deployment pathway for customizing photonic interactions through nano‐engineering, potentially extendable to other photonic materials.

NuTag: a proof-of-concept study for a long-baseline neutrino beam

The study of neutrino oscillation at accelerators is limited by systematic uncertainties, in particular on the neutrino flux, cross section, and energy estimates. These systematic uncertainties could be eliminated by a novel experimental technique: neutrino tagging. This technique relies on a new type of neutrino beamline and its associated instrumentation which would enable the kinematic reconstruction of the neutrinos produced in and decays. This article presents a proof-of-concept study for such a tagged beamline, aiming to serve a long-baseline neutrino experiment exploiting a megaton scale natural water Cherenkov detector. After optimising the target and the beamline optics to first order, a complete Monte Carlo simulation of the beamline has been performed. The results show that the beamline provides a meson beam compatible with the operation of the spectrometer, and delivers a neutrino flux sufficient to collect neutrino samples with a size comparable with similar experiments and with other un-tagged long-baseline neutrino experimental proposals.

Estimation of Turbulent Mixing Factor and Study of Turbulent Flow Structures in Pressurized Water Reactor Sub Channel by Direct Numerical Simulation

Abstract Subchannel analysis codes are presently a requirement for design and safety analysis of nuclear reactors. Among the crucial inputs for these codes, the turbulent mixing factor holds particular significance. However, acquiring this factor through experimental means proves to be a challenging endeavor, primarily due to the necessity for precise pressure equilibrium between subchannels. Consequently, this requirement leads to the undertaking of expensive and intricate experiments for each new reactor or in cases where there are modifications in fuel bundle design. The need for direct numerical simulation (DNS) stems from the challenges and costs involved in experimental techniques, and the uncertainties due to empiricism in computational fluid dynamics (CFD) models. In this study, DNS has been conducted across six Reynolds numbers, ranging from 17,640 to 1.176 × 105, in the geometry of a pressurized water reactor (PWR) subchannel. The resulting turbulent flow structures have been computed and their dynamics are examined. Furthermore, this paper presents a methodology for directly calculating the turbulent mixing factor from the fluctuating velocity field obtained from DNS data. The turbulent mixing process has been scrutinized in-depth, and a correlation for the turbulent mixing factor is developed. It is noted that most of the mixing occurs in the near-wall region. The study suggests different mixing factors for mass and momentum mixing. This paper aims to provide a comprehensive insight into the turbulent mixing phenomenon.

The Versatility of the Taguchi Method: Optimizing Experiments Across Diverse Disciplines

AbstractThe Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.