Design Of Experiments Research Articles

A Review on QbD Approach in Analytical Method Development and Validation .

Quality by Design (QbD) is a cutting-edge pharmaceutical development that enhances analytical method robustness by emphasizing a thorough understanding of processes, risk management, and continuous improvement. Unlike traditional methods that often focus on end-product testing, QbD integrates quality into the development stages through a structured process. This involves defining the analytical target profile, identifying critical quality attributes, and employing the design of experiments to optimize method parameters. The implementation of QbD begins with a comprehensive understanding of the analytical objectives, leading to the identification of method variables that impact quality. Using risk assessment tools such as failure mode and effects analysis, developers can pinpoint potential risks and focus on areas of highest concern. Design of Experiments (DoE) plays a crucial role in this approach by exploring the interaction between variables and establishing a robust design space. Organizations can decrease variability, comply with regulations, and perform better methods when they adopt QbD concepts. This systematic approach not only minimizes the risk of method failures but also ensures consistent product quality, leading to increased patient safety and satisfaction. Furthermore, QbD facilitates faster development cycles and cost efficiencies by reducing the need for post-launch troubleshooting and modifications. This abstract provides a comprehensive overview of the role and significance of QbD in analytical method development, highlighting its components, benefits, and impact on the pharmaceutical industry.

EXPERIMENTAL INVESTIGATION ON FREE VIBRATIONAL DAMPING AND DRILLING BEHAVIOR OF FLAX REINFORCED EPOXY COMPOSITES USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Flax reinforced epoxy (F-Ep) composites were prepared by the compression moulding technique, varying the fiber content (0, 25, 35 and 45 wt%). The free vibration test was performed on the neat epoxy and F-Ep composites to understand their dynamic characteristics, and results showed that natural frequency and damping factor of the F-Ep composites increased with an increase in the fiber content. The F-Ep composite (45F-Ep) that exhibited better damping was selected for performing the drilling operation. Factors such as spindle speed (rpm), feed rate (mm/min) and drill point angle (degree) were chosen as input parameters and the tabulated set of experiments were in accordance with Taguchi’s design of experiment. The response measured was thrust force and the obtained values were found in the range of 19.66 to 50.75 N. The minimum value of thrust force was achieved when the F-Ep composite was drilled at high spindle speed (3000 rpm), with a feed rate of 75 mm/min using the drill point angle of 118°. ANOVA analysis showed that the developed regression model was significant and thrust force was mainly influenced by the spindle speed. Mathematical models were developed for drilling F-Ep composite using response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS) and compared for their efficacy.

Delamination and Evaluation of Multilayer PE/Al/PET Packaging Waste Separated Using a Hydrophobic Deep Eutectic Solvent.

This research concerns the development and implementation of ground-breaking strategies for improving the sorting, separation, and recycling of common flexible laminate packaging materials. Such packaging laminates incorporate different functional materials in order to achieve the desired mechanical performance and barrier properties. Common components include poly(ethylene) (PE), poly(propylene) (PP), and poly(ethylene terephthalate) (PET), as well as valuable barrier materials such as poly(vinyl alcohol) (PVOH) and aluminium (Al) foils. Although widely used for the protection and preservation of food produce, such packaging materials present significant challenges for established recycling infrastructure and, therefore, to our future ambitions for a circular economy. Experience from the field of ionic liquids (ILs) and deep eutectic solvents (DESs) has been leveraged to develop novel green solvent systems that delaminate multilayer packaging materials to facilitate the separation and recovery of high-purity commodity plastics and aluminium. This research focuses on the development of a hydrophobic DES and the application of a Design of Experiments (DoE) methodology to investigate the effects of process parameters on the delamination of PE/Al/PET laminate packaging films. Key variables including temperature, time, loading, flake size, and perforations were assessed at laboratory scale using a 1 L filter reactor vessel. The results demonstrate that efficient separation of PE, Al, and PET can be achieved with high yields for material and solvent recovery. Recovered plastic films were subsequently characterised via Fourier-transform infra-red (FTIR) spectroscopy, Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) to qualify the quality of plastics for reuse.

Lignocellulose Treatment Using a Flow-Through Variant of OrganoCat Process.

This study adapts the biphasic OrganoCat system into a flow-through (FT) reactor, using a heated tubular setup where a mixture of oxalic acid and 2-methyltetrahydrofuran (2-MTHF) is pumped through beech wood biomass. This method minimizes solvent-biomass contact time, facilitating rapid product removal and reducing the risk of secondary reactions. A comparative analysis with traditional batch processes reveals that the FT system, especially under severe conditions, significantly enhances extraction efficiency, yielding higher amounts of lignin and sugars with reduced solid residue. Notably, the FT system shows partial hydrolysis of the cellulose, which increases with temperature while not producing significant amounts of furfural or 5-HMF, indicating more efficient depolymerization of polysaccharides without substantial sugar degradation. A statistical design of experiments (DOE) using a Box-Behnken design elucidates the influence of process variables (time, solvent flow rate, temperature) on the yield. Key findings highlight reactor temperature as the dominant factor affecting yields, with process time showing a significant but less pronounced impact. This study demonstrates the potential of the FT OrganoCat system for efficient lignocellulosic biomass fractionation and represents an advancement towards continuous lignocellulose processing, contributing to our knowledge of process optimization for improved biorefinery applications.

Buffer Screening of Protein Formulations Using a Coarse-Grained Protocol Based on Medicinal Chemistry Interactions.

In drug and vaccine development, the designed protein formulation should be highly stable against the temperature, pH, buffer, excipients, and other environmental settings. Similarly, in a sensing unit, one needs to know how strongly two biomolecules bind to guide the design of the biorecognition unit accordingly. Typically, the community performs a series of experiments to thoroughly examine the parameter space, the so-called design-of-experiment (DoE) method, to identify the optimal formulation conditions. Unfortunately, extensive physical testing entails high costs, repeatability issues, and a lack of in-depth knowledge of the underlying mechanisms that affect the final outcome. To address these challenges, we developed a physics-based simulation protocol for buffer screening of protein formulations. We are introducing a coarse-grained molecular simulation protocol that consists of six different interactions. The so-called medicinal chemistry interactions (electrostatics, hydrophobicity, hydrogen bonding propensity, disulfide bonding, and water-water) are based on the physical nature of the protein's amino acid and the partitioning/polarity of any other chemical constituent. The protocol is applied in immunoglobulin-based monoclonal antibodies. We have analyzed the protein behavior as a function of acidity (pH) to discover the isoelectric point by solving the Poisson-Boltzmann equation in a mesoscale grid. To identify the conditions under which the protein oligomerizes in a given buffer, pH, temperature, and ionic strength, we are performing dissipative particle dynamics (DPD) simulations. The protocol allows researchers to reach the high time/space scales required to study protein formulations in their full complexity. Combined with the disruptive protein folding artificial intelligence (AI) algorithms that have been recently developed, the protocol creates a powerful digital framework for cultivating advanced pharmaceutical and biological applications.

Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells

AbstractTraditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL−1 in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.

DoE-based modeling of cracked 3D orthotropic panels with stiffeners

This study uses the Design of Experiments (DoE) technique to develop analytical formulations for the stress intensity factor and other non-singular variables near the crack front of a stiffener-reinforced three-dimensional orthotropic panel. The method employs a 52 fractional factorial design, effectively reducing the number of finite element simulations required. The input variables consist of four parameters, each with five levels, which directly affect the process responses. The statistical analysis is performed using the Ellistat software, which offers powerful tools for Design of Experiments (DoE) and incorporates specialized algebraic-statistical approaches. These techniques are utilized for analysing the outcomes and formulating a mathematical model of the cracking characteristics for panels that are strengthened with five stiffeners. The findings of the analysis of variance (ANOVA) indicate strong statistical significance for all tested models, hence verifying their validity based on statistical evaluation criteria. In addition, the residual analysis demonstrates a normal distribution, which strengthens the reliability of the underlying models and fulfils a vital requirement for numerous statistical analyses. The analytical formulations that come from this study have enormous potential for practical use in the field of engineering, specifically for structures that are orthotropic and feature cracks. This study signifies a notable progress in integrating numerical simulation, statistical analysis, and mathematical modelling to develop pragmatic and dependable analytical instruments for the examination of intricate systems.

An Automated Electrochemical Flow Platform to Accelerate Library Synthesis and Reaction Optimization

Automated batch and flow reactors are well‐established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single‐pass flow systems. In order to address these issues, we have designed and implemented a new, slug‐based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C‐N cross‐couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous‐flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6‐fold increase in reaction yield.

A Nondestructive and Rapid Method for the Determination of Triamterene Content by Transmission Raman Spectroscopy Combined with Chemometrics

Background: Triamterene is a potassium-conserving diuretic mainly used in the treatment of edematous diseases. Although there are some methods to measure the content of triamterene, the existing methods have the characteristics of sample destructiveness and low flux, making it difficult to meet the needs of online monitoring. In recent years, transmission Raman spectroscopy (TRS), as a new technology for the determination of drug content, has emerged as being nondestructive and rapid and provides a new method for the determination of triamterene content. Objective: In this study, we used transmission Raman spectroscopy combined with partial least squares (PLS) approaches to establish a six concentration levels model for measuring the content of triamterene. The model was applied to determine whether tablets are commercially available. Method: Firstly, TRS was used to collect the spectra of the principal components and mixed excipients of triamterene, and the feasibility test was carried out. Secondly, six concentration levels were determined by the design of experimental (DOE), and hand-made tablets were prepared to obtain corresponding spectra. The content prediction model was established by the PLS method, and the content of triamterene was determined by high-performance liquid chromatography (HPLC) to correct the model. Finally, the model was applied to the determination of triamterene in commercially available tablets. Results: The results showed that the established model was successfully applied to the determination of triamterene. The values of RMSEC (0.0089783) and RMSECV (0.0097241) of the final model were very low and close to each other. The relative error of 12 hand-made tablets predicted by this model was less than 5% compared with the results determined by HPLC. In addition, in the process of applying the model to the determination of the content of commercially available tablets, the accuracy of the model can be significantly improved by adding the spectrum of commercially available tablets. The corrected model was used to determine the content of triamterene in two commercially available tablets. The results showed that the relative error was less than 5%. Conclusion: We described a new strategy to analyze the content of active pharmaceutical ingredients (API) in triamterene tablets by TRS with PLS. The established model has the advantage of non-destructive and rapid quantitation, which can provide a new method for real-time monitoring of production lines and promote the development of process analytical technology (PAT).

Deep Eutectic Solvent as a New Zeta Potential Altering Chemical for Sand Agglomeration.

Fines migration can cause various issues, such as plugging of the sand screen and damage to tubings. There are two chemical sand control methods: consolidation and agglomeration. Consolidation works by injection of a solvent into the formation to harden over time and hold the sand in place, while agglomeration works by altering chemical properties of the sand surface to attract and clump up sand. Various chemicals have been used for research in sand control. Some chemicals for consolidation, mostly resins, have been effective in consolidating sand but may cause permeability impairment, which will reduce production. Some chemicals for agglomeration such as a polymer with amines have been less effective or are nonbiodegradable. In this work, a novel deep eutectic solvent (DES) and ionic polymer combination as a fines stabilizer is formulated in-house and tested through extensive experimental study. The development of chemicals is based on agglomeration principles which determine the range of zeta potential reduction that can be achieved to destabilize, coagulate, and flocculate the fine particles together with different combinations of DESs and ionic polymers tested systematically using the design of experiment (DoE) method. The chemicals are then tested for compatibility with reservoir fluids in the jar test. The optimized formulation is characterized by thermogravimetric analysis (TGA) for limit of temperature degradation and laser particle size analysis (LPSA) for the extent of particle size. The novelty of this work is the development of a greener and more cost-saving in-house DES and ionic polymer combination as a fines stabilizer chemical, which is effective for both injection or production wells after stimulation or enhanced oil recovery (EOR) treatments. Due to the tunable nature of the DES, the formulated chemical can be tailored for various reservoir conditions to cater to specific requirements.

An Automated Electrochemical Flow Platform to Accelerate Library Synthesis and Reaction Optimization.

Automated batch and flow reactors are well-established for high throughput experimentation in both thermal chemistry and photochemistry. However, the development of automated electrochemical platforms is hindered by cell miniaturization challenges in batch and difficulties in designing effective single-pass flow systems. In order to address these issues, we have designed and implemented a new, slug-based automated electrochemical flow platform. This platform was successfully demonstrated for electrochemical C-N cross-couplings of E3 ligase binders with diverse amines (44 examples), which were subsequently transferred to a continuous-flow mode for confirmation and isolation, showing its applicability for medicinal chemistry purposes. To further validate the versatility of the platform, Design of Experiments (DoE) optimization was performed for an unsuccessful library target. This optimization process, fully automated by the platform, resulted in a remarkable 6-fold increase in reaction yield.

Optimization of Pyrolysis Parameters by Design of Experiment for the Production of Biochar from Sewage Sludge

Sewage sludge management is a key concern in today’s world. Improper disposal can lead to various environmental issues including air, water and soil pollution. Among the available technologies, thermal treatments, particularly pyrolysis, are gaining interest for their ability to reduce sewage sludge volume and to recover materials and energy from it. This study explored the influence of some relevant parameters in the thermal pyrolysis process. The design of experiment, named central composite design, was accounted to optimize temperature, heating rate and residence time in order to maximize the biochar yield and its CO2 adsorption capacity. A two-factor interaction model provided a satisfactory interpretation of the results. Within the studied ranges, maximum values of 47.8 wt% and 0.514 mol CO2/kg were obtained for the yield and CO2 adsorption capacity, respectively. Two significant experiments were repeated in a different pyrolysis system highlighting how other factors (e.g., reactor geometry, granulometry, etc.) can influence the quantity and the quality of produced biochar. The biochar obtained under the best pyrolysis conditions was characterized by a surface area of 124 m2/g and an ash content of 61 wt%. Lastly, the theoretical energy balance showed that the drying process is the main energy-intensive step in the pyrolysis of sewage sludge.

Analysis, modelling, and optimization of force in ultra-precision hard turning of cold work hardened steel using the CBN tool

The machinability of high-performance materials such as superalloys, composites, and hardened steel has been a big challenge due to their mechanical, physical, and chemical properties, which give them inherent complex machining characteristics. Additionally, majority of machinability tests conducted on these materials have been carried out on conventional and less precise lathes based on Taguchi, composite, and other designs of experiments that do not exploit all the possible combinations of cutting parameters. This work reports an investigation on ultra-precision hard turning (UHT) of cold work hardened AISI D2 steel of HRC 62, based on the full factorial design of experiment, carried out on an ultra-precision lathe. A theoretical analysis of the force components generated is reported. Modelling of the process, based on the resultant force, is also reported through a machine learning model. The model was developed from the experimental data and statistically evaluated with validation data. Its average MAPE values of 1.47%, 4.81%, and 10.66% for training, testing, and validation, respectively, attest to its robustness. The excellent coefficient of determination values, R2, also justify the model’s robustness. Multi-objective optimization was also conducted to optimize material removal rate (MRR), resultant force, and vibration simultaneously. For sustainable and efficient UHT, optimal cutting velocity (158.8 m/min), feed (0.125 mm/rev), and depth of cut (0.074 mm) were proposed to generate optimal resultant force (224.8 N), MRR (2603.6 mm3/min), and vibration (0.03 m/s^2) simultaneously. These results can be beneficial in planning UHT processes for high-performance materials.

Optimizing Cold Spray Parameters for High Entropy Alloy Coatings Using Taguchi and Box–Behnken Design Approaches for Mechanically Alloyed Powder

AbstractThe present study focused on optimizing the cold spray (CS) process parameters for depositing Fe20Cr20Mn20Ni20Co20 (Cantor alloy) coatings using mechanically alloyed (MA) powder. A two-step design of experiments approach was employed, beginning with the initial screening of input variables using the L8 Taguchi method, followed by the refinement of process parameters through the Box–Behnken design of experiments. Key performance indicators included deposition efficiency (DE), coating thickness per pass, and microstructural parameters including porosity, cracks, and interfacial defects/delamination. The study identified process gas temperature as the primary factor influencing both DE and thickness per pass. Higher gas temperature and pressure, combined with increased scanning speed, resulted in higher DE. The DE of the MA Cantor alloy powder peaked at around 14-15%, with a deposit density greater than 99% achieved at the highest process gas temperature and pressure (1000 °C and 60 bar, respectively). The average hardness of the optimal CS coating deposited using MA powder was found to be 679 ± 17 HV0.1, which is approximately 90% greater than the average hardness reported for CS coatings deposited using atomized powder.

Handshake of deep eutectic solvent and ionic liquid: Two liquid-liquid microextraction procedures for plant analysis

Plants are subjects of interest due to the secondary metabolites in their extracts which are promising as new pharmaceuticals. Phytochemistry do not have united system of sample preparation or analysis still due to different structure of plant cells, wide broad range of chemical properties and concentrations of bioactive compounds. Such challenges can be addressed in a green chemistry manner using new approaches through smart materials in routine monitoring and researches. Liquid smart materials, such as ionic liquids (ILs) and deep eutectic solvents (DESs) are attractive due to flexible properties, lots of extraction approaches, recycle potential, and direct compatibility with powerful analytical methods.In this study DES-based microextraction procedure with pH-switching was developed. Four choline chloride DESs were suggested as selective extraction phases for polar compounds from acetonitrile extracts. Method was successfully tested on four plants (Iris sibirica L., Hypericum perforatum L., Scutellaria baicalensis G, Citrus reticulata B.). Developed procedure was optimized and validated for the choline chloride – urea (1:2 mol/mol) DES that demonstrated better results in extraction. LOD for rutin was found as 0.05 mg ml−1. For low-polar compound, imidazolium ionic liquid-based dispersive liquid-liquid microextraction procedure was developed. 1-hexyl-3-methylimidazolium salts have demonstrated desired selectivity. The main factors influencing the extraction efficiency have been identified and optimized by design of experiment on two model plants (Iris sibirica L. and Scutellaria baicalensis G.). Validation procedures were done for thymol. LOD for thymol was found as 0.021 mg ml−1.The methods were compared with each other and traditional methanol extraction. The selectivity of the smart materials supports each other, usage of such extraction phases provides same or better results as obtained with methanol.

Knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm for semiconductor final testing scheduling problem

The semiconductor final test scheduling problem (SFTSP), recognized as a crucial bottleneck in the semiconductor production process, holds immense significance for improving both quality control and scheduling efficiency within chip and integrated circuit enterprises. This article introduces the knowledge-enhanced multidimensional estimation of distribution hyper-heuristic evolutionary algorithm (KMEDHEA) for addressing the SFTSP with the aim of minimizing the makespan. First, a single-vector encoding scheme is used to represent feasible solutions, and a problem-specific constrained-separable left-shift decoding scheme is devised to transform these solutions into feasible scheduling schedules. Second, eight simple yet effective heuristics with problem-specific knowledge are developed that served as a suite of low-level heuristics (LLHs) for exploring the problem solution space. Third, the multidimensional estimation of distribution algorithm (MEDA) is employed as the high-level strategy to estimate the correlations and connections of the pre-designed LLHs, thereby guiding the search scope towards high-quality individuals. Finally, critical configurations of parameters are systematically analyzed by conducting a design-of-experiment (DOE) approach. Numerical experiments are conducted on well-known benchmark datasets, and the experimental results demonstrate the superiority of the KMEDHEA versus several state-of-the-art approaches. The best-known solutions are updated for nine out of ten benchmark instances, highlighting the effectiveness and efficiency of the proposed KMEDHEA in solving the SFTSP.

Bayesian Sequential Design of Computer Experiments for Quantile Set Inversion

We consider an unknown multivariate function representing a system—such as a complex numerical simulator—taking both deterministic and uncertain inputs. Our objective is to estimate the set of deterministic inputs leading to outputs whose probability (with respect to the distribution of the uncertain inputs) of belonging to a given set is less than a given threshold. This problem, which we call Quantile Set Inversion (QSI), occurs for instance in the context of robust (reliability-based) optimization problems, when looking for the set of solutions that satisfy the constraints with sufficiently large probability. To solve the QSI problem we propose a Bayesian strategy, based on Gaussian process modeling and the Stepwise Uncertainty Reduction (SUR) principle, to sequentially choose the points at which the function should be evaluated to efficiently approximate the set of interest. We illustrate the performance and interest of the proposed SUR strategy through several numerical experiments.

A Lab-Scale Evaluation of Parameters Influencing the Mechanical Activation of Kaolin Using the Design of Experiments.

This research investigates the mechanical activation of kaolin as a supplementary cementitious material at the laboratory scale, aiming to optimize milling parameters using the response surface methodology. The study evaluated the effects of rotation speed and milling time on the amorphous phase content, the reduction in crystalline kaolinite, and impurity incorporation into the activated clay through the Rietveld method. The results demonstrated that adjusting milling parameters effectively enhanced clay activation, which is crucial for its use in low-carbon cements. High rotation speeds (300/350 rpm) and prolonged grinding times (90/120 min) in a planetary ball mill increased the pozzolanic activity by boosting the formation of amorphous phases from kaolinite and illite and reducing the particle size. However, the results evidenced that intermediate milling parameters are sufficient for reaching substantial degrees of amorphization and pozzolanic activity, avoiding the need for intensive grinding. Exceedingly aggressive milling introduced impurities like ZrO2 from the milling equipment wear, underscoring the need for a balanced approach to optimizing reactivity while minimizing impurities, energy consumption, and equipment wear. Achieving this balance is essential for efficient mechanical activation, ensuring the prepared clay's suitability as supplementary cementitious materials without excessive costs or compromised equipment integrity.

Analyzing Homogeneity of Highly Viscous Polymer Suspensions in Change Can Mixers.

The mixing of highly viscous non-Newtonian suspensions is a critical process in various industrial applications. This computational fluid dynamics (CFD) study presents an in-depth analysis of non-isothermal mixing performance in change can mixers. The aim of the study was to identify parameters that significantly influence both distributive and dispersive mixing in these mixers, which are essential for optimizing industrial mixing processes. The study employed a numerical design of experiments (DOE) approach to identify the parameters that most significantly influence both distributive and dispersive mixing, as measured by the Kramer mixing index (MKramer) and the Ica Manas-Zloczower mixing index λMZ¯. The investigated parameters included mixing time, number of arms, arm size ratio, revolutions per minute (RPM), z-axis rotation, z-axis movement, and initial and mixing temperatures. The methodology involved employing the bootstrap forest algorithm for predicting the mixing indices, achieving an R2 of 0.949 for MKramer and an R2 of 0.836 for λMZ¯. The results indicate that the z-axis rotation has the greatest impact on both distributive and dispersive mixing. An increased number of arms negatively impacted λMZ, but had a small positive effect on MKramer. Surprisingly, in this study, neither the initial temperature of the material nor the mixing temperature significantly impacted the mixing performance. These findings highlight the relative importance of operational parameters over traditional temperature factors and provide a new perspective on mixing science.

Selective Cross‐Metathesis Versus Ring‐Closing Metathesis of Terpenes, Taking the Path Less Travelled

A diiodo ruthenium olefin metathesis pre‐catalyst was employed to achieve remarkably selective cross‐metathesis reactions of prenylated 1,6‐dienes, effectively overcoming the entropically favored intramolecular ring‐closing metathesis. This reaction was investigated using Density Functional Theory (DFT) computations and fine‐tuned through the application of a Design of Experiments (DoE) approach. The potential of this innovative process was demonstrated through the unprecedented functionalization of various terpene natural products via cross‐metathesis, resulting in the synthesis of new derivatives in a single step.