Screening Experimental Design Research Articles

Olaparib Process Development Employing Quality by Design (QbD) Principles.

This study focuses on multivariate experimental design and statistical analysis to optimize the process of Olaparib 1. Quality by design (QbD) methodology was adopted for optimization of the Olaparib process consisting of three reaction steps: (1) amidation, (2) deprotection, and (3) acylation. Every chemical conversion was studied in isolation, employing risk assessment to identify key material attributes and key process parameters that may have the potential to impact the reaction. Thereafter, the screening design of experiment (DoE) was employed to scrutinize the factors that significantly impacted yield. Moving forward, the scrutinized factors which were found to impact the responses, the set of critical material attributes (CMAs) and critical process parameters (CPPs), were considered for optimization by applying I-Optimal design to define design space arriving at a robust setting wherein the predefined targets were supposedly optimal. To our delight, we got 95, 91, and 75% yield with more than 99% purity in amidation, deprotection, and acetylation, respectively, which enabled us to systematically identify design space to meet the desired quality target of the product consistently. More importantly, to distinguish the CMAs and CPPs, these elements ought to be monitored to have control of the quality parameter throughout the active pharmaceutical ingredient (API) value chain until commercial manufacturing followed by marketing. Eventually, we have developed a greener process in comparison to precedented one for Olaparib 1.

Model-based catalyst screening and optimal experimental design for the oxidative coupling of methane

The oxidative coupling of methane (OCM) to produce ethane and ethylene (C2 compounds) as platform chemicals involves complex chemistry with reactions both in the gas phase and on the catalyst surface, resulting in a distribution of products at the expense of C2 selectivity. This work uses experimental data from a variety of mixed metal oxides on supports at different reaction conditions (temperature, contact time, and reactant flow rates) to train a random forest regressor that predicts methane conversion and C2 selectivity (key performance indicators (KPIs)). The kinetically validated random forest models are deployed to locate optimal conditions that maximize C2 yield for each of the catalysts. Investigating the regressor interpretability via feature importance reveals that the choice of metals and support are crucial to C2 selectivity predictions in addition to the reaction conditions, while the predictions of methane conversion are largely governed by the reaction conditions. The machine learning (ML) regressor is used as a kinetic surrogate to find a locus of optimal reaction conditions that maximize both selectivity-conversion for each of the catalysts via a multi-objective optimization routine. The maximum C2 yields for catalysts are projected to be improved by 15% on average. Analyzing the catalysts with respect to a popular OCM catalyst, Mn-Na2WO4/SiO2, using the optimal locus eliminates variability in the process conditions to reveal distinct patterns based on intrinsic properties of metals and supports. Further, the decision space with catalyst descriptors and reaction conditions is optimized for high C2 yields using the ML surrogate, in a static multi-objective optimization routine, and an adaptive Bayesian routine, where the latter was found to have a wider field focus in proposing catalyst formulations and reaction conditions. Transition metal oxides on a variety of supports were proposed but not their lanthanide oxide counterparts. The framework has the potential to lend itself to materials acceleration platforms where it is crucial to consider multi-scale phenomena that impact downstream KPIs.

Measuring aliphatic hydrocarbons in sediments by direct thermal desorption-gas chromatography-mass spectrometry: Matrix effects and quantification challenges

Direct sample introduction thermal desorption (TD) coupled to GC–MS was investigated for the analysis of paraffinic hydrocarbons (HCs) from polluted sediments. TD-GC–MS is sometimes used for analysing paraffinic HCs from atmospheric particles but rarely for their direct desorption from sediments. So, the new TD methodology, applied to sediments, required development, optimization and validation. A definitive screening experimental design was performed to discriminate the critical factors on TD efficiency, from model sediments containing various organic matter (OM) amounts. Low molecular weight HCs had extraction behaviours markedly different from high molecular ones (HMW-HCs), but a compromise was found using very few sediment amount (5 mg), high temperature rate (55 °C min−1) and final temperature (350 °C). Linear HCs (n-C10 to n-C40) could be quantified using the matrix-matched calibration method, with very low detection limits (3.8–13.4 ng). The amount of the overall paraffinic alkanes was also determined as a sum of unresolved components between predefined equivalent carbon ranges. The developed solventless methodology was compared to an optimized solvent microwave assisted extraction (MAE). Matrix effects could be higher for TD compared to MAE but it depended on sediment matrix. When matrix effect was strong, particularly on HMW-HCs signal depletion, a dilution with pure non-porous sand was favourable for accurate quantification. The sum of resolved and unresolved HCs gave comparable results between MAE and TD extractions, with an exception of alkanes greater than C30 which were less quantitatively extracted via TD. However, TD-GC–MS was more sensitive than MAE-GC–MS. So TD-GC–MS is useful for analyzing sediments containing a great range of paraffinic HCs (C9-C34) and it has the advantages of being fully automated, with few sample preparation and operator intervention, using very low amounts of solvent, and generating few wastes.

Fast-dissolving calcium carbonate particles as a high-performance lime for amelioration of agricultural acidic soils

Effective soil management is essential for maximizing crop yields, as soil fertility plays a pivotal role in this process. Unfortunately, liming and fertilization practices are frequently ineffective, leading to overdoses and unnecessary product waste. Liming is essential for correcting soil acidity, mitigating aluminum toxicity, and promoting nutrient bioavailability before fertilizer application. This study explores the potential of vaterite, a highly soluble CaCO3 polymorph, as a sustainable liming agent. Vaterite particle synthesis is challenging because of its poor stability and rapid phase transformation into calcite, therefore, we analyze the effects of precursor concentration, Ca2+/CO32- ratio, and stirring rate on vaterite morphology and content in a reactive precipitation process through screening and D-optimal experimental designs, resulting in a design space to tailor the conditions that promote vaterite particles with a high dissolution profile at the lowest possible cost. To assess the efficacy of fast-dissolving CaCO3 particles, a comparative incubation test was conducted in andisol-type soil with a high buffering capacity. The results demonstrated that vaterite particles were capable of quickly adjusting the soil pH in the first few days using 360% fewer conventional liming agents. This innovative strategy aligns with Precision Agriculture principles, enabling efficient crop production while minimizing the environmental impact.

Comprehensive analysis of vitamin D3 impurities in oily drug products using supercritical fluid chromatography-mass spectrometry.

Vitamin D3 , an essential micronutrient, often requires supplementation via medicines or food supplements, which necessitate quality control (QC). This study presents the development of a method for detecting and quantifying seven impurities of vitamin D3 in oily drug products using supercritical fluid chromatography-mass spectrometry (SFC-MS). Targeted impurities include two esters of vitamin D3 and five non-esters including four that are isobaric to vitamin D3 . Firstly, a screening study highlighted the Torus 1-AA column and acetonitrile modifier as adequate for the separation, followed by optimization of the SFC conditions. Secondly, make-up solvent composition and MS settings were optimized to reach high sensitivity. For both the separation and MS response, the screening design of experiments proved useful. Lastly, a fast saponification and liquid-liquid extraction method was developed, enabling efficient sample cleanup and impurities recovery from the complex oily matrix. The SFC-MS method suitability was assessed in two validation studies. The first study employed the ICH Q2 guideline for impurity limit test to demonstrate method specificity and establish a limit of detection (LOD) and a limit of quantification (LOQ) at 0.2% and 0.5%, respectively, for ester impurities. The second study conducted a comprehensive quantitative assessment for three non-ester impurities using a total error approach, determining method validity through accuracy profiles. The validated method exhibited reliable performance across impurity concentrations from 0.1% to 2.0%, with estimated LODs ranging from 2 to 7ng/mL. This study further promotes SFC-MS as a valuable, versatile, and green tool for routine pharmaceutical QC.

Screening of variables affecting the selective leaching of valuable metals from waste motherboards' PCBs.

The presence of valuable and hazardous metals in waste printed circuit boards, especially, motherboards, makes their recovery necessary as implies great economic and environmental advantages and develops urban mining processes. Hence, this research is focused on the selective leaching of Cu, Pb, and Sn as base metals using nitric acid and hydrochloric acid and Au, Ag, and Pd as precious metals using thiourea and sodium thiosulfate from waste motherboards' PCBs in a sequential eco-friendly two-stage process. Previously, thiourea and sodium thiosulfate were used as leaching agents to investigate their applicability for the leaching of metals from PCBs in a single-stage process. Screening experimental design was applied to screen the variables affecting the leaching process in order to evaluate their impact on the recovery of metals and select the significant factors. The results demonstrated that base and precious metals can be leached appropriately in two consecutive stages compared to a single-stage process. Nitric acid was found to be a much more efficient agent to leach Cu and Pb in comparison with hydrochloric acid which was more suitable for the leaching of Sn. In the case of precious metals, higher amounts of Au were leached using thiourea, whereas sodium thiosulfate was able to leach more Pd. Roughly similar results were obtained for the leaching of Ag using these leaching agents. Nitric acid concentration, average particle size, temperature, and leaching time were found to be significant to maximize the leaching of Cu and Pb and minimize that for Au, Ag, and Pd in the first stage. Initial pH was the only variable influencing the second stage, in particular, Au leaching by thiourea.

Gradient process parameter optimization in additive friction stir deposition of aluminum alloys

As one of the most novel additive manufacturing methods, currently selection and optimization of processing parameters in additive friction stir deposition (AFSD) have mainly relied on experiments and subsequent characterization of microstructural and mechanical properties. Such approaches are both time- and resource-consuming. Therefore, an ultrasound elastography enhanced gradient process parameter optimization method was applied in the present work to obtain a window of optimized processing parameters for AFSD processing of aluminum alloy by varying both rotational and linear deposition speeds. The quality of AFSD processed layer was investigated for physical nature of surface, dynamic elastic modulus, and microstructural aspects in cross-sections of the deposited layer. The efficiency in exploring process parameters was significantly enhanced by implementing a high-throughput screening experimental design based on application of gradient process parameters and continuous ultrasound elastographs. In addition, the applied ultrasonic elastography technique assisted in evaluating the homogeneity in microstructure and mechanical properties of AFSD sample over the entire gradients of the process parameters. The techniques adopted in current work can be further extended to identify suitable parameters for AFSD fabrication of components with desired mechanical properties such as hardness, fatigue, etc.

Fabric phase sorptive extraction as a sustainable sample preparation procedure to determine synthetic musks in water

A rapid sample preparation fabric phase sorptive extraction (FPSE) methodology followed by gas chromatography coupled to tandem mass spectrometry is proposed for the first time to determine 12 synthetic musks including nitro-, polycyclic and macrocyclic musk in environmental water and wastewater. An asymmetric screening design of experiments was used to evaluate the influence of nine parameters affecting FPSE in only 16 experiments. The factors included the sol–gel sorbent coating, stirring mode (both extraction and desorption), extraction and desorption time, salting-out effect, sample volume, type of solvent, and desorption solvent volume. The selected conditions imply the use of the sol–gel PDMS sorbent coating, 15 min extraction by ultrasound energy employing 10 mL of sample and 0.5 mL of ethanol as desorption solvent during 5 min. Accuracy and precision were assessed in different real water samples. Matrix effects were evaluated performing recovery studies in several aqueous matrices: river, sea, spring, laundry washing place, and wastewater at different concentration levels (0.1, 2, 10 µg L−1) demonstrating method accuracy (results ranged between 82 and 110%) and good precision (relative standard deviation, RSD < 12% in all cases). Limits of detection (LODs) were below 8 ng L−1 for the analyzed synthetic musks. As extraction was quantitative (exhaustive extraction), calibration was carried out using solvent standards without the need of repeating the extraction step, which is required in most microextraction methods, improving sample throughput, and reducing costs. Finally, the analysis of real contaminated samples revealed the presence of 11 out of the 12 target synthetic musks at concentrations up to 30 µg L−1, three of them banned in cosmetic regulation (musk tibetene, ambrette, and moskene). The developed FPSE based methodology uses low volume (0.5 mL) of a Generally Recognized as Safe (GRAS) solvent (ethanol), as well as minimum energy and time consumption, demonstrating to be a sustainable alternative to detect these microcontaminants in environmental waters and wastewaters.

Scalable level-wise screening experiments using locating arrays

Alternative design and analysis methods for screening experiments based on locating arrays are presented. The number of runs in a locating array grows logarithmically based on the number of factors, providing efficient methods for screening complex engineered systems, especially those with large numbers of categorical factors having different numbers of levels. Our analysis method focuses on levels of factors in the identification of important main effects and two-way interactions. We demonstrate the validity of our design and analysis methods on both well-studied and synthetic data sets and investigate both statistical and combinatorial properties of locating arrays that appear to be related to their screening capability.

Method to Rapidly Identify Potential Solvent Systems for Crystallization of Cocrystals

This study aims at easing the design of crystallization and the cake washing step of cocrystals. It is assessed if solvent systems can be classified based on the dissolution behavior of the cocrystal by leveraging on the solubility of its pure components. Solubility data of pure components are typically readily accessible data in the pharmaceutical industry at an early stage of development. For the three model systems considered in this study, the composition of eutectic liquid phases was determined at 20 °C in numerous solvent systems and compared with the solubility of pure components at the same conditions. In the solvent systems in which the cocrystal dissolves congruently, the solubilities of the pure components are similar. Incongruent dissolution behavior is in turn consistently observed if the solubilities of the pure components differed by at least one order of magnitude. For these solvent systems, the eutectic liquid phases are consistently enriched in the more soluble component. Comparison of the solubilities of the pure components thus enables determination of the component that needs to be charged in excess to reach thermodynamic stability of the cocrystal, which is particularly relevant for the design of screening and solubility experiments. Finally, highly asymmetric phase diagrams, which are not suitable for crystallization and cake washing purposes, are easily identifiable by highly dissimilar solubilities of pure components.

Finasteride-loaded nano-lipidic carriers for follicular drug delivery: preformulation screening and Box-Behnken experimental design for optimization of variables

Finasteride (FIN), a 5-α reductase enzyme inhibitor is mainly used orally for the treatment of androgenic alopecia and benign prostate hyperplasia. The present study was undertaken for systematic optimization and assessment of the designed nanostructured lipid carriers (NLC) to enhance follicular delivery of FIN by topical administration. The NLCs were prepared by microemulsion method, by employing a 33 Box-Behnken design and subsequently confirmed by ANOVA analysis. Compritol ATO-888 and Fenugreek oil were selected as the solid lipid and liquid lipid respectively for the fabrication of NLCs. The formulations were characterized for particle size, zeta potential, entrapment efficiency, storage stability and in vitro drug release profile. Morphological profile of the NLCs nanocarriers was studied by transmission electron microscopy (TEM). The Fourier Transform Infrared Spectroscopy (FT-IR) spectrum and differential scanning calorimetry (DSC) thermogram demonstrated that FIN entrapment within NLCs was devoid of chemical interaction with the components. The prepared NLCs had satisfactory particle dimensions, zeta potential and entrapment efficiency. The numerical optimization process indicated the optimal NLC composition with 3 mg of SPC, 6 mg lipid and 5 mg of drug. NLCs loaded with FIN had acceptable particle size at 379.8 nm, zeta potential of −37.1 mV and an entrapment efficiency of 84%. Transmission electron microscopy indicated the spherical morphology. In vitro release profile indicated a fast initial release and subsequently a prolonged release of FIN from the carrier for 24 h. The release kinetics data displayed a Higuchi diffusion release model with the best match R2 value (0.848). Short-term stability tests conducted over 4 weeks at 6° and 25 °C demonstrated that the formulation could retain their initial properties during the test period.

A Core-Shell Amino-Functionalized Magnetic Molecularly Imprinted Polymer Based on Glycidyl Methacrylate for Dispersive Solid-Phase Microextraction of Aniline

A core-shell amino-functionalized glycidyl methacrylate magnetic molecularly imprinted polymer (MIP) was synthesized by the suspension polymerization/surface imprinting method and characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), mercury porosimetry, nitrogen gas adsorption–desorption, and elemental analysis. This MIP was used as the sorbent in dispersive solid-phase microextraction (DSPME) of aniline from textile wastewater prior to high-performance liquid chromatography-mass spectrometry (HPLC-MS) measurements. Since aniline is toxic and a probable human carcinogen, its determination in water is of great significance. This is a challenging task because aniline is usually present at trace levels. The effects of different DSPME variables on the preconcentration efficiency have been studied by using the Plackett–Burman screening design of experiments (DoE) followed by response surface methodology optimization using the Box-Behnken design. Thus, DoE enabled the investigation of several variables simultaneously. Under optimized conditions, aniline was effectively and selectively separated by a small amount of the DSPME sorbent and detected in real textile wastewater samples. The method detection limit of 1 ng mL−1 was attained, with good method linearity and acceptable recovery and precision. The results showed that the studied MIP could be a reliable DSPME sorbent for efficiently analyzing trace aniline in real wastewater samples.

Development, Characterization, Optimization, and In Vivo Evaluation of Methacrylic Acid-Ethyl Acrylate Copolymer Nanoparticles Loaded with Glibenclamide in Diabetic Rats for Oral Administration.

The methacrylic acid–ethyl acrylate copolymer nanoparticles were prepared using the solvent displacement method. The independent variables were the drug/polymer ratio, surfactant concentration, Polioxyl 40 hydrogenated castor oil, the added water volume, time, and stirring speed, while size, PDI, zeta potential, and encapsulation efficiency were the response variables analyzed. A design of screening experiments was carried out to subsequently perform the optimization of the nanoparticle preparation process. The optimal formulation was characterized through the dependent variables size, PDI, zeta potential, encapsulation efficiency and drug release profiles. In vivo tests were performed in Wistar rats previously induced with diabetes by administration of streptozotocin. Once hyperglycemia was determined in rats, a suspension of nanoparticles loaded with glibenclamide was administered to them while the other group was administered with tablets of glibenclamide. The optimal nanoparticle formulation obtained a size of 18.98 +/− 9.14 nm with a PDI of 0.37085 +/− 0.014 and a zeta potential of −13.7125 +/− 1.82 mV; the encapsulation efficiency was of 44.5%. The in vivo model demonstrated a significant effect (p < 0.05) between the group administered with nanoparticles loaded with glibenclamide and the group administered with tablets compared to the group of untreated individuals.

Screening Design of Experiments of AGET ATRP of Butyl Methacrylate in a Stirred Emulsion Reactor

AbstractThis study investigates the ab initio atom transfer radical polymerization of butyl methacrylate using activator generated by electron transfer in a well‐mixed 2 L emulsion reactor. A first time reported polymerization system initiates by a catalyst complex with hexamethylenetetramine as a ligand and in presence of sodium dodecyl sulfate as an anionic surfactant. The system shows a relatively low monomer conversion rate of 14% and a controlled number average molecular weight (Mn) of 8 kg mol−1 with a polydispersity index (Ð) of 1.24 for 2 h reaction time. A statistical design of experiments is followed to study the effects of reaction temperature, surfactant amount, stirring speed, and ligand amount to assess their impact on the monomer conversion, Mn, and Ð. A fractional factorial with a resolution IV design is adopted and an empirical regression model is developed from eight experimental trials. The results tell that the reaction temperature has the greatest influential on the process outputs. Also, the surfactant amount and stirring speed strongly affect the conversion rate, while the ligand amount strongly affects the Mn and Ð. The results also show that the temperature–surfactant interaction is the most significant on the conversion rate and Mn.

Development and Optimization of Itraconazole-Loaded Solid Lipid Nanoparticles for Topical Administration Using High Shear Homogenization Process by Design of Experiments: In Vitro, Ex Vivo and In Vivo Evaluation.

The aim of present study was to develop topical itraconazole (ITZ)-loaded solid lipid nanoparticles for treatment of superficial fungal infections. Formulations were prepared using high shear homogenization process, and optimized by employing a two-step design of experiments (DoE) approach. It comprised a Taguchi experimental design for screening of 'vital few' factors, and a central composite experimental design for optimization. Overlay of the response surface maps for percent drug entrapment (PDE), particle size, ITZ skin retention and permeation was performed to obtain the optimized ITZ-loaded SLNs (OPT-SLNs) suspension. The optimized ITZ-loaded SLNs (OPT-SLNs) showed mean particle size of (262.92 ± 8.56nm) and zeta potential value of 22.36mV. Excellent drug entrapment (94.21 ± 3.35%) and skin retention of ITZ (43.03 ± 1.86μg/cm2) was achieved by OPT-SLNs. The hydrogel formulation of OPT-SLNs exhibited good gel consistency and spreadability characteristics. Pharmacodynamic and skin sensitivity studies in standardized rodent models revealed that OPT-SLNs hydrogel was more efficacious than conventional oral and topical antifungal therapies, and also safe for topical administration. Furthermore, the histoptahological evaluation depicted complete recovery of infected rats after 14-day treatment regimen of OPT-SLNs hydrogel. The developed formulation was found to have tremendous potential to enhance ITZ activity through topical administration approach.

The Effect of Class-Wide Enrichment Applied to Gifted and Normal Children in Early Childhood

The purpose of the study was two-fold: The first purpose of the research was to conduct the "adaptation study of Brigance K &amp;amp; 1 screen II to Turkish," while the second was to “investigate the effect of the enrichment program applied to gifted children and their classmates in the 1st grade of primary education on their performance in multiple intelligence fields". The research was conducted in the screening model and experimental design. The study group consisted of 477 primary schools first grade students (6 years old). Data collection tools are as follows, Brigance K &amp;amp; 1 Screen II scale, Goodenough-Harris draw-a-person test, Teele Multiple Intelligence Preference Inventory (TIMI), Enrichment Program Pre-test -Post-test, and "Project Evaluation Criteria List." After the screening process, a class-wide enrichment program based on multiple intelligence was applied to gifted and talented children and their classmates. Statistical Package for the Social Sciences (SPSS) was used to analyze the data at hand. The findings of the study revealed that Brigance K &amp;amp; 1 Screen II scale was effective in determining gifted children attending the first grade of primary school with a rough evaluation. The enrichment program results were different from the existing school program in favor of enrichment. The class-wide enrichment program has improved all children, with a more potential for gifted children. As a part of the program, a total of 163 students were given project assignments. At the end of the evaluation, it was determined that the students improved in multiple intelligence areas.

Effect of Mixing Elements on Granule Formation in Hot Melt Twin Screw Granulation

Twin screw granulation (TSG) has been applied to wet granulation, although its application in melt granulation has been more limited. This work explores potential advantages of hot melt granulation using twin screw extrusion. Four main operating and formulation parameters were investigated: screw speed, number of mixing elements, temperature, and binder percentage. Combinations of these factors were then studied to determine their impact on the quantity and characteristics of granules within the desired size range of 125 – 1000 µm. A screening design of experiments (DOE) study was used with each factor set at three levels, to investigate individual factor effects and interactions. Two types of mixing elements were studied: kneading block (KB) and chaotic elements. The type and number of mixing elements were found to be paramount in contributing to the quantity and characteristics of granules formed. Results obtained agreed with previous findings in literature on the influence of different screw elements on the characteristics of granules formed by twin screw granulation. Additionally, the study revealed the unique impact which different mixer elements have on both granule production and characteristics. Depending on the specific need or use of granules in required applications, the granulation process can be effectively designed to meet the end product quality and outcome.

A comparative evaluation of design factors on bubble column operation in photosynthetic biogas upgrading

Studies attempting to optimise photosynthetic biogas upgrading by simultaneous investigation of the bubble column-photobioreactor setup have experienced considerable variability in results and conclusions. To identify the sources of such variation, this work quantitatively compared seven design factors (superficial gas velocity; liquid to gas flow rate (L/G) ratio; empty bed residence time; liquid inlet pH; liquid inlet alkalinity; temperature; and algal concentration) using the L16 Taguchi orthogonal array as a screening design of experiment. Assessments were performed using the signal to noise (S/N) ratio on the performance of CO2 removal (CO­2 removal efficiency, CO2 absorption rate, and overall CO2 mass transfer coefficient) and O2 stripping (O2 concentration in biomethane and O2 flow rate in biomethane). Results showed that pH and L/G ratio were the most critical design factors. Temperature and gas residence times had minimal impact on the biomethane composition. The interactive effect between pH and L/G ratio was the most impactful, followed by the interactive effects between superficial gas velocity and L/G ratio and pH on CO2 removal efficiency. The impact of L/G ratio, algal concentration, and pH (in that order of impact) caused up to a 90% variation in oxygen content in biomethane. However, algal concentration had a diminishing role as the L/G ratio increased. Using only the statistically significant main effects and interactions, the biomethane composition (CO2% and O2%) was predicted with over 95% confidence through regression equations for superficial gas velocity up to 0.2 cm/s.

Development of a novel method for the in-situ dechlorination of immovable iron elements: optimization of Cl\u2212 extraction yield through experimental design

The conservation of iron objects exposed to marine aerosol is threatened by the formation of akaganeite, a highly unstable Cl-bearing corrosion phase. As akaganeite formation is responsible of the exfoliation of the rust layer, chlorides trigger a cyclic alteration phenomenon that often ends with the total consumption of the iron core. To prevent this degradation process, movable iron elements (e.g. archaeometallurgical artefacts) are generally immersed in alkaline dechlorination baths. Aiming to transfer this successful method to the treatment of immovable iron objects, we propose the in-situ application of alkaline solutions through the use of highly absorbent wraps. As first step of this novel research line, the present work defines the best desalination solution to be used and optimizes its extraction yield. After literature review, a screening experimental design was performed to understand the single and synergic effects of common additives used for NaOH baths. Once the most effective variables were selected, an optimization design was carried out to determine the optimal conditions to be set during treatment. According to the experimental work here presented, the use of 0.7 M NaOH solutions applied at high temperatures (above 50 °C) is recommended. Indeed, these conditions enhance chloride extraction and iron leaching inhibition, while promoting corrosion stabilization.

Predicting the Mixing Behavior of Aqueous Solutions Using a Machine Learning Framework.

The most direct approach to determining if two aqueous solutions will phase-separate upon mixing is to exhaustively screen them in a pair-wise fashion. This is a time-consuming process that involves preparation of numerous stock solutions, precise transfer of highly concentrated and often viscous solutions, exhaustive agitation to ensure thorough mixing, and time-sensitive monitoring to observe the presence of emulsion characteristics indicative of phase separation. Here, we examined the pair-wise mixing behavior of 68 water-soluble compounds by observing the formation of microscopic phase boundaries and droplets of 2278 unique 2-component solutions. A series of machine learning classifiers (artificial neural network, random forest, k-nearest neighbors, and support vector classifier) were then trained on physicochemical property data associated with the 68 compounds and used to predict their miscibility upon mixing. Miscibility predictions were then compared to the experimental observations. The random forest classifier was the most successful classifier of those tested, displaying an average receiver operator characteristic area under the curve of 0.74. The random forest classifier was validated by removing either one or two compounds from the input data, training the classifier on the remaining data and then predicting the miscibility of solutions involving the removed compound(s) using the classifier. The accuracy, specificity, and sensitivity of the random forest classifier were 0.74, 0.80, and 0.51, respectively, when one of the two compounds to be examined was not represented in the training data. When asked to predict the miscibility of two compounds, neither of which were represented in the training data, the accuracy, specificity, and sensitivity values for the random forest classifier were 0.70, 0.82 and 0.29, respectively. Thus, there is potential for this machine learning approach to improve the design of screening experiments to accelerate the discovery of aqueous two-phase systems for numerous scientific and industrial applications.