Michelle Sergent

Mixture design applied to the formulation of hydrotropes for liquid detergents

Abstract In a previous study, we presented a new method for preparing a mixture of sodium toluenesulfonates (STS), sodium xylenesulfonates (SXS), sodium benzenesulfonate (SBS) and Na 2 SO 4 by sulfonating the BTX fraction of a Tunisian natural gas. Such mixtures can be used as a hydrotrope agent for concentrated liquid detergents. In the present work, we performed a mixture design in order to study the effect of each of these four components on the clear point and the viscosity of a liquid detergent, and therefore, to determine the conditions allowing to improve the effectiveness of the hydrotrope. Twenty-eight combinations of the 4 components out of 51 candidate points are selected by the Nemrod-W software according to the D-optimal criterion to fit two polynomial models. The statistical study shows that the fitted models were adequate to describe the clear point and the viscosity responses. Optimal conditions allowing to lower the two responses are then looking for by examining the response surface both as a contour plot, and as three-dimensional surface plot and the response trace. We prove that Na 2 SO 4 exhibits a harmful negative effect, while SXS and STS exhibit, respectively, a strong and moderate positive effect on both clear point and viscosity responses. As expected, SBS has a harmful effect on the two responses but the magnitude of this effect is lesser than that predicted by the preliminary experiments carried out with SBS alone. This phenomenon is explained by the formation of heteroassociation between SBS, STS and SXS similar to what is found in surfactants. The effectiveness of the hydrotrope, obtained by sulfonation of the BTX fraction of the Tunisian natural gas, is really improved by removing sulfates either by adding lime to precipitate gypsum, or isopropanol to reduce the solubility of Na 2 SO 4 .

Correct and incorrect use of multilinear regression

Abstract Multilinear regression is applied when experimenters wish to investigate the relationship between a block of predictor variables ( X ), whose values are fixed by the experimenter, and one or more responses ( Y ), measured at each experiment. The objective is to find a mathematical equation relating X and Y , by means of regression coefficients. Regression analysis is said to give good results when a squared multiple regression coefficient R 2 close to 1 is obtained and when the sum of squares of residuals (differences between experimental value of the response and computed value) is small. However, it must be noted that a bad choice of the experiments renders the obtained regression equation meaningless. In this paper an example of correct and incorrect use of multilinear regression is presented in detail the quality of the coefficients and the goodness of the prediction depend on the experimental design, and the value of R 2 gives no information at all about them. A set of criteria useful to judge the quality of an experimental plan, before carrying out any experiment, is proposed.

Assessment of potential positive effects of nZVI surface modification and concentration levels on TCE dechlorination in the presence of competing strong oxidants, using an experimental design

Nanoscale zero-valent iron (nZVI) particles are efficient for the remediation of aquifers polluted by trichloroethylene (TCE). But for on-site applications, their reactivity can be affected by the presence of common inorganic co-pollutants, which are equally reduced by nZVI particles. The aim of this study was to assess the potential positive effects of nZVI surface modification and concentration level on TCE removal in the concomitant presence of two strong oxidants, i.e., Cr(VI) and NO3(-). A design of experiments, testing four factors (i.e. nZVI concentration, nZVI surface modification, Cr(VI) concentration and NO3(-) concentration), was used to select the best trials for the identification of the main effects of the factors and of the factors interactions. The effects of these factors were studied by measuring the following responses: TCE removal rates at different times, degradation kinetic rates, and the transformation products formed. As expected, TCE degradation was delayed or inhibited in most of the experiments, due to the presence of inorganics. The negative effects of co-pollutants can be palliated by combining surface modification with a slight increase in nZVI concentration. Encouragingly, complete TCE removal was achieved for some given experimental conditions. Noteworthily, nZVI surface modification was found to promote the efficient degradation of TCE. When degradation occurred, TCE was mainly transformed into innocuous non-chlorinated transformation products, while hazardous chlorinated transformation products accounted for a small percentage of the mass-balance.

Parameters optimization using experimental design for headspace solid phase micro-extraction analysis of short-chain chlorinated paraffins in waters under the European water framework directive

The water framework directives (WFD 2000/60/EC and 2013/39/EU) force European countries to monitor the quality of their aquatic environment. Among the priority hazardous substances targeted by the WFD, short chain chlorinated paraffins C10-C13 (SCCPs), still represent an analytical challenge, because few laboratories are nowadays able to analyze them. Moreover, an annual average quality standards as low as 0.4μgL(-1) was set for SCCPs in surface water. Therefore, to test for compliance, the implementation of sensitive and reliable analysis method of SCCPs in water are required. The aim of this work was to address this issue by evaluating automated solid phase micro-extraction (SPME) combined on line with gas chromatography-electron capture negative ionization mass spectrometry (GC/ECNI-MS). Fiber polymer, extraction mode, ionic strength, extraction temperature and time were the most significant thermodynamic and kinetic parameters studied. To determine the suitable factors working ranges, the study of the extraction conditions was first carried out by using a classical one factor-at-a-time approach. Then a mixed level factorial 3×2(3) design was performed, in order to give rise to the most influent parameters and to estimate potential interactions effects between them. The most influent factors, i.e. extraction temperature and duration, were optimized by using a second experimental design, in order to maximize the chromatographic response. At the close of the study, a method involving headspace SPME (HS-SPME) coupled to GC/ECNI-MS is proposed. The optimum extraction conditions were sample temperature 90°C, extraction time 80min, with the PDMS 100μm fiber and desorption at 250°C during 2min. Linear response from 0.2ngmL(-1) to 10ngmL(-1) with r(2)=0.99 and limits of detection and quantification, respectively of 4pgmL(-1) and 120pgmL(-1) in MilliQ water, were achieved. The method proved to be applicable in different types of waters and show key advantages, such as simplicity, automation and sensitivity, required for the monitoring programs linked to the WFD.

Experimental research methodology applied to the development of a formulation for use with textiles

Abstract Chardon, J., Nony, J., Sergent, M., Mathieu, D. and Phan-Tan-Luu, R., 1989. Experimental research methodology applied to the development of a formulation for use with textiles. Chemometrics and Intelligent Laboratory Systems, 6: 313-321. The development of a finishing product formula for polyester/cotton cloth for use as bed linen for the hotel trade required four criteria to be taken into consideration: the fabrics feel, hydrophilicity, wash and wear quality, and soil release after treatment. The finishing product formula was based on the use of a resin (and its catalyst) and from 1 to 3 fabric softeners. The problem lay in detertmining the nature of the fabric softeners and the relevant quantities of resin and softener needed to obtain the best characteristics. A two-phase experimental strategy was drawn up: one phase for the quantities of resin and softeners, and the other phase for the types of softeners used. The quantities phase was studied using a Doehlert experimental matrix and the softener type phase was assessed using a Scheffe simplex-centroid design for the three products. The experiments carried out allowed the definition of a zone of two phases in which the objectives were attained: two softeners were selected and the appropriate quantities of resin and softeners were determined.

High-dimensional sensitivity analysis of complex optronic systems by experimental design: applications to the case of the design and the robustness of optical coatings

We present the advantages of experimental design in the sensitivity analysis of optical coatings with a high number of layers by limited numbers of runs of the code. This methodology is effective in studying the uncertainties propagation, and to qualify the interactions between the layers. The results are illustrated by various types of filters and by the influence of two monitoring techniques on filter quality. The sensitivity analysis by experimental design of optical coatings is useful to assess the potential robustness of filters and give clues to study complex optronic systems. OCIS codes: 310.0310, 220.0220, 120.0120. doi: 10.3788/COL201008S1.0021. The study of complex optronic systems entails sensitivity analysis with a large number of parameters. Very often the response depends on synergies or interactions between these parameters. Due to interference characteristics of multilayer filters, optical coatings make possible the evaluation of methods that can explore highdimensional space parameters and the presence of interactions between parts of these parameters. For coatings production with a high number of layers, sensitivity analysis is an efficient way to determine the most critical layers of an optical coating [1] . Refractive index errors or thickness errors during the manufacturing of these layers can induce dramatic consequences on the desired optical properties [2] . We present the advantages of using the method of experimental design [3] , which is used for metamodel constructions and high-dimensional code explorations with limited numbers of runs of the code, particularly in the case of coatings with a high number of layers. This methodology is more effective in studying uncertainties propagation (refractive index or thickness values) to determine the influence of errors on the optical properties, and to quantify the interactions between the errors of each layer. The results are illustrated by various types of filters, particularly bandpass filters and multiple halfwave filters. Different designs such as factorial, fractional factorial, and space-filling designs are used to present the results. Furthermore, we study the influence of two monitoring techniques, and show the most critical coating layers and the dependency of these layers with future manufacturing. The results show that the study of thin-film filters is very useful in examining the interactions of highdimensional systems due to the filter’s adjustable number of layers, and the existence of interactions between these layers. Finally, we demonstrate that sensitivity analysis of optical coatings by experimental design is useful in assessing the potential robustness of filters, and gives clues to study complex optronic systems. The codes to study complex phenomena become more and more realistic with a larger input data set. However, due to the complexity of the mathematical system underlying the computer simulation tools, there are often no explicit input-output formulas. Although computer power has significantly increased in the past years, the evaluation of a particular setting of the design parameters may still be very time-consuming. The simulator is often replaced by a metamodel to approximate the relationship between the code and the design parameters. These metamodels are built using numerical designs of experiments that can indicate interactions between the parameters. The choice of an underlying empirical model (depending on accuracy and interactions level) can be written as Y = Cste + ∑ i biXi + ∑ i

Biological wheat straw valorization: Multicriteria optimization of Polyporus brumalis pretreatment in packed bed bioreactor

The purpose of this work was to optimize the pretreatment process of wheat straw by Polyporus brumalis_BRFM985 in order to improve carbohydrate accessibility for more efficient bioconversion. Indeed, there is growing demands to develop sustainable routes for lignocellulosic feedstocks valorization into value‐added products in energy, chemicals, materials, and animal feed fields. To be achieved, implementation of cheap and ecofriendly biomass pretreatment processes is necessary. In this frame, white rot basidiomycetes, well known for their ability to degrade lignin efficiently and selectively, are of great interest. The pretreatment of wheat straw by Polyporus brumalis_BRFM985 was performed in packed bed bioreactor and optimized using response surface methodology. The four pretreatment parameters optimized were metals addition (Cu, Mn, and Fe), time of culture, initial water content, and temperature. Multicriteria optimization highlighted that wheat straw pretreatment by Polyporus brumalis_BRFM985 in the presence of metals with high initial water content of 3.6 g H2O/g at 27°C for 15–16 days led to an improvement of carbohydrate accessibility with minimal matter loss.

Methodological Approach of the Experimental Research

Abstract Many preliminary studies contain a large number of potentially relevant factors, but often only a few are believed to have actual effects. To isolate influential factors ( screening ) in an efficient way, special experimental strategies are needed, such as the supersaturated designs or the techniques of group screenin. For investigating k factors, these supersatured designs are composed of n observations where n ≪ k + 1. They are useful in situations in which the number of active factors is very small compared to the total number of factors being considered; more specifically, in practice experiments with simulated systems.