Flexible Experiment Introducing Factorial Experimental Design

Laboratory Experiment Cite This: J. Chem. Educ. XXXX, XXX, XXX−XXX

pubs.acs.org/jchemeduc

Flexible Experiment Introducing Factorial Experimental Design Penny Snetsinger* and Eid Alkhatib Department of Chemistry, Sacred Heart University, Fairfield, Connecticut 06825, United States S Supporting Information *

ABSTRACT: Although experimental design is important in many fields and industries, most undergraduate students do not get exposure to this in a standard lab curriculum. This work describes a student-designed, multiple-week, flexible chemistry experiment with environmental applications that include factorial experimental design and analysis of variance statistical analysis. Through the experiment, students attempt to gain a better understanding of activated carbon adsorption of dyes under a set of factors, which could include type of dye, type of carbon, pH, salinity, hardness, and time of contact, each at multiple levels. The experiment is easily adaptable for a range of student enrollments and uses only common laboratory glassware and any visible spectrometer.

KEYWORDS: Upper-Division Undergraduate, Laboratory Instruction, Interdisciplinary/Multidisciplinary, UV−Vis Spectroscopy, Hands-On Learning/Manipulatives

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INTRODUCTION

Specifically, the learning outcomes are that at the conclusion of this activity, participants will be able to • Plan and execute a team-driven experimental design and assign responsibilities to collect requisite data, and calculate and interpret results. • Recognize the advantage of factorial experimental design over the traditional “one factor at a time” method of studying a system. • Use ANOVA statistical parameters to determine whether an experimental parameter and/or interaction between parameters affects the adsorption of dye onto activated carbon. The study of the adsorption of materials onto activated carbon and the Freundlich and Langmuir isotherms are often covered as part of a physical chemistry lab course.8,9 Most students are familiar with activated carbon in water filters and as a hospital treatment for some poisonings and appreciate the real-world aspects of studying activated carbon. Another application of interest is in the treatment of effluents from textile industries, which may contain a high concentration of commercial dyes. Such waste may impact receiving water bodies aesthetically and, by reduction of light penetration, affecting biological processes. In addition, studies have shown that dyes can be toxic to aquatic life, and the expanded use of azo dyes has shown that some of them and their reaction products are highly carcinogenic.10 Guidelines and legislation for dye effluents are currently enforced by many countries

Although important in many fields and in industry, experimental design has only recently begun to be introduced in the undergraduate chemistry curriculum.1−6 This work describes a multiple-week, flexible chemistry experiment with environmental applications, which includes factorial experimental design and analysis of variance (ANOVA) statistical analysis. The experiment is easily adaptable for a range of student enrollments and uses only common laboratory glassware and a visible spectrometer. Additionally, students work as a team (or teams for a larger class) to make decisions about which factors to investigate, how to divide the work to accomplish necessary tasks, and the analysis of the data. Most students are familiar with the traditional method of studying a system in which a single factor is varied while all other variables are held constant. Such studies are inefficient and also neglect possible synergistic interactions between factors. By incorporating factorial design, students can efficiently study a group of factors and their interactions in a student-designed study of activated carbon adsorption of textile dyes. The purpose of this work is to evaluate collectively studentchosen experimental factors under different levels using Minitab18 software.7 This experiment is easily adaptable for any number of students by simply limiting or increasing the number of factors studied; data are easily generated using a Beer−Lambert plot of absorbance of the dyes using any visible spectrometer. Through this experiment, students gain an understanding of experimental design, factorial analysis, and surface chemistry in an important environmental context and gain experience in working as a team. © XXXX American Chemical Society and Division of Chemical Education, Inc.

Received: June 19, 2017 Revised: December 1, 2017

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DOI: 10.1021/acs.jchemed.7b00431 J. Chem. Educ. XXXX, XXX, XXX−XXX

Journal of Chemical Education

Laboratory Experiment

across the world.11 Factors that may influence the adsorption capacity of activated carbon can include pH, salinity, water hardness, type of carbon, time of contact, and type of dye. For some years, Sacred Heart University has had highly variable enrollments in physical chemistry lab (ranging from 3 to sometimes 12 students), and so, we were seeking an experiment that could be easily adaptable for a range of student enrollments without additional equipment needs and that would use an analysis method that would not require considerable time or be costly to generate data. Additionally, as part of the departmental assessment plan, an alumni survey indicated that many alumni who were working in industry positions felt that they would have benefited from more experience with experimental design and statistical analysis. Consequently, this experiment was developed to meet needs in both these areas: an adaptable experiment that also introduces students to factorial experiment design and analysis. Although we use this experiment as a semester capstone for our physical chemistry lab course, this versatile experiment could fit in many areas of a chemistry curriculum: as a lab for analytical chemistry, an integrated lab course, an environmental chemistry course, or even an advanced-level general chemistry lab.

analytical chemistry lab and so were experienced with use of volumetric glassware, visible spectroscopy, and the Beer− Lambert law. Because of this, only general directions were given to the students (these are included in the student instructions in the Supporting Information) and they were responsible for designing and implementing the work necessary. Earlier in the semester, students had completed an experiment with the adsorption of acetic acid onto activated carbon and so were familiar with the concepts of adsorption and the calculation of the quantity Y (mg adsorbate/gram activated carbon) from initial and equilibrium concentrations. In general, stock solutions of dye(s) in tap water were prepared and tested so that the absorbance level is at the upper level of the spectrometer used. Students measured the λ-max and prepared a Beer−Lambert plot of absorbance versus concentration at the λ-max. Students were then asked to decide on the number of experimental parameters they wanted to investigate with the understanding that the number of runs would be the product of Nx, where N is the number of levels and x is the number of factors. In this particular case, students opted to investigate carbon dosage, exposure time, and pH with a single dye (three levels of carbon doses: 0.5, 1.0, and 1.5 g), three levels of exposure time (1 h, 2 h, and 1 week), and two pHs (4 and 10), for a total of 32 × 21 experiments: two factors run at three levels and 1 factor run at 2 levels. Thus, a total of 36 runs since each run is done in duplicate. Additional factors that other student groups have studied include type of carbon, salinity, water hardness, and multiple dyes. Students developed a plan to create the number of stock solutions necessary to run the experiments, thus ensuring all students were working with a single batch of dye for their particular run. The pH was adjusted to the required pH level by the addition of small amounts of 1 M HCl or NaOH, which did not appreciably change the volume. Instant Ocean (synthetic sea salt) was used to adjust the salinity when this was being studied. Water hardness was adjusted by addition of MgCl2 and CaCl2 to create the desired level. Students enter the number of factors and the number of levels into Minitab software, and the program generates run specifications of the appropriate combination of pH, carbon dosage, and time. The creation of the experiment design was performed using Minitab, and a sample of the randomized run orders with the randomized levels for the experiments is presented in Table 1, also generated by Minitab. Thus, run 11 will have the pH level 2, carbon dosage 1, and time 3 (pH = 10, carbon dose of 0.5 g, and 1 week of contact time based on the student parameters in this experiment). Runs 13 and 14 show an example of duplicate runs in which the factors are the same. The statistical validity of the results is

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THEORY The basic principles of factorial design were introduced in the Journal of Chemical Education in 196812 in the context of an organic experiment, and on a more general level in 1990.13 More recently, with the advent of commercially available software, the technique has been applied to experiments in organic chemistry and instrumental analysis. In factorial design, more than one parameter is varied in each experiment, and the results are evaluated for statistical significance. In most of the previous examples of factorial design applied in undergraduate chemistry,1−6 the technique was used to optimize experimental or instrumental conditions. In this current application, experimental design and ANOVA are used to evaluate whether or not a parameter (or the interaction between two parameters) is statistically significant in affecting the adsorption of dye onto activated carbon. The null hypothesis assumes that a particular parameter will not affect the adsorption of dye onto activated carbon (the measured response factor being Y, the mass of dye adsorbed in mg per gram of carbon). The statistical output from Minitab lets students use statistics to decide whether the null hypothesis is valid. The actual experiment uses absorbance from a visible spectrometer to determine the concentration of dye before and after contact with the activated carbon. The dye concentration is determined by use of a previously determined straight-line-fit equation of a Beer−Lambert plot of absorbance versus concentration at the λ-max for the dye. The difference between the initial concentration and the concentration determined after contact with the activated carbon represents the amount of dye adsorbed onto the carbon. The response factor, Y (mg of dye adsorbed per gram of carbon), is then calculated and recorded as the response variable for Minitab analysis.

Table 1. Excerpt of Sample Output Codes of Run Order from Minitab

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EXPERIMENT The method below describes work for a group of eight students; it is easily adapted to a larger or smaller group by simply increasing or decreasing the number of factors studied. All students had previously completed a semester-long B

Standard Order

Run Order

pH

Dosage

Time

30 10 36 31 18 22 29 16

11 12 13 14 15 16 17 18

2 2 2 2 2 1 2 2

1 1 3 3 3 2 1 3

3 1 3 3 1 1 2 1




Figure 1. Output of residual plot from Minitab for student-generated data as described (36 runs).

dependent on randomization, and it is assumed that the runs are identical except for the particular factors being studied. The randomized run orders were followed as created by the software when performing all simulation runs. A 50 mL portion of the appropriate stock solution was removed and added to a 125 mL brown bottle that had been previously cleaned and dried. Each bottle contains the carbon dose in grams as specified by the software. A magnetic stir bar was inserted into each bottle, and all bottles were placed on a multimagnetic stir plate and allowed to spin at very low speed. The isotherm bottles with duplicates, a total of 36 experiments in this case, were all prepared similarly. With larger numbers of students and runs, not all experiments were run simultaneously because of limitations on the number of stir plates. The stock solutions’ initial absorption was previously measured. The bottles were run for the specified time on the stirrer and allowed to settle. All samples were centrifuged for 5 min prior to measurement to remove any suspended carbon. For each, an aliquot was removed and placed in a cuvette and the absorbance measured at the λ-max specific for the dye. The absorbance measurements were converted to concentrations in mg/mL using the Beer−Lambert best-fit equation for the dye that the students had previously generated. Because each bottle had 50 mL, this was easily converted to milligrams of dye, which was subtracted from the initial amount of dye to calculate total dye adsorbed onto carbon in milligrams. The response factor, milligrams of dye adsorbed/gram of carbon, was then calculated. As students found their results, they entered data for their response variable Y (mg of dye adsorbed/g of carbon) on a class template and used the results in the Minitab analysis. More information on the concentrations of dyes used, carbon dosages, pH levels, and how the work was assigned to students

is included in the instructor’s notes in the Supporting Information.

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HAZARDS Procion fiber-reactive dyes are considered relatively nontoxic, but are fine powders, and repeated inhalation of the dye powder can cause an allergic reaction in some people. Students work with these dyes in very small quantities and only to make stock solutions and wear protective gloves and eye protection while preparing. Waste materials are collected in containers following the guidelines of laboratory hazardous waste management.

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RESULTS One of the outputs from Minitab which is shown in Figure 1 provides a four-in-one residual plot for the response variable. These plots provide a visual approach to identifying nonnormality in the residuals. The residual is the difference between an observed value and the corresponding fitted value, which is the value predicted by the model equation developed by the Minitab software. In general, the residuals should be randomly distributed, with no obvious patterns and no major unusual values. In this case, the normal probability plot (the upper left plot of Figure 1) of the residuals verifies the assumption that the residuals are normally distributed by approximately following a straight line. Minitab notes unusual data points, and students can easily identify the normality of the data by noting any unusual patterns in the residual plots. As can be seen in the histogram plot of Figure 1, the residuals are randomly distributed around zero, and the versus order plot shows that the residuals are randomly distributed around zero with the randomized observation order. Analysis of variance (ANOVA) was used to evaluate the experimental results. Each factor which was varied in the C




using the factorial design of experiment over simply varying a single parameter at a time.

experiment is decomposed into the total sum of squares which allows real effects to be distinguished from random error.14 An analysis of a sample of student data is given in Table 2. The

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CONCLUSION The study adsorption of dye onto activated carbon provides an ideal system in which to introduce the concept of experimental design and ANOVA analysis to students in a setting which allows them to work as a team in investigating the significance of various parameters affecting the adsorption. The experiment is easily scalable for large or small classes and lets students plan and design the experiment using skills they have learned in previous courses.

Table 2. Analysis of Variance Results from Minitab Factor pH Dosage Time pH × Dosage pH × Time Dosage × Time Pure Error Total

Degrees of Freedom

Adjusted SS

Adjusted MS

FValuea

p-Valuea,b

1 2 2 2

125.0 1368.9 5657.6 1092.4

124.97 648.46 2828.80 546.21

2.49 13.65 56.41 10.89

0.129 0.000 0.000 0.001

2 4

620.9 2066.5

310.45 516.62

6.19 10.30

0.007 0.000

18 35

608.30 12,520.1

33.80 d

c c

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ASSOCIATED CONTENT

* Supporting Information S

c c

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.7b00431. Student handouts, with sample data and results and more detailed discussion of the statistics (PDF, DOCX) Instructor notes (PDF, DOCX)

N = 36. bα = 0.05. cF- and p-values are provided for each term (and the two way interactions) in the model. dAdjusted MS values are calculated by subtracting from the total sum of squares so no adjusted MS is given for the total. a

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actual experimental data is given as a table as provided in student instructions in the Supporting Information. In this example, there are 3 sources of variability: pH, time of contact, and carbon dosage each at three different levels. Synergistic interactions can occur between any combination of the factors. The null hypothesis is that there is statistically no difference between experiments. The Minitab ANOVA analysis outputs a set of F-values and p-values which allows students to evaluate whether each factor, including two factor interactions, is nonsignificant. The ANOVA analysis presented in Table 2 includes the total sum of squares (SS) which gives the variance in the entire sample. Subtracting all of the sums of the squares of the individual factors (pH, dosage, and time) from the total sum of squares gives the residual mean square error (MS). Each SS is calculated as a comparison between two sets of results (averages for high and low level) for two-level factors, in this case pH.14 For the three-level factors (carbon dosage and time), there are two degrees of freedom (df). The F-value is compared with the critical values in statistical tables. For use of the pvalue, the null hypothesis is assumed to be true, and the p-value sets the smallest value for which the null hypothesis must be rejected. If α is 0.05, then for the example given (a 95% confidence limit), all factors and all two factor combinations give p-values, which are sufficient for rejection of the null hypothesis in this particular example. A more detailed discussion of the statistics including sample calculations is included in the student instructions in the Supporting Information. In this particular study, the evaluation of threeway interactions was not included since it would have increased the number of experiments. Student lab reports were analyzed and evaluated in order to assess whether the student learning outcomes were achieved. All students were able to work as a team, successfully design and carry out the experiments, and record results, and many mentioned in the course evaluation that they appreciated the opportunity to work in a team and to choose how to design and divide the work. All students correctly stated the null hypothesis in application to their work, and all students were correctly able to use F and P statistic values to state whether factors or a combination of factors was statistically significant. Of the students, 80% made some mention of the advantage of

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Penny Snetsinger: 0000-0001-7226-437X Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Student work and results are from Sacred Heart University’s physical chemistry lab courses from 2014−2016. Additional work was done by Ahmad Alanazi and Sarah Aanonsen. A high school version of this was done by Keri Tenerowicz, and her spectrum was used in the graphical abstract.

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REFERENCES

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(10) Beoniger, M. F. Carcinogenity of Azo Dyes Derived from Benzidine; Department of Health and Human Services: Cincinnati, OH, 1980; NIOSH Pub. No. 8-19. (11) Hessel, C.; Allegre, C.; Maisseu, M.; Charbit, F.; Moulin, P. Guidelines and Legislation for Dye House Effluents. J. Environ. Manage. 2007, 83, 171−180. (12) Smith, R. B.; Billingham, E. J. Factorial Design in Undergraduate Organic Experiments. J. Chem. Educ. 1968, 45, 113−115. (13) Strange, R. S. Introduction to Experiment Design for Chemists. J. Chem. Educ. 1990, 67, 113−115. (14) Alkhatib, E. A.; Rapaglia, J.; Theim, L. A PES Study of Factors Influencing Metal Partitioning in Aquatic Systems: “Design of Experiment As, Cd, Co, Cr, Cu, Pb, Ni, and Zn”. Am. Int. J. Contemp. Res. 2016, 6 (6), 9−18; http://www.aijcrnet.com/journals/Vol_6_ No_6_December_2016/2.pdf (accessed Oct 2017).

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Flexible Experiment Introducing Factorial Experimental Design

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