Activity Cite This: J. Chem. Educ. XXXX, XXX, XXX−XXX
pubs.acs.org/jchemeduc
Chemical Analysis of Household Oxygen-Based Powdered Bleach: An Engaging Approach to Teaching Sampling of Heterogeneous Materials and Addressing Statistics Mauro S. F. Santos, Alexandre L. B. Baccaro, Guilherme L. Batista, Fernando S. Lopes, and Ivano G. R. Gutz* Departamento de Química FundamentalInstituto de Química, Universidade de São Paulo, Av. Prof. Lineu Prestes, 748, 05508-000 São Paulo SP, Brazil S Supporting Information *
ABSTRACT: Crucial steps of a chemical analysis, such as sampling, determination, and statistical analysis of data, are addressed in a well-tested and stimulating laboratory activity that convincingly unveils the stratification of ingredients in a heterogeneous household product for laundry: a powdered oxygen bleach. Three different samples of bleach were taken from a single jar: one from the top, one from the bottom, and a more representative mixture collected by driving a core sampler (a graduated cylinder was found to be appropriate) throughout the full depth of the jar content. The resulting frequency distribution curves (histograms) of the H2O2 titrations with MnO4− presented widely separated maxima for the three samples, while no statistically significant difference was found for the titration of CO32− with HCl. These results intrigue the students, leading them to reflect until they understand that this is due to the presence of a single compound acting as a source of H2O2 (sodium percarbonate), while two ingredients in the formulation of the stratified bleach powder provide CO32− after dissolution. Large and light percarbonate grains are more frequent in the upper layer of the bleach jar, while smaller and denser Na2CO3 particles are richer at the bottom. KEYWORDS: High School/Introductory Chemistry, First-Year Undergraduate/General, Second-Year Undergraduate, Analytical Chemistry, Calculator-Based Learning, Hands-On Learning/Manipulatives, Acids/Bases, Oxidation/Reduction, Titration/Volumetric Analysis, Quantitative Analysis
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after stirring or pouring.8 Experiments with real samples suitable for application in lab classes are, however, rarely presented. The segregated oxygen bleach powder in its original package is perfectly suited for this purpose, as will be shown. Furthermore, the importance and the proper use of statistics for treating and evaluating data are more prone to be appreciated by students while handling their own experimental data. So, for this activity, an affordable powdered household product (oxygen-based bleach) was chosen as a real sample to demonstrate important aspects of the sampling of solid matrices, and to illustrate the relevance of the statistical treatment of data. Many oxygen bleach powders are mostly composed of a mixture of sodium carbonate (Na2CO3) and sodium percarbonate (2Na2CO3·3H2O2). Such bleaches have been valued before as a material for experiments in lab classes for high school and college freshmen students.10−13 For instance, in analytical chemistry classes, oxygen-based bleaches have been analyzed by thermogravimetry, by measuring the variation of
INTRODUCTION In analytical chemistry only a small fraction of a material is analyzed: the sample. Sampling is a fundamental step in chemical analysis and a determining factor of the representativeness of the result, especially for the analysis of nonhomogeneous target materials. Even though it is a subject treated in most analytical chemistry textbooks,1 and occasionally in this Journal,2−8 it remains untaught by many universities and colleges,9 and is seldom practiced in chemistry undergraduate or technical courses. This is likely because there are few published activities well-suited for realistic, practical work during laboratory courses. Examples of current lab experiments comprise the accounting of different colored candy mixtures6 and the chemical analysis of lawn fertilizer.7 However, problems associated with the sampling of heterogeneous materials, like those imparted by segregation of particles by size and density as herein proposed, are rarely addressed. In one conceptually rich publication, the “realistic” sample consists of a mixture of nickel sulfate (crystals) and silicon carbide (boiling chips). The students are asked to perform a time-consuming gravimetric determination of nickel dimethylglyoximate (in triplicate).5 In another example, colored plastic beads are mixed for visual observation of the segregation © XXXX American Chemical Society and Division of Chemical Education, Inc.
Received: May 29, 2017 Revised: November 6, 2017
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DOI: 10.1021/acs.jchemed.7b00364 J. Chem. Educ. XXXX, XXX, XXX−XXX
Journal of Chemical Education
Activity
Additional detailed procedures for the preparation of samples and solutions, the balanced chemical reactions, the student’s data, and the statistical analysis provided in a ready-for-use spreadsheet (Microsoft Office Excel) are available in the Supporting Information.
weighed mass of the powder during its thermal decomposition,10 and by gas volumetry of evolved O2 produced by the reaction of H2O2 in aqueous solution with excess hypochlorite.11 The latter technique also allows the determination of carbonate from the volume of CO2 formed by its reaction with acetic acid. Under the lens of a microscope, the same two reactions allow students to conclude that particles in the bleach powder, differing merely in morphology, show different reactivity and composition.11 Herein, the content of an oxygen-based bleach jar is sampled in three vertically defined zones: upper zone, lower zone, and a core from top to bottom. Samples from each zone are observed with a digital microscope, and the contents of hydrogen peroxide and carbonate ion after dissolution are determined by the students through titration with potassium permanganate14 and hydrochloric acid, respectively. Afterward, all results are compiled and statistically analyzed with the help of a ready-foruse spreadsheet that also generates boxplots (available in the Supporting Information). Gaussian frequency distribution curves may be fitted to the sets of data for each analyte and sampling zone. Application of the paired samples t-test reveals that there is a statistically significant difference for just one analyte, hydrogen peroxide, motivating the students to seek understanding.
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HAZARDS The diluted solutions of hydrochloric acid, sulfuric acid, potassium permanganate, and samples of oxygen bleach should be handled with care. In addition to the lecturer’s assistance, gloves, safety goggles, and an appropriate apron should be worn when performing these experiments.
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RESULTS AND DISCUSSION During the determination of hydrogen peroxide, almost every student could observe a decrease of the results of the calculated mass fraction following the order TOP > MIX > BOT. When all the gathered data (mean of 20 determinations of subsamples per fraction, available on Supporting Information) was organized in a histogram (Figure 1A) and analyzed statistically, the results seemed normally distributed around its mean and a Gaussian curve could be fitted to the histogram plots. An analysis of variance test (ANOVA, available in the Data Analysis supplement of Excel) of the three data sets reveals that not all the means compared in the test are equal. The
EXPERIMENTAL SECTION
Sample Preparation and Titration Procedure
A 450 g screw-capped plastic jar, of powdered household oxygen-based bleach (Vanish Oxi Action), was mechanically treated by gently hitting the jar’s bottom on a table for a minute or so. As an alternative to Vanish Oxi Action (available in U.K. and South America), OxiClean Versatile Stain Remover (commercialized in the United States) can be used. That enhances the already existing partial segregation of particles, with different sizes and densities in the mixture, induced by the shaking during regular shipment of the product. Then, three fractions of approximately 100 g were taken from the inhomogeneous powder: a core sample, obtained by pressing a graduated cylinder throughout the full depth of the jar content (MIX), an upper layer fraction (TOP), and a bottom layer (BOT) (more details in Support Information). Each sample was observed using a microscope coupled with a digital camera (Coleman NSZ-405), before and after being ground in a mortar until an apparent homogeneity was achieved (random heterogeneity). The titration experiments reported next were performed by a class of 38 high school students. The titrants were standardized in advance. Subsamples of the homogenized MIX, TOP, and BOT samples were provided to each of them. There were 18 students who performed the titrations of weighed masses of their three subsamples using standardized 0.1 mol L−1 hydrochloric acid, with phenolphthalein as indicator, with the aim to determine the total carbonate content. The remaining 20 students have determined the H2O2 content by dissolving the weighed mass of each subsample in 0.5 mol L−1 H2SO4 medium (great excess of H+ required) and using a 5.5 × 10−3 mol L−1 potassium permanganate solution as titrant (no need of an indicator). Continuing the activity, each student reported the weighed mass, the volume of titrant consumed, and the calculated mass fraction of either CO32− ion or H2O2 found in the subsamples MIX, TOP, and BOT of the bleach. The data was organized in a spreadsheet for statistical analysis and discussion.
Figure 1. Histograms with normalized Gaussian distributions fitted to the data for (A) percarbonate concentration (from hydrogen peroxide determination) (% w/w for the BOT, MIX, and TOP fractions) and (B) total carbonate concentration (from carbonate determination) (% w/w for the blue-BOT, green-MIX, and red-TOP fractions). The two outliers were rejected before the Gaussian curve fit to the (B) TOP fraction. B
DOI: 10.1021/acs.jchemed.7b00364 J. Chem. Educ. XXXX, XXX, XXX−XXX
Journal of Chemical Education
Activity
useful when the data does not follow a known distribution or presents distortions like skewness or kurtosis. The boxplot helps in the recognition of outliers among the data, which may be difficult to notice on the basis of only mean and standard deviation values. If they are not rejected, these values may significantly displace the summary statistics of the sample, as it might be seen for the TOP fraction of total carbonate analysis (Figure 2B). Considering the whole data, the mean mass fraction (and standard deviation) is found to be 63 (±10)% (w/w). However, while excluding outliers, the value is 67 (±3)%, which is interestingly closer to the other sample means of BOT, 68 (±3)%, and MIX, 70 (±3)%. At this point in the activity, more advanced classes can compare data rejection methods like the q-test and the Chauvenet criteria.16,17 The explanation for this apparently contradictory difference in the H2O2 and CO32− determinations can be easily found in the images recorded from the three different fractions of the sample shown in Figure 3, consonant with a detailed microchemical investigation of the particles.11 Visually, the MIX fraction (after being well-mixed) as well as a portion taken from the half-height of the jar (Figure 3A) seem to better represent the whole powder, since they show all kinds of particles. On the other hand, the TOP part (Figure 3B) exhibits mostly larger, approximately spherical particles which correspond to sodium percarbonate,11 while the BOT fraction (Figure 3C) contains more irregular particles composed of finely divided granules. The microscopic examination of the subsamples is done preferably by the students themselves, but if there is not enough time or equipment available, micrographs previously obtained by the instructor or lab assistant can be projected during the discussion after the lab class and forwarded to the students. There is a clear correlation between the shape, dimension, and composition of particles.11 The segregation induced by the discrimination of size and density leads to the lack of homogeneity of the samples taken, most noticeable for particles collected near the top or the bottom of the jar. In fact, there are two kinds of particles with different size and shape providing carbonate ions after their dissolution: the sodium percarbonate itself,18 present as round shaped particles with a bulk specific gravity of 0.9−1.2 g cm−3, enriched in the upper region of the jar, and the finer sodium carbonate particles (specific gravity of 2.5 g cm−3), more abundant at the bottom, lending to the apparent uniform availability (within the uncertainties of determinations) along the entire depth of the jar. Back to the statistics and results spreadsheet, it is evident that the sodium percarbonate concentration in the bleach can be directly calculated from the mean of the MIX results for H2O2, taking into account the 2Na2CO3·3H2O2 stoichiometry. The sodium carbonate concentration in the powder bleach is obtained after subtracting the percarbonate contribution to the total CO32− titrated in the MIX sample.
comparison of the means and standard deviations of the data pairs (TOP-MIX, TOP-BOT, and MIX-BOT) by a t-test showed that the H2O2 contents in the TOP, MIX, and BOT fractions are different (CI ≥ 95%). The results showed an increase of about 40% of percarbonate mass fraction (w/w) from the bottom to the top. At this point in the activity, the relative standard deviations from the means can be used to discuss the extent of random errors in the analytical procedures. On the other hand, the results for total carbonate content obtained by each student were quite similar for the fractions (MIX, TOP, and BOT). When all these 18 sets of results were analyzed, the histogram (Figure 1B) presented much overlapped curves for the fractions, although not all the means are equal on the basis of an ANOVA test. Discrepant measurements for the TOP sample, observable in Figure 1B, interfere with the ANOVA test as well as with the paired t-test, both based on the assumption of normal data distribution. Another way to describe the data is with the box-and-whisker plot (simply boxplot, Figure 2),15 frequently used in nonparametric statistics, which represents both the summary statistics and the distribution of data. This tool is especially
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CONCLUSION The activity highlights the importance of the sampling step during the chemical analysis of a product presenting vertical stratification. Statistical tools were introduced to the students as helpful allies to characterize the phenomenon, validating or refuting the similarity between the results, taking into account the dispersion of the data. While the percarbonate concentrations found for samples from different depths were statistically different, those for the total sodium carbonate were not, and a coherent explanation for this apparent
Figure 2. Boxplot of the analysis’ results for (A) sodium percarbonate and (B) total sodium carbonate content (% w/w) in each fraction. (C) Boxplot explanation. IQR, the interquartile range, is a measure of statistical dispersion extending from the 25th to the 75th percentiles. The range bars of the dashed lines in the boxplots A and B indicate the maximum and minimum real data points falling within the limiting range bars (Q1 − 1.5IQR and Q3 + 1.5IQR) depicted in the explanation (C). C
DOI: 10.1021/acs.jchemed.7b00364 J. Chem. Educ. XXXX, XXX, XXX−XXX
Journal of Chemical Education
Activity
Figure 3. Microscope images of the fractions of the commercial oxygen-based bleach obtained from the (A) half-height, (B) top, and (C) bottom of the jar.
students from a public technical school of chemistry established in São Paulo, SP, Brazil.
contradiction was sought and found by the students. The activity demonstrates the utility of statistical tests by application to real data, complementing the theoretical lessons on statistics. The activity also allows the reflection upon a factual problem: the particle segregation during the shipment can cause a clear difference of performance of the product during different phases of its consumption, with higher bleaching activity on the first applications regardless of the expiration date of the product. It is worthwhile to mention to the students that the lessons on sampling and statistics in analytical chemistry extrapolate domestic and industrial products, once more elaborate sampling strategies, including core profiling, are applied, e.g., in soil analysis for agriculture, landfill monitoring, biological materials analysis, geological prospection, or climate change studies involving analysis of lake sediments or drilled polar ice cores (providing samples of up to 800,000 years), not to mention extraterrestrial sampling.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.7b00364. Chemical reactions involved in the activity; procedures to prepare the sample and the titrants to perform the titrations and the calculations; instructions for the interpretation of the boxplots; and laboratory script for the application of the acidimetric and permanganometric titrations in the laboratory (PDF, DOC) Spreadsheet, filled out with real data for the generation of histograms, normal distribution fits and boxplots, and provision for (reversible) rejection of data (XLSX)
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REFERENCES
(1) Harvey, D. T. Chapter 7: Collecting and Preparing Samples. In Analytical Chemistry 2.1: An Open-Access Digital Resource for Undergraduate Education in Analytical Chemistry, Online; 2016; pp 271−336. http://dpuadweb.depauw.edu/harvey_web/eTextProject/AC2.1Files/ Chapter7.pdf (accessed Oct 2017). (2) Guy, R. D.; Ramaley, L.; Wentzell, P. D. An Experiment in the Sampling of Solids for Chemical Analysis. J. Chem. Educ. 1998, 75 (8), 1028. (3) Ross, M. R. A Classroom Exercise in Sampling Technique. J. Chem. Educ. 2000, 77 (8), 1015. (4) Harvey, D. Two Experiments Illustrating the Importance of Sampling in a Quantitative Chemical Analysis. J. Chem. Educ. 2002, 79 (3), 360. (5) Logue, B. A.; Youso, S. L. A Laboratory Exercise to Demonstrate the Theory and Practice of Analytical Sampling. J. Chem. Educ. 2010, 87 (3), 316−319. (6) Canaes, L. S.; Brancalion, M. L.; Rossi, A. V.; Rath, S. Using Candy Samples to Learn About Sampling Techniques and Statistical Data Evaluation. J. Chem. Educ. 2008, 85 (8), 1083. (7) Jeannot, M. A. Analysis of Iron in Lawn Fertilizer: A Sampling Study. J. Chem. Educ. 2006, 83 (2), 243. (8) Fritz, M. D. A Demonstration of Sample Segregation. J. Chem. Educ. 2005, 82 (2), 255. (9) Griffiths, P. R. Whither “quant”? An examination of the curriculum and testing methods for quantitative analysis courses taught in universities and colleges in the Western USA. Anal. Bioanal. Chem. 2008, 391 (3), 875−880. (10) Bracken, J. D.; Tietz, D. Analysis of OxiClean: An Interesting Comparison of Percarbonate Stain Removers. J. Chem. Educ. 2005, 82 (5), 762. (11) Lopes, F. S.; Baccaro, A. L. B.; Santos, M. S. F.; Gutz, I. G. R. Oxygen Bleach Under the Microscope: Microchemical Investigation and Gas-Volumetric Analysis of a Powdered Household Product. J. Chem. Educ. 2016, 93 (1), 158−161. (12) Nakano, M.; Ogasawara, H.; Wada, T.; Koga, N. Reactivity of Household Oxygen Bleaches: A Stepwise Laboratory Exercise in High School Chemistry Course. J. Chem. Educ. 2016, 93 (8), 1415−1421. (13) Wada, T.; Koga, N. Chemical Composition of Sodium Percarbonate: An Inquiry-Based Laboratory Exercise. J. Chem. Educ. 2013, 90 (8), 1048−1052. (14) Copper, C. L.; Koubek, E. Analysis of an Oxygen Bleach: A Redox Titration Lab. J. Chem. Educ. 2001, 78 (5), 652. (15) Spitzer, M.; Wildenhain, J.; Rappsilber, J.; Tyers, M. BoxPlotR: A Web Tool for Generation of Box Plots. Nat. Methods 2014, 11 (2), 121−122. (16) Wikipedia entry for Chauvenet’s Criterion. https://en.wikipedia. org/wiki/Chauvenet%27s_criterion (accessed Oct 2017). (17) Blaedel, W. J.; Meloche, V. W.; Ramsay, J. A. A comparison of criteria for the rejection of measurements. J. Chem. Educ. 1951, 28 (12), 643. (18) ScienceLab.com. Material Safety Data Sheet for Sodium Percarbonate. http://www.sciencelab.com/msds.php?msdsId= 9927598 (accessed Oct 2017).
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. ORCID
Mauro S. F. Santos: 0000-0002-9035-9839 Ivano G. R. Gutz: 0000-0001-9759-7920 Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors are grateful to CNPq (Conselho Nacional do ́ Desenvolvimento Cientifico e Tecnológico, Brazil) through Grants 311324/2014-2, 483944-2012-2, 161293/2012-3, and 156097/2010-9. The authors would like to thank to Stephanie Scribner for the revision of the English text. The data reported herein and made available in the Supporting Information corresponds to the execution of the activity by a class of 38 D
DOI: 10.1021/acs.jchemed.7b00364 J. Chem. Educ. XXXX, XXX, XXX−XXX
