Choosing the Greenest Synthesis: A Multivariate Metric Green

Article pubs.acs.org/jchemeduc

Choosing the Greenest Synthesis: A Multivariate Metric Green Chemistry Exercise Sean M. Mercer,† John Andraos,‡ and Philip G. Jessop*,† †

Department of Chemistry, Queen’s University, Kingston, Ontario, Canada K7L 3N6 CareerChem, 504-1129 Don Mills Road, Toronto, Ontario, Canada M3B 2W4

‡

S Supporting Information *

ABSTRACT: The ability to correctly identify the greenest of several syntheses is a particularly useful asset for young chemists in the growing green economy. The famous univariate metrics atom economy and environmental factor provide insufficient information to allow for a proper selection of a green process. Multivariate metrics, such as those used in life-cycle assessment (LCA) to determine environmental impact, are much more informative. A team exercise was developed, based upon nine LCA environmental impact metrics, where students are tasked with selecting the greenest synthesis from a set of literature procedures. Students select the greenest synthesis by quantifying the environmental impact of all the materials involved in each synthesis rather than solely the quantity of generic waste produced, as occurs with univariate metrics.

KEYWORDS: Upper-Division Undergraduate, Analytical Chemistry, Curriculum, Environmental Chemistry, Inquiry-Based/Discovery Learning, Problem Solving/Decision Making, Green Chemistry, Industrial Chemistry, Student-Centered Learning, Synthesis

T

he implementation of green chemistry and engineering material into more undergraduate and graduate curricula will enhance the critical thinking skills and the environmental awareness of future chemists and engineers. As the world’s population becomes increasingly concerned about our environmental impact, the practice of developing “greener” solutions will become more prevalent and those who have been trained in green chemistry and engineering will be better equipped to address these issues. Because these fields are becoming popular topics of study, the need to provide innovative teaching materials has become paramount. A particular need is to develop exercises that allow students to think critically and give them the skills to choose the greenest of two or more options. Given that many chemistry and chemical engineering students of today will be decision-makers tomorrow, teaching them how to assess environmental impact and how to compare the impact of two or more choices will give them the ability to make environmentally wise decisions in the future. Some methods for quantifying environmental impact are better than others. During the instruction of how to assess the “greenness” of a chemical, reaction, or process, univariate metrics such as atom economy (AE, the mass % of atoms from reactants that appear in the desired product of a balanced reaction equation)1 and environmental (E) factor (the ratio of mass of waste over mass of product)2 are often taught. Unfortunately, these numbers do not provide enough information to make an informed assessment of the “greenness” of a chemical, reaction, or process (Figure 1). Several proposed student exercises have used such univariate metrics3−5 or, in a © 2011 American Chemical Society and Division of Chemical Education, Inc.

Figure 1. Students, when shown this figure and asked, “Which route is the greenest?”, correctly answer that there is insufficient information to make such a decision. Although route B makes less waste, that waste could be much more hazardous than the waste from the other two routes. Similar problems also make atom economy inadequate for decision-making.

somewhat better approach, combinations of these with subjective impact metrics.6 However, a scientifically informed assessment is only possible with the use of quantitative multivariate metrics that provide a more detailed and objective data set for assessment. A recent study, which compares and contrasts univariate metrics to multivariate metrics for the selection of a green polymer, illustrates this point.7 An activity has been developed that can be used as a team assignment for assessing the greenness of a series of similar Published: December 5, 2011 215

dx.doi.org/10.1021/ed200249v | J. Chem. Educ. 2012, 89, 215−220

Journal of Chemical Education

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to save time can give the students a spreadsheet already programmed to do the MCM. If environmental chemistry is a component in the course or in a prerequisite course, the students may already understand MCM calculations. An explanation is given in the Supporting Information.

reactions or processes. The activity is appropriate as the major assignment for a third- or fourth-year undergraduate course on green or analytical chemistry. With some modification, it can be used for a graduate level course. For this exercise, nine environmental impact metrics were chosen based upon the ISO life-cycle assessment (LCA) methods, as described by Guinée and co-workers8 and Allan and Shonnard.9 A more informative, comprehensive method of comparing and contrasting chemical process than the traditional univariate metrics exercises is provided, while still affording an exercise that can be carried out in a reasonable time frame of several hours. This particular exercise has been carried out successfully for the past three years in green chemistry courses. The exercise requires that students imagine themselves to be process chemists who have been told by their employer to determine the greenest route to make 1 kg of a desired chemical product. The students are grouped into teams of four or five. Each team is assigned a transformation (e.g., benzene to aniline) and told to analyze four or five instructor-selected methods from the literature for achieving that chemical transformation. It is helpful if at least one team member is experienced in the use of spreadsheet software. The team evaluates the methods using the nine metrics and then choose which process is the greenest. The exercise is carried out outside of class over a 6 week period and then the students report their results to the entire class. During the exercise, the students need to cooperate, search the literature, calculate the metrics, identify the weaknesses of each route, suggest improvements for each route, and finally, using the data, identify the greenest route.

Acidification Potential (AP)

The potential for a gaseous compound to generate acid rain is relative to an equal mass of SO2.10 If a gaseous chemical is a strong Brønsted acid or is capable of being transformed into a strong Brønsted acid by oxidation and hydration in the atmosphere, then the AP is calculated as shown in eq 2,

AP = (α /MW)/(αSO2 /MWSO2)

where MW is the molecular weight of the original gaseous compound and α is the number of dissociable protons in the strong acid that it forms. For example, SO2 has a MW of 64.1 and an α of 2 (because it forms H2SO4). The acidification index, IAP, is the AP of the compound multiplied by the emitted mass of the compound:

IAP = APm

(3)

Literature AP values of many common gaseous compounds can be found in books by Guinée and co-workers.8,10 Ozone Depletion Potential (ODP)

The ODP measures the ability of gaseous or very volatile halogen-containing compounds to deplete ozone relative to CCl3F (CFC-11). ODP values can be found in several literature sources.8,9,11−14 The ozone depletion index, IOD, for the compound is calculated by equation 5, where m is the mass emitted.

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THE METRICS The nine metrics that are used in this exercise are acidification potential (AP), ozone depletion potential (ODP), smog formation potential (SFP), global warming potential (GWP), human toxicity by ingestion (INGTP), human toxicity by inhalation (INHTP), persistence (PER), bioaccumulation (ACCU), and abiotic resource depletion potential (ADP). The students are also asked to calculate the atom economy and E factor of each route, so that they can discuss whether these two univariate metrics would have led them to the same choice of greenest route. The ISO LCA methods also include other factors such as carcinogenicity, eutrophication potential, and ecotoxicity, but these are omitted from the exercise because of time constraints and the lack of accessible data. The ISO LCA methods take into account the quantity of material waste in addition to the inherent harmfulness of the waste. For every chemical involved, a risk potential, P, is calculated relative to an equal mass of a reference compound. The risk potential is therefore unitless and nonarbitrary. The risk potential can be multiplied by the mass of chemical released into the environment, m, to give the risk index, I:

I = Pm

(2)

IOD = ODPm

(4)

Smog Formation Potential (SFP)

This metric (eqs 5 and 6) measures the ability of a volatile organic chemical to contribute to the formation of ground-level ozone (the compound’s “maximum incremental reactivity” or MIR) relative to the MIR for a standard mixture of reactive organic gases (ROG). The MIR for the standard ROG mix is 3.10. The MIR values for other compounds have been measured by Carter15 and are also available in several reviews.9,10

SFP = MIR/MIR ROG

(5)

ISF = SFPm

(6)

Global Warming Potential (GWP)

This potential is the ratio of irradiative forcing of a gaseous or volatile chemical over time compared to an equal mass of CO2. The potentials for many chemicals have been previously calculated.8,16 However, in the case of volatile organic chemicals for which there is no published GWP, the students may use an indirect calculation that assumes immediate oxidation to CO2 and therefore considers the molecular weight and the number of carbons (NC) in a molecule of the compound (eq 7).9 In either case, the global warming index is calculated with eq 8.

(1)

In an industrial setting, process engineers would have access to actual or predicted emission quantities for each chemical in a process; the accuracy of such data strongly affects the accuracy of the LCA conclusions. In a classroom, the students are given a set of assumed emission rates (see the Supporting Information). For several of the metrics, a calculation to find the final concentrations of an emitted substance in air, water, soil, and sediment is necessary. To approximate these concentrations, a multimedia compartmental model (MCM, also known as a fugacity model) is used.9 Although the students learn more if they do the MCM calculation themselves, instructors who wish

GWP = (NC/MW)/(NCCO2 /MWCO2)

(7)

IGW = GWPm

(8)

In addition, the student should calculate the quantity of CO2 created by energy use during the chemical process. To keep the assignment from being too long, the students are only asked to calculate the energy consumption for heating bulk solids, 216



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Abiotic Depletion Potential (ADP)

heating, refluxing, or distilling liquids (see the Supporting Information), and for irradiation. The quantity of CO2 generated is calculated from the energy consumption by assuming that the generation of 1 kJ of energy generates 0.042 g of CO2 (the suggested CO2 emission limit for new European coal-burning power plants).17

The ADP measures the relative risk of depletion of each element relative to antimony. The potential values are available from the literature8 for each element that has an appreciable potential for depletion. Students should ignore the abundant elements, meaning those with an ADP below 1 × 10−6 (e.g., C, H, O, N, Al, Ca, Cl, Fe, K, Na, Mg, Si). Only materials going into the process (solvents, reagents, catalysts, drying agents, column packing, etc.) cause resource depletion, so intermediates, products, and byproducts are not evaluated for ADP. When calculating the index of abiotic depletion, IAD, students must multiply the ADP by the total mass m of a particular element used (not the mass emitted) in the chemical process:

Human Toxicity by Ingestion Potential (INGTP)

The INGTP is the ratio of the concentration of the compound in water (Cw, as calculated using the MCM) divided by the LD50 value (rat, oral, mg kg−1), relative to the same formula for toluene (eqs 9 and 10).8,9 The students must therefore do the MCM calculation for both the compound itself and for an equal mass of toluene. Although the literature recommends the use of RfD (reference dose) values where possible instead of LD50 values, there are so few RfD values available that it is more expedient to tell the students to only use LD50. Material safety data sheets, found using a Web search engine, are the easiest source of LD50 data.

INGTP = (C w /LD50)/(C w,tol /LD50,tol ) IINGT = INGTPm

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(10)

Human Toxicity by Inhalation Potential (INHTP)

Again, utilizing the multicompartmental model provided, the airborne concentration of the emitted chemical, Ca, is divided by the LC50 (rat, g/m3 4 h) value and is divided by the same formula for an equal mass of toluene (eq 11 and 12).9 Students can save time by assuming INHTP is zero for compounds that have negligible volatility. (11)

IINHT = INHTPm

(12)

Persistence (PER)

The persistence of an organic chemical in the environment can be modeled using the Boethling Index, 18 an aerobic biodegradation model that approximates the lifetime of a chemical based upon the type and number of functional groups, f n and an, respectively, on the molecule. The value obtained from eq 13, using the f n data in ref 18, gives a rough guide to the lifetime of the compound in soil, where 5 = hours, 4 = days, 3 = weeks, 2 = months, and 1 = very long-lived. The lifetime is not multiplied by mass. In advanced courses, the students can also be taught how to calculate the lifetime of volatile or gaseous organic compounds in the atmosphere using the method of Kwok and Atkinson.19

C6H6 + Me3SiN3 + 2F3CSO3H + NaOH → C6H5NH2 + N2 + Me3SiOSO2 CF3 + NaF3CSO3 + H2O

∑ (an + fn )

(15)

Benzene (75 mL, 0.842 mol) and triflic acid (20 mL, 0.22 mol) are warmed to 55 °C. Trimethylsilyl azide (0.037 mol, 4.4 g) in 20 mL benzene (0.224 mol) is added. The mixture is stirred for 50 min until no more N2 is given off. The mixture is then cooled to room temperature and poured over ice. The organics are extracted with three washings of dichloromethane. The aqueous layer is basified to pH ∼13 and any additional product is extracted with three washings of dichloromethane. The organic fractions are combined and dried with MgSO4. The solvent is evaporated off to give aniline in 95% yield and 100% selectivity. (modified from Olah and Ernst20). After the students balance the reaction equation (eq 15), they list the materials used or produced in the synthesis in a table of materials (Table 1). The product is not included because all of the routes are assumed to emit the same quantity of product. Water, SiO2, N2, O2, and H2 are also not included

Boethling Index=3.199 − 0.00221 MW +

(14)

PROCEDURE Groups of four or five students are assigned a particular chemical transformation. For an undergraduate course, they are given four or five preselected literature routes for that conversion. For a graduate course, the students should find the routes themselves. Members of the group are responsible for analyzing one route each, including balancing the overall reaction equation, calculating the yield and selectivity, identifying all of the materials used or produced, and calculating the potentials and indices. Students should also calculate the atom economy and E factor. The group should then prepare a report and presentation to class detailing their results and their conclusion as to which route was the greenest. As an example, five preparations were chosen for the conversion of benzene to aniline: two from academic literature (routes 1 and 2),20,21 one from a patent (route 3),22 one from a common organic laboratory textbook (route 4),23 and finally, one from an industrial preparation.24 The Supporting Information contains descriptions, E factors, atom economies, and tables of materials for all five routes. The balanced reaction equation and the experimental description of route 1 are presented below. Information about the other four routes is shown in the Supporting Information.

(9)

INHTP = (Ca /LC50)/(Ca,tol /LC50,tol )

IAD = ADPm

(13)

Bioaccumulation (ACCU)

The bioaccumulation of an organic chemical can be approximated by its octanol−water partition coefficient, log P or log Kow. For this exercise, chemicals with a log Kow of 3.5 or lower have a low bioaccumulation potential, those with a log Kow value of 3.5−4.3 have a moderate bioaccumulation potential, and those with a log Kow value greater than 4.3 are considered to have a high bioaccumulation potential.9 The bioaccumulation potential is not multiplied by mass. 217



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(Table 2). Students can save time by assuming that AP, ODP, SFP, GWP, and INHTP are zero for compounds that have negligible volatility. Students should enter “n/a” (not applicable) for inorganic compounds in the PER and ACCU columns and for products, byproducts, and intermediates in the ADP column. There is often insufficient literature data for less-common chemicals. If a value cannot be calculated or reasonably estimated, then students leave the potential as an unknown. To meet time constraints, students should be told not to spend an exorbitant amount of time searching for hard-to-find data. Impact indices are then calculated and entered into a table of indices (Table 3). For the PER and ACCU columns, the values from Table 2 are used without modification. Cells in Table 3 that contain the highest value in a column can be labeled with color unless the value is trivially low; in the example shown, benzene and dichloromethane stand out as the most problematic chemicals in the route. This gives students an indication of the chemicals responsible for the greatest impact and, therefore, an indication of what needs to be improved. The sum of the contents of each column is the index for the entire route and is shown in the bottom row. The values for persistence and bioaccumulation cannot be summed, so the longest persistence time from the Boethling index and the largest log Kow are recorded in the summation line for each route. With the three tables for each process completed, the summed indices (the last line of the table of indices) for each route can be compiled and compared (Table 4, values rounded to one significant digit). To facilitate visual comparison, we recommend labeling the cells in the table using a green, yellow, and red coloring system, where the highest index for a metric, or the least benign route based on that particular metric, is colored red, the lowest value is assigned green, and the other values are labeled yellow; close ties are given the same color. For the ACCU column, however, it makes more sense to use

Table 1. Materials for Route 1 Mass Mass Used/kga Produced/kg

Material

Role

Benzene Me3SiN3 Triflic Acid Me3SiOSO2CF3 NaOSO2CF3 Dichloromethane NaOH MgSO4 CO2

reagent/solvent reagent reagent byproduct byproduct solvent reagent drying agent energy byproduct

20 1.3 9.8 236 2.3 8.9 -

2.4 9.3 1.83

Mass Emitted/kg 2.0 × 10−2 1.30 × 10−3 9.8 × 10−3 2.4 × 10−3 9.3 0.24 0.12b 8.9 1.83

a

For a 1 kg scale. bThe amount of NaOH remaining after the balanced reaction and neutralization of the excess of triflic acid.

because they score zero on all potentials. CO2 is included in the list whether or not it appears in the reaction equation; the quantity of CO2 produced is that due to energy consumption plus any produced in the reaction. The students then scale up the process to generate 1 kg of product. It is assumed that 0.1% of the mass of every compound used is emitted to the environment if the compound can be incinerated afterward (e.g., organic solvents, organic byproducts) and 100% is emitted if the compound cannot be incinerated (e.g., inorganic compounds, gases, drying agents). Unfortunately, most synthetic procedures in the literature are very poorly documented (e.g., How much solvent and packing material were used in a column separation? How much drying agent was used?). These failings of the literature make it necessary for the students to make assumptions to fill in the missing data. Such assumptions are given in the Supporting Information. The calculated masses are also included in the table of materials. Every material in the list can then be assigned a potential for each metric. The data is collected in a table of potentials Table 2. Potentials for Route 1 Material

a

AP

ODP

SFP

GWP

INHTP

INGTP

PER

ACCU Log Kow

ADPa 0 0 F: 3.0 × 10−6 S: 3.6 × 10−4 n/a n/a 0 0 S: 3.6 × 10−4 0

Benzene Me3SiN3 Triflic Acid

0 0 0

0 0 ?

0.14 0 ?

3.4 0 0

12 ? 0

1.0 ? 4.7 × 10−2

months months weeks

2.1 2.3 −0.5

Me3SiOSO2CF3 NaOSO2CF3 Dichloromethane NaOH MgSO4 CO2

0 0 0 0 0 0

0 0 0.4 0 0 0

0 0 3.0 × 10−2 0 0 0

0 0 0.5 0 0 1

? 0 5.0 × 10−2 0 0 0

? ? 160 ? ? 0

months n/a weeks n/a n/a n/a

0.6 n/a 1.3 n/a n/a n/a

Given for each relevant element.

Table 3. Indices for Route 1

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Table 4. Comparison of Routes

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CONCLUSION In green chemistry and engineering education, it is important to give students the tools and the skills to make environmentally conscious decisions. This exercise allows the students to make such a decision themselves, using multivariate LCA metrics, which are much more reliable indicators of environmental impact than the univariate metrics E Factor and atom economy. Although multivariate metrics take more work to calculate, they are not too difficult for an upper-year undergraduate class on green chemistry. Guiding the students through an LCA-style assessment allows them to see for themselves the strength of the method and the effects of decisions like solvent choice on the environmental impact of a process. This exercise, as a major assignment in a course, provides learning opportunities in the realms of scale-up chemistry, environmental assessment, and reaction modification and engineering. It instills an appreciation of the strengths and necessity of comprehensive studies when evaluating whether to attach a label of “green” to a particular chemical or process.

green, yellow, and red for log Kow values of 4.3, respectively.

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DISCUSSION

After completing the exercise, students can assess the comparison table and identify the greenest route. Appropriate questions that can be posed to the students in the assignment are • If you had been using E Factor or atom economy as your sole metric, would you have chosen the same route as the greenest? • Do you think that the greenest route is also the safest? • These calculations require many assumptions. If the error bars are large (e.g., a value in Table 4 could be double or half the correct value), could that affect how certain you are of the greenest route? • This exercise was a gate-to-gate impact assessment. What would be involved in making it into a cradle-to-grave impact assessment? • How could each route have been made greener? (Hint: Consider the table of indices). • The comparison of routes neglected costs, even though industry would of course take costs into consideration. What could a process chemist do if the cost comparison and the impact comparison made different recommendations? In our example, the results in Table 4 suggest that route 5, the current industrial route, is the most green. Route 4, the route commonly taught in organic chemistry courses, is the least green despite having one of the smaller E factor values. The other three routes possess an equal number of disadvantages so that their overall ranking is difficult to distinguish. Situations in which two or more routes seem to have comparable impact provide students the opportunity to discuss, rationalize, and prioritize metrics, as is often done in industry. The instructor can encourage the students to discuss the accuracy of the calculation. As part of this discussion, it should be noted that the accuracy of the results are weakened by the assumptions that are necessary to fill in the gaps in the literature method description. Recognition of this problem may encourage the students, if they go on to do research themselves, to describe their experimental methods more carefully than is currently the norm in chemistry journal articles. In a formal LCA calculation, however, those missing details would be determined properly by experimentation. Students can use the information in Tables 3 and 4 to make intelligent proposals about how their route can be improved. This practice of modification and improvement is fundamental to green chemistry and engineering and perhaps the most important concept of the exercise. The concluding reflection allows students to use their growing expertise in chemistry and engineering and facilitate peer learning, discussion, and innovation.

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ASSOCIATED CONTENT

* Supporting Information S

Description of assumptions; explanation of multicompartmental model (MCM) calculations; details of the five routes for making aniline from benzene. This material is available via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

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ACKNOWLEDGMENTS The authors thank the National Science and Research Council of Canada, the Canada Research Chairs program, and the Walter C. Sumner Foundation for funding, Mary Kirchhoff of the American Chemical Society for valuable advice, and the former participants in this exercise at Queen’s University and the ACS Summer School on Green Chemistry for their feedback.

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REFERENCES

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(9) Allen, D. T.; Shonnard, D. R. Green Engineering: Environmentally Conscious Design of Chemical Processes; Prentice-Hall: Upper Saddle River, NJ, 2001. (10) Heijungs, R.; Guinée, J.; Huppes, G.; Lankreijer, H. A.; Udo de Haes, A.; Wegener Sleeswijk, A. M. M.; Ansems, P. G.; van Duin, R.; de Goode, H. P. Environmental Life Cycle Assessment of Products; CML: Leiden, The Netherlands, 1992. (11) World Meteorological Organization. Scientific Assessment of Ozone Depletion: 1991; Global Ozone Research and Monitoring Project − Report #25, Geneva, Switzerland, 1992. (12) World Meteorological Organization. Scientific Assessment of Ozone Depletion: 1998; Global Ozone Research and Monitoring Project − Report #44, Geneva, Switzerland, 1999. (13) Nimitz, J. S.; Skaggs, S. R. Environ. Sci. Technol. 1992, 26, 739− 744. (14) Solomon, S.; Mills, M; Heidt, L. E.; Pollock, W. H.; Tuck, A. F. J. Geophys. Res., [Atmos.] 1992, 92, 825−842. (15) Carter, W. P. L. Air Waste 1994, 44, 881−899. (16) International Panel on Climate Change Third Assessment ReportClimate Change 2001. http://www.grida.no/publications/ other/ipcc_tar/?src=/climate/ipcc_tar/wg1/248.htm (accessed Nov 2011). (17) Stageland, A.; Frisvold, P.; Hauge, F. Bellona Position Paper on CO2 Capture and Storage and Emission Performance Standards; The Bellona Foundation: Oslo, 2008. (18) Boethling, R. S.; Howard, P. H.; Meylan, W.; Stiteler, W.; Beauman, J.; Tirado, N. Environ. Sci. Technol. 1994, 28, 459−465. (19) Kwok, E. S. C.; Atkinson, R. Atmos. Environ. 1995, 29, 1885− 1695. (20) Olah, G. A.; Ernst, T. D. J. Org. Chem. 1989, 54, 1203−1204. (21) Parida, K. M.; Dash, S. S.; Singha, S. Appl. Catal., A 2008, 351, 59−67. (22) Poojary, D.; Borade, R.; Hagemeyer, A.; Dube, C. D.; Zhou, Z. P.; Nothels, U.; Armbrust, R.; Rasp, C.; Lowe, D. M. Amination of Aromatic Hydrocarbons and Heterocyclic Analogs Thereof. WO Patent 0069804, 2000. (23) Fieser, L. F. Experiments in Organic Chemistry; D.C. Heath & Co.: New York, 1941; pp 144−151. (24) Faith, W. L.; Keyes, D. B.; Clark, R. L. Industrial Chemicals; John Wiley & Sons: New York, 1965; pp 104, 541.

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NOTE ADDED AFTER ASAP PUBLICATION This article was published ASAP on December 5, 2011. Data in Tables 2, 3, and 4 has been corrected. The corrected version was posted on December 22, 2011.

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Choosing the Greenest Synthesis: A Multivariate Metric Green

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