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Useful Tools for the Next Quarter Century of Green Chemistry Practice – A Dictionary of Terms and a Dataset of Parameters for High Value Industrial Commodity Chemicals John Andraos ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03360 • Publication Date (Web): 08 Jan 2018 Downloaded from http://pubs.acs.org on January 9, 2018

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ACS Sustainable Chemistry & Engineering

1 Useful Tools for the Next Quarter Century of Green Chemistry Practice – A Dictionary of Terms and a Dataset of Parameters for High Value Industrial Commodity Chemicals

John Andraos*, CareerChem, 504-1129 Don Mills Road, Toronto, ON M3B 2W4 Canada ([email protected])

Abstract

A dictionary consisting of 260 commonly used terms in the green chemistry literature over the last quarter century is compiled for easy reference in a single source. The best available datasets of key parameters important for evaluating the degree of greenness of chemical processes for about 300 first and second-generation chemical feedstocks, including 75 solvents, relevant to life cycle, safety-hazard, and energy consumption assessments are also presented. Lavoisier numbers are introduced as a new kernel material efficiency metric analogous to process mass intensity.

Keywords: green chemistry education, green chemistry engineering, sustainability metrics

Introduction

As the chemistry community celebrates a quarter century of green chemistry philosophy and practice since the publication of Barry Trost’s seminal paper on the concept of atom economy 1, it seems fitting at this time to take stock of developments in the field. From a practical and useful point of view, the best way to recap developments in a scientific field for the benefit of novices and professionals alike is to summarize in one place the most common terminologies used in that field. Such a listing forms the foundation of research in the field and is an effective teaching tool for quickly bringing people up to speed on our current understanding and practice. Learning about the

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2 vocabulary of a scientific area is therefore a fundamental first step. For this special issue we have compiled a dictionary of 260 terms relevant to the quantification of greenness or synthesis efficiency, commonly called green metrics or sustainability metrics. Accompanying this list are two datasets containing the best available values of key parameters important for carrying out life cycle and safety-hazard assessments for chemical processes involving about 300 high value industrial commodity chemicals. A third data set of thermodynamic parameters for these compounds is also provided since they are important for estimating energy consumption. We focused on these materials since they form the basis of all the chemical industry and to illustrate how much we have done and how much is still left to do in order for us as a scientific community to continue work in this important field for the benefit of all humanity.

Vocabulary of Sustainability Metrics

The alphabetical list given in the Supporting Information (green-chemistrydictionary.pdf) includes the most relevant technical terms used in practice along with brief definitions and original literature references where they first appeared. The list is up-to-date covering the period 1991 to 2017. For terms referring to particular physical and toxicological parameters of compounds, a list of references is included where reliable values of such parameters can be obtained. For the purposes of this work, no comment is made as to the frequency of a particular term’s use in the literature or on the ranking of importance of a given term. We have recently critically reviewed and compared the performances of various material efficiency, environmental impact and safety-hazard impact metrics and algorithms for calculating them 2,3 where such commentary was made. In those references we have also thoroughly discussed the relevance of the associated terminologies in the context of practicing green chemistry principles including comparisons of strengths and weakness of various algorithms and tools available to compute the related metrics in all categories pertinent to metrics calculations. For clarity, metrics pertaining to material efficiency metrics were dealt with separately from those pertaining to environmental and safety-hazard impact. We also discussed pitfalls and examples of errors in computation and abuses of claimed greenness that have appeared in


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3 the literature. We therefore direct the interested reader to those discussions for background information. However, what was not covered there, but will soon be addressed by an IUPAC Metrics for Green Syntheses (MGS-1) Project is a vetting of all terminologies relevant to green chemistry metrics with a view to provide rationales for ranking them with respect to accuracy, utility, and relevance to current practice. As in any committee tasked to examine nomenclature in a scientific field, of particular interest will be decisions regarding which metrics terminologies will be considered favoured or discouraged depending on current understanding and usage. That work is expected to be completed by the end of 2018 and is anticipated to gather significant interest within the green chemistry community. One key point that needs mentioning is that such a ranking process requires collaboration among many scientific partners from academia and industry and agreement must be achieved through consensus since many of the topics are not developed to the same degree, source data gaps exist, and controversies among metrics terminologies are not yet resolved (see vide infra). For the sake of brevity and conciseness, in this preliminary work our purpose is not to preempt the IUPAC project but rather to present all terms at face value in simple language that a novice can understand. In the present discussion special attention is paid to concepts or terms referring to quantifiable variables that are given different names by different authors. This has been a chronic problem in the green chemistry literature which unfortunately has caused much confusion and undue duplication, particularly for novices who wish to learn about the field for the first time. In the last five years there has been a noticeable divergence between academic and industrial groups in the sustainability metrics literature to the point where contextual or comparative discussions as to the merits and limitations of one method over another are conspicuously absent. An example of this scenario is the latest work by authors from the pharmaceutical industry representing the IQ (Innovation and Quality) Consortium Green Chemistry Working Group who advance a suite of trademarked metrics terminologies 4. These are quickly calculable using back-of-theenvelope elementary arithmetic that is palatable for “scientists in a hurry” working in industry who have limited time available to do the tedious task of quantitative analyses of literature synthesis plans as they would wish, but who also recognize that practicing



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4 green chemistry makes good business sense in the modern world where the traditional and irrational paradigm of infinite availability of resources has shifted to one of finiteness. Examples of such fast calculations are the estimation of the E-factor for a synthesis by simply counting the total number of reaction steps and multiplying by 37, or the estimation of the Green Aspiration Level (GAL) by counting the number of construction or target bond forming steps in a synthesis and multiplying by 26. Though these appear to be attractive with respect to short cutting time and labour we point out that such a general and oversimplified approach can lead to erroneous conclusions with respect to ranking of synthesis plans based on material efficiency greenness criteria. All of our past work on synthesis plan analysis clearly shows that each plan needs to be examined in detail on a case-by-case basis in order to make reliable rankings and thus draw reliable conclusions about advantages and disadvantages of one plan over another 2. Unfortunately, the report by the IQ Consortium only cited papers from other fellow pharmaceutical scientists while completely ignoring important previous work done by others. Nevertheless, the terms they used are also included in our compilation without bias. Table 1 summarizes a complete list of concepts that have duplicate nomenclatures arranged according to first appearance in the literature. Readers should take notice of these terms in their own readings of the literature and be aware of their interconnections. Another problematic issue is the coining of apparently “new” metrics which are simple arithmetic manipulations of existing metrics. For example, Clark 5 recently introduced waste intensity (WI) and optimum efficiency (OE) as “new” metrics when in fact they are simply the ratio of E-factor to process mass intensity (PMI) and the ratio of global reaction mass efficiency (gRME) to atom economy (AE), respectively. To some these extra terms may be interpreted as redundant and therefore do not add any real value to the discussion of parameterizing the degree of greenness for a given reaction or synthesis plan to a desired product. Both trends of re-branding existing names with new ones and manipulating established metrics into new ones are expected to continue for the foreseeable future in step with the noted trend of divergence. This means that the present compilation will continue to grow and future updates will be needed. At present, even after two decades of study, there is still no universal acceptance of any single or set of metrics that best describes material efficiency for an individual


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5 reaction or synthesis plan. Academics tend to gravitate to overall yield and number of steps in a synthesis plan, while workers in the pharmaceutical industry have embraced process mass intensity as the preferred metric along with its variants. As shown in Figure 1, 163 years after the establishment of reaction yield (also known in foreign language literature as ausbeute (Ger.), rendement (Fr.), and resa (It.)) as the first material efficiency metric applied to the synthesis of urea, the modern evolution of green or sustainability metrics began with the introduction of atom economy as the launching metric in 1991 and was soon followed by the E-factor in 1994. Interestingly, all of the reactions described in Trost’s landmark paper had atom economies of 100 %, so no calculations were required. In fact, none of Trost’s follow-up papers included a formula for calculating this fundamental metric. In the early years leading up to 2005 these and other metrics were introduced in the literature as separate unconnected entities often in competition with one another as to which were better at capturing the concept of material efficiency. They fell into two main groups depending on one’s point of view. If one considered the glass-half-full philosophy, one opted to choose metrics that selected for reaction performance on the basis of how much of the input materials end up in the product. On the other hand, if one considered the glass-half-empty philosophy, one opted to choose metrics that selected for reaction performance on the basis of how much waste output was produced per unit mass of target product. Both philosophies describe the same thing once one recalls the law of conservation of mass in chemical reactions introduced by Antoine Lavoisier in 1775, but yet was neither considered nor appreciated by those who introduced metrics philosophy in the context of green chemistry in the first place. Hence, PMI and E-factor based on mass are two sides of the same coin as are AE and E-factor, Emw, based on molecular weight. However, since one pair of variables is based on mass and the other based on molecular weight they do not necessarily track in the same direction since there is no connection between the molecular weights of reactants and products for a given reaction and the masses of all input and output materials involved in that reaction. This means that a high AE reaction does not necessarily have a low PMI, for example. In fact, since AE and PMI can each be either low or high there are four possible permutations for extreme outcomes to consider: (a) high AE and high PMI corresponding to a reaction producing minimal by-products but



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6 consuming significant auxiliary material and/or having a low reaction yield; (b) high AE and low PMI corresponding to a reaction producing minimal by-products and consuming few auxiliary materials and/or having high reaction yields (best scenario); (c) low AE and high PMI corresponding to a reaction producing significant by-products and consuming significant auxiliary material and/or having a low reaction yield (worst scenario); and (d) low AE and low PMI corresponding to a reaction producing significant by-products but consuming few auxiliary material and/or having high reaction yields. It is important to recognize that employing mid- to low-performing reactions with respect to AE in synthesis plans significantly reduces the chances of overall positive “green” performances for entire plans in spite of any gains made in reducing auxiliary material consumption. These rather different scenarios arise as a direct result of the independence of AE from PMI and not as a consequence of a perceived inherent philosophical difference between waste productions versus input material efficiency usage. Then, in 2005 mathematical relationships, shown in Figure 2, were found that connected these apparently advertised “separate” and competing perspectives into a unified whole as a direct consequence of applying what Lavoisier taught us, namely that any chemical reaction can be depicted by a balanced chemical equation representing the transformation of reactants to the intended reaction product and its associated by-products, if any. As an aside, it is worth mentioning here that Lavoisier’s idea of balanced chemical equations arose as a direct consequence of his actual occupation as a royal accountant and tax collector, a task which involves balancing credits and debits. If accounts could be balanced in a ledger, then why not apply this notion to chemical reactions in the laboratory? The result of unification was the establishment of a set of core metrics applicable to individual reactions and entire synthesis plans that form the essential pillars of how to quantitatively describe reaction performance in a succinct manner without overburdening the issue. It can be argued, then, that the true origin of green metrics thinking goes all the way back to Lavoisier, nearly two and a half centuries before today’s practice of green chemistry, but his seminal ideas lay dormant until recently. Therefore, in order to recognize and entrench Lavoisier’s contribution to quantitative green chemistry we put forward the name Lavoisier number (LN) to represent the inverse of atom economy based on molecular weight to be a parallel metric to process mass


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7 intensity as the inverse of global reaction mass efficiency based on mass (see second equation in Figure 2). Currently no such metric exists paralleling PMI that is based on molecular weight and so the Lavoisier number fills this gap in the general formalism of material efficiency green metrics. In our opinion we believe this honour is long overdue and is appropriate to bestow at a time when chemists celebrate a quarter century of green chemistry as a modern branch of chemical science. An ideally designed reaction that produces a chemical with a 100 % atom economy will have a Lavoisier number equal to 1. If a by-product arises, then the reaction will have a Lavoisier number greater than 1. Therefore, maximization of atom economy is identical to minimization of the Lavoisier number. Following Sheldon’s numerical argument that a metric that increases in value in step with increasing waste output has more impact conceptually and psychologically than a metric that decreases in value, the higher the Lavoisier number for a given reaction the more potentially wasteful it is. Lavoisier numbers for reactions suggest a convenient scale that measures how far away from ideality they are at the kernel level. For example, a reaction producing a target molecule with a Lavoisier number of 2 (i.e., with an atom economy equal to 0.5 or 50%) means that it is two times further away from ideality than another reaction producing the same target molecule with no by-products. The Supporting Information contains tables of maximum Lavoisier numbers (Lavoisiernumbers-named-organic-reactions.pdf) for the named organic reactions library categorized according to the seven main groups of reaction types: additions (carboncarbon bond forming and non-carbon-carbon bond forming, condensations, and multicomponent reactions), substitutions, eliminations, rearrangements, oxidations, reductions, and sequences. These were determined from an extensive library of balanced chemical equations written using Markush structures as described previously 26. Figure 3 shows a series of associated histograms that form a visual display of the current inventory of an organic chemist’s toolbox with respect to which kinds of reactions should be selected in the design of material efficient syntheses of molecular targets having complex structures. We observe from this figure that redox and elimination reactions have the broadest ranges of Lavoisier numbers exceeding unity consistent with significant byproduct formation from these classes of reactions. In contrast, rearrangement, condensation, and multicomponent reactions have the narrowest Lavoisier number distributions centred between



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8 1 and 2. Therefore, these results are consistent with the strategy of selecting reactions from the latter pool of reactions rather than the former when planning a synthesis of a complex molecule in line with optimizing for various synthesis economies: step, atom, redox, and pot. Despite the obvious step forward of unified metrics, the continuing growth of yet further material efficiency metrics shows that the chemistry community is not yet convinced or satisfied that the parameterization of material efficiency for individual reactions or synthesis plans is well described. Four successive reviews 27-30 of the impact of the E-factor (glass-half-empty philosophy) in the literature continue to brand this metric as THE best metric of choice, even comparing the citation count of it against its rival process mass intensity metric (glass-half-full philosophy) as sufficient evidence of its apparent superiority. Moreover, these reviews dismiss the unified approach as an “attempt” and go on to state 30 that “In our opinion none of these alternative metrics offer any particular advantage over the E factor for describing how wasteful a process is.” Further, it is written 30: “The ideal PMI is 1, whereas the ideal E factor is 0, which more clearly reflects the ultimate goal of zero waste. The E factor also has the advantage that, in evaluating a multi-step process, E factors of individual steps are additive but PMIs are not because PMI doesn’t discount step products from the mass balance.” The last statement implies that in order to obtain the E-factor for an entire synthesis all one needs to do is add up the E-factors for each reaction step as Corma and coworkers demonstrated in a comparative study of fine chemical synthesis plans 31. However, an elementary proof showed that such a statement is mathematically incorrect for both E-factors and PMI 2 since the E and PMI values for each reaction step are determined with respect to the mass of product obtained at each step, whereas the step E and PMI contributions to the overall E and overall PMI, respectively, are determined with respect to the mass of the final product of the entire synthesis plan. In the former case the frame of reference in the metrics calculation keeps changing whereas in the latter it is constant. Therefore, the correct statement is that it is the step E and step PMI contributions to the overall respective metrics values that are additive, not the individual step E and PMI metrics themselves.


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9 The final ongoing literature debate discussed here in the context of vocabulary deals with the development of relationships between cumulative material efficiency metrics and reaction step metrics applied to linear synthesis plans. A recent reference 32 made the claim that such relationships did not exist when in fact they were presented in 2015-2016 33, 2. Below we summarize in one place equations (1) to (6) that show all of the key relationships including reaction yield, atom economy, E-factor, PMI, gRME, and Lavoisier numbers introduced in this work applicable to a linear synthesis plan of N steps producing 1 mol of product. For each equation we also show what the overall metric quantities are not equal to. (1)

(2)

εT =

N

∏εj

( AE )T

j =1 =

 N −1  1  1 1  ∑ ( MW )Y  − 1  + j    AE AE ( MW )P  j =1  ( )j   ( )N

(4)

PMIT = ET + 1 = PMI N +

(6)

j =1

( )

ET = E N +

gRME =

≠ ∏ ( AE ) j

N 1 N −1 m E ≠ ∑ Yj j ∑ Ej mP j =1 j =1

(3)

(5)

N

1

1 = ( PMI )T

( LN )T = ( LN ) N +

1 N −1 ∑ mY j mP j =1

(

)

( PMI ) j − 1 ≠

N

∑ ( PMI ) j

j =1

1 N −1

  1 1 1   1 + m − Y j RME (  ( RME ) N mP j∑ ) j =1  

1 ( MW ) P

N −1

∑ ( MW ) ( ( LN ) j =1

Yj

)

≠

N

∏ ( RME ) j j =1

N

− 1 ≠ ∑ ( LN ) j j j =1

where the subscripts T and N refer to the total or overall value of the metric and the value of the metric for the Nth step in the linear synthesis plan, respectively; ε is reaction yield; AE is atom economy; MW is molecular weight; E is E-factor; PMI is process mass intensity; gRME is global reaction mass efficiency; LN is Lavoisier number;



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10

mY j =

( MW )Y

j

ε j +1...ε N

, mYj and (MW)Yj represent the mass and molecular weight of intermediate

product Yj along the linear chain, respectively, mP is the mass of the target product, and

( MW )P

is the molecular weight of the target product. We may also write the following

recursive formulas for the same cumulative metrics (equations (7) to (12)) which are amenable to calculation by computer programming methods in an iterative sense. (7)

(8)

ε1→ j +1 = ε1→ jε j +1

( AE )1→ j +1 =

( MW )Y

j  ( AE )1→ j − 1 + ( AE ) j +1  ( MW )Y  j +1 mY j

(9)

( E )1→ j +1 =

(10)

( PMI )1→ j +1 =

(11)

( RME )1→ j +1 =

(12)

( LN )1→ j +1 =

mY j +1

( E )1→ j + ( E ) j +1

mY j  ( PMI )1→ j − 1 + ( PMI ) j +1   mY j +1 1  mY  1 1 j  − 1 + mY RME ) RME ) 1→ j  ( j +1 j +1  (

( MW )Y

1

  1 1 j  − 1 + ( MW )Y  ( LN )1→ j  ( LN ) j +1  j +1 

Table 1. List of duplicate nomenclatures used in sustainability metrics. First

Seminal reference

Duplicate

occurrence

occurrence

term

term

Coupled

Secondary reference

6

By-product

7, 8

9

Effluent load

10

product E-factor


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11 factor Environmental

9

quotient Global

Environmental

11, 12

index 21

Global

process mass

material

intensity

economy;

20

Global mass efficiency Global

13

Balance yield

13

Actual or

14

reaction mass efficiency Kernel reaction mass

experimental

efficiency

atom economy

Mass index

19

Inverse of

15, 16, 17, 18

13

global reaction mass efficiency Mass intensity

11

Process mass

21

intensity Overall

13

reaction mass

Global mass

22

efficiency

efficiency Sacrificial

23

step Target bond forming step

Concession

24, 25

step 23

Construction step


24, 25


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12

Figure 1. Evolution of selective sustainability metrics describing material efficiency.

Figure 2. Interconnecting relationships between material efficiency metrics.


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13

Figure 3. Histograms of number of reactions versus maximum Lavoisier numbers for various reaction types found in the library of named organic reactions.

Life Cycle Assessment and Safety-Hazard Datasets for High Value Industrial Commodity Chemicals

Accompanying the vocabulary compilation we also present the best available physical and toxicological parameters for about 300 high value industrial commodity chemicals (see Supporting Information). Among these are the following chemicals used as solvents: water, acetic acid, acetic anhydride, formic acid, ethylene glycol, n-butanol, sec-butanol, tert-butanol, ethanol, 2-propanol, methanol, 2-methoxyethanol, benzyl alcohol, isopropyl acetate, ethyl acetate, diethyl carbonate, dimethyl carbonate, n-butyl acetate, methyl isobutyl ketone, methyl ethyl ketone, acetone, p-xylene, p-cymene, toluene, pyridine, benzene, isooctane, n-heptane, cyclohexane, n-hexane, n-pentane, petroleum ether, 2-methyltetrahydrofuran, tetrahydrofuran, diisopropyl ether, 1,4dioxane, diethyl ether, methyl tert-butyl ether, dimethoxyethane, dimethyl sulfoxide, acetonitrile, N-methylpyrrolidinone, nitromethane, dimethylformamide, triethylamine, sulfolane, chlorobenzene, dichloromethane, 1,2-dichloroethane, chloroform, and carbon tetrachloride. Two datasets are presented. The first deals with the following parameters relevant to determining environmental impact in life cycle assessments (see databaseindusrial-chemicals-LCA-parameters.xls in Supporting Information): number of carbon



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14 atoms for global warming potential (GWP) determination, number of acid hydrogen atoms in chemical structure whose pKas are less than 7, acidification-basification potential (ABP), smog formation potential (SFP), LD50(oral, rat), LC50(inhalation, rat), Henry law constants (HLC), octanol-water partition coefficients (log Kow), abiotic resource depletion potential (ARDP), and photochemical ozone creation potential (POCP). The second deals with the following parameters relevant to determining safetyhazard impact (see database-industrial-chemicals-safety-hazard-parameters.xls in Supporting Information): number of oxygen atoms for oxygen balance (OB) determination, LC50(dermal, rabbit), flash point (FLP), lower explosion limit (LEL), occupational exposure limit (OEL), water solubility (WS), skin dose (SD), risk phrases (RP), and Q-factors. All of these parameters are well recognized as being fundamental to any kind of LCA determination. Among these datasets we identify those compounds for which data are known, unknown, or non-applicable. Tables 2 and 3 summarize statistics found for each of these categories for both datasets applicable to the entire group of 300 chemicals and 75 solvents, respectively.

Table 2. Summary of statistics for various physical and toxicological parameters for 300 high value industrial commodity chemicals. % % % Not Parameter Known Unknown applicable LD50 (oral, rat) 75.9 12.4 11.7 LC50 (inhalation, rat) 48.2 49.8 2.0 SFP 20.8 61.6 17.6 ODP 1.0 11.7 87.3 POCP 18.9 62.9 18.2 LD50 (dermal, rabbit) 38.1 46.9 15.0 FLP 60.6 7.5 31.9 LEL 47.6 19.9 32.6 OEL 69.4 30.3 0.3 risk phrases 96.1 2.3 1.6 Table 3. Summary of statistics for various physical and toxicological parameters for 75 solvents. Parameter

% % % Not Known Unknown applicable


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15 LD50 (oral, rat) LC50 (inhalation, rat) SFP ODP POCP LD50 (dermal, rabbit) FLP LEL OEL risk phrases

90.7 65.3 37.3 1.3 42.7 54.7 90.7 81.3 74.7 97.3

9.3 33.3 62.7 10.7 56.0 42.7 2.7 12.0 24.0 1.3

0.0 1.3 0.0 88.0 1.3 2.7 6.7 6.7 1.3 1.3

We note from the results given in Table 2 that a significant proportion of data are missing in the literature for the following parameters: smog formation potential (62 %), LC50 (50 %), LD50(dermal) (47 %), occupational exposure limits (30 %), and photochemical ozone creation potential (63 %). Clearly, these gaps jeopardize conducting any meaningful and reliable LCA or safety-hazard impact analyses and interpreting their results, particularly in that part of the analysis where one chemical process is ranked against another. So long as this situation persists these problems will remain chronic. We highlighted this problem in our previous review of environmental and safety-hazard metrics and emphasize it here again.3 A further point that needs to be emphasized is that most of the industrial compounds given in the present list have been well studied and well documented in the literature, so it would be safe to say that the data are the most reliable available. Despite this apparent positive scenario, obvious data gaps persist which does not bode well for holistic analyses of any synthesis plans to more exotic chemical compounds that inevitably need to be traced back to this progenitor set of compounds through cradle-to-gate or cradle-to-cradle LCA analyses. In the next quarter century any meaningful work done in the field of green chemistry must eliminate this serious shortcoming otherwise the field will be permanently in limbo. Having said that, the results presented in Table 3 for solvents (see database-solvents.xls in Supporting Information) shows a higher proportion of parameters that are known across the board which is consistent with the expected trend that the pharmaceutical industry has focused on this group of chemicals to “green up” synthetic procedures since they form the bulk of material (at least 75 %) used in any given chemical reaction or process, and consequently the bulk of the waste produced. The publication of various solvent guides 34-36 suggest



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16 the following chemicals as “green solvents” mainly based on their minimum valued safety, occupational health, and environmental impact scores: water, ethanol, isopropanol, n-butanol, tert-butanol, isobutanol, isoamyl alcohol, ethylene glycol, methyl ethyl ketone, methyl isobutyl ketone, ethyl acetate, isopropyl acetate, n-butyl acetate, isobutyl acetate, isoamyl acetate, glycol diacetate, anisole, tert-amyl methyl ether, and dimethyl carbonate. The present compilation of parameters verifies the justification of the elementary ranking scores used to select this group of solvents. An argument made by a referee stated that data gaps in toxicological parameters are routinely overcome by computational methods commonly referred to as computational toxicology 37,38 as a practical and cost-effective way to close gaps as necessary and that the current situation is not as problematic as claimed here. However, a serious caveat with this approach as noted by others 39 in the field is that any kind of computation cannot be used as a panacea for lack of experimental verification. Pfizer chemists 39 noted that “there is a tendency for these approaches to be hyped up and [that] claims of reliability and performance may be exaggerated”. The success of computational methods in predicting toxicological parameters with a reasonable degree of certainty rests solely on the size and quality of the training sets on which they depend. These training sets are of course determined from experimental data. Limitations of such computational methods have been thoroughly discussed. A particularly vexing problem is the use of structural similarity analysis in read-across assessments in order to infer the toxicological activity of a molecule with unknown activity. The authors noted that the dilemma lies in defining what is similar and what is not when comparing chemical structures. Another key problem is that reported LCA assessments in the literature, in general, do not disclose degrees of associated uncertainties in those assessments. The concluding remarks of those authors are worthy of repeating here 38: “The computational predictions are only as good as the data used to train the model and the inherent noise in these data sets is often overlooked when training or assessing performance. Similarly, the current lack of knowledge about mechanisms of toxicity along with the fact that multiple molecular initiating events can lead to the same observed phenotype make it difficult to select the best measures of chemical similarity to use in any given model. Clearly, the need for higher throughput and more cost effect approaches for safety assessments make


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17 computational approaches a useful tool in toxicology but if used inappropriately or without consideration for the limitations of the approaches then this may lead to poor or regrettable decisions being made.” It is not unreasonable therefore to suggest that existing gaps as noted in Tables 2 and 3 for the 300 first and second generation feedstock chemicals be closed by experimental verification to improve the quality of existing training sets. Other well-developed areas of computational chemistry such as geometry optimization of chemical structures and prediction of 3-dimensional protein structures from primary amino acid sequences have evolved in step with experimental data verification. It is therefore scientifically prudent and advisable that computational toxicology also follows this trend in order for any practitioner to have confidence in believing the results of those algorithms.

Thermodynamic Parameter Datasets for High Value Industrial Commodity Chemicals Next, we present the best available thermodynamic parameters important for determining energy consumption for chemical reactions for the same set of 300 high value industrial commodity chemicals (see database-indusrial-chemicals-thermodynamicparameters.xls in Supporting Information). These include melting point, boiling point, phase at standard temperature and pressure (25oC, 1 atm), enthalpy of fusion at melting point, enthalpy of vaporization at boiling point, critical temperature, critical pressure, acentric factor, thermal expansion coefficients, molar volume coefficients, liquid vapour pressure coefficients, and heat capacity coefficients at constant pressure (1 atm) for each of the three phases. The best resources to obtain thermodynamic parameters for industrial chemicals are the DIPPR (Design Institute for Physical Property Data) database 40

and Yaws 41 which were recently used by our group to determine and compare

theoretical energy consumptions for 18 industrial routes to methyl methacrylate 42. Among these datasets we identify those compounds for which data are known, unknown, estimated, and not applicable. Table 4 summarizes statistics found for each of these categories for this dataset.



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18 Table 4. Summary of statistics for various thermodynamic parameters for high value industrial commodity chemicals. % % % % Not Parameter Known Unknown Estimated applicable 0.6 Tb (K) 87.9 11.1 0.3 0.3 Tm (K) 98.7 0.6 0.3 ∆ H(fus) (kJ/mol) 2.5 71.7 25.5 0.3 2.2 77.7 19.7 0.3 ∆ H(vap) (kJ/mol) 3.2 Tc (K) 81.5 15.0 0.3 3.2 Pc (atm) 81.5 15.0 0.3 3.5 acentric factor 80.9 15.3 0.3 0.0 thermal expansion coefficients 74.5 25.2 0.3 0.0 molar volume (L/mol) 80.9 18.8 0.3 0.0 liquid vapor pressure (atm) 80.9 18.8 0.3 0.6 heat capacity coefficients (gas) 87.9 11.1 0.3 0.3 heat capacity coefficients (liquid) 98.7 0.6 0.3 2.5 heat capacity coefficients (solid) 71.7 25.5 0.3 We note from the results given in Table 4 that about a fifth of the data are missing in the literature for the following parameters: enthalpy of fusion and vaporization, critical temperature and pressure, acentric factor, thermal expansion coefficients, temperature dependent molar volume, and temperature dependent heat capacity functions for all three phases. Of particular attention are missing thermodynamic parameters for chemicals that are routinely used as solvents particularly 2-methoxyethanol (ethylene glycol monomethyl ether), diethyl carbonate, dimethyl carbonate, ethylene carbonate, propylene carbonate, formaldehyde, lactic acid, triethylamine, methyl isobutyl ketone, n-butyl acetate, isopropyl acetate, methyl acetate, acetic anhydride, dimethoxyethanol (ethylene glycol dimethyl ether), propylene glycol, triethylene glycol monomethyl ether, dimethyl isosorbide, cyclopentyl methyl ether, hexamethylphosphoramide, and petroleum ether. In an effort to close these gaps in fundamental thermodynamic data, algorithms have been developed based on functional group additivity methods. These are far more reliable than any computational methods available to deal with missing toxicological parameters. Table 5 summarizes the methods along with the associated uncertainties in the thermodynamic parameter. As with any computational method predictions need to be checked experimentally.


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19 Table 5. Summary of computational methods used to estimate thermodynamic parameters along with percent uncertainties.

Thermodynamic Parameter Method Tm Gani43 Tb Nannoolal44 Tc Joback45,46 Pc Joback45,46 ∆ H(fus) Chickos47 Vetere48 ∆ H(vap) p(vap) Riedel49

% Uncertainty 25 2 1 5 50 4 1, for T > Tb 5 to 30, for T > T(triple pt) Cp(gas) Benson50 4 51-53 Cp(liq) Ruzicka-Domalski 4 Cp(solid) Goodman54 10 acentric factor Edmister55,56 6

Conclusion We have presented two compilations that we hope aspiring and practicing chemists will find useful in their research work in developing sustainable chemical processes and syntheses to known and future chemical products. We have also introduced a material efficiency metric in honour of Antoine Lavoisier’s contribution to modern quantitative green chemistry via his discovery of the law of conservation of mass in chemical reactions and its corollary that properly written chemical equations must be element and mass balanced. These ideas are a mandatory starting point for applying any kind of sustainability metric calculation.

Supporting Information

Alphabetic listing of 260 technical terms used in green chemistry practice: greenchemistry-dictionary.pdf. Tables of maximum Lavoisier numbers for various named organic reactions categorized by reaction types: Lavoisier-numbers-named-organicreactions.pdf. Datasets of life cycle assessment, safety-hazard, and thermodynamic parameters for about 300 high value industrial commodity chemicals: databaseindustrial-chemicals-LCA-parameters.xls; database-industrial-chemicals-safety-hazard-



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20 parameters.xls; database-industrial-chemicals-thermodynamic-parameters.xls. Datasets for solvents (database-solvents.xls).

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Synopsis: A dictionary of 260 technical terms used in sustainability metrics literature and best available datasets for about 300 high value industrial commodity chemicals including 75 solvents are presented. Lavoisier numbers are introduced.


Useful Tools for the Next Quarter Century of Green ... - ACS Publications

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