Sustainable Analytical Methods: The Challenge Is Assessing Methods Before and After Improvements (Part 2)

This second article in the series reviews three analytical greenness assessment methods: the analytical method greenness score (AMGS), the analytical GREEnness metric approach (AGREE), and the sample preparation metric of sustainability (SPMS). This article continues the series' goal of introducing sustainability metrics developed over the past 10–15 years that have the potential to become standard tools for evaluating analytical methods. The aim is to encourage the inclusion of a sustainability assessment in all published analytical methods by adopting a single, widely accepted measurement system. A uniform approach would enable consistent comparison of analytical procedures and support ongoing improvements. This article continues the evaluation of these sustainability measurement systems.

Analytical Method Greenness Score (AMGS)

Developed and driven by the American Chemical Society’s Green Chemistry Institute Pharmaceutical Roundtable participants,^1,2 this technique contains the analytical method volume index (AMVI), which incorporates method choice and subsequent solvent consumption in its calculation.³ The analytical method greenness score (AMGS) also includes the impact of solvent environmental toxicity (SHE) by including an environmental assessment tool (EAT). In addition, the energy required in the manufacture of the solvents used in the methods (solvent energy demand), instrument energy consumption, and waste production are integrated into the calculation, thereby extending the evaluation to consider the solvent’s cumulative energy demand (CED). This tool provides a single, concise method that enumerates the contributions from each area, specifically, waste, environmental safety, and energy consumption.

Equation 1 and Table 1 show how to determine the AMGS:

The beauty of this tool is not only that it includes fourteen well-used liquid chromatography (LC) solvents along with water for reverse phase HPLC (RP-HPLC) and carbon dioxide for supercritical fluid chromatography (SFC), but it also includes the average measured energy consumption value for common instrumentation. The Sn, Hn, and En values are all integrated into the tool based on current index scores for each. The user enters method information, i.e., gradient parameters, run times, and flow rates.

Solvents and instruments are chosen from a drop-down list, which includes high-performance liquid chromatography (HPLC), ultra-high-performance liquid chromatography (UHPLC), supercritical fluid chromatography (SFC) as well as these instruments coupled with mass spectrometry (MS) systems; preparative-scale instruments are also included. The output of the tool is a numerical greenness score, however, built into the tool is also the contributing factor values that make up the overall final AMGS. A color code shows the major contributor to the AMGS score with yellow and red being used to highlight areas where the method could be improved.

As an example, in just a few minutes, the following parameters were entered into the AMGS calculator: An 8-min HPLC method with a flow rate of 1.0 mL/min and gradient conditions using acetonitrile and water as both the mobile phase and the sample and standard diluent. The gradient was isocratic for 5 min at a ratio of 30:70 Acetonitrile:unbuffered water, at 6 min the gradient was brought to 90:10 ACN:H₂O and held until 7 min, when the gradient was then returned to the original composition by 8 min of 30:70 ACN: H₂O. One sample was analyzed in duplicate; one blank was run, and seven standards were also prepped in duplicate, the first standard was prepped in 100 mL of 30:70 ACN:H₂O all the remaining were subsequent dilutions into 10 mL of solvent. The following AMGS score of 352 was obtained (Figure 1a), with the largest contributing factor being the solvent energy score, followed by the instrument energy score and the greenest contribution being the Solvent EHS score (shown in green). Figure 1b shows the AMGS score reduction when UHPLC is the technique of choice, and the flow rate is dropped from 1mL/min to 100 µL/min, a decrease of a factor of 10 yields an AMGS greenness score of 226, where all factors are considered green. Finally, a change in solvent choice to the more environmentally friendly methanol reduced the AMGS score by one-third again to a total two-thirds reduction in the overall Greenness score, showing the ease of adjustments that can be made to the entries of the calculator to evaluate the effects of minor changes in the experimental parameters. (As an aside, I was able to do the data entry and make the changes within the calculator to show the example in Figures 1a, 1b, and 1c in less than 10 min).

This tool is easy to use and accessible on the ACS’s website, and it is an approach to aid in developing more sustainable methods. It is a simple quantitative way to show how method choices impact sustainability. Because the system uses impact from three major areas: instrument selection using built in energy consumption values, solvent selection by using cumulative energy demand, and solvent consumption (waste generation) via environmental health and safety a complete assessment of a separation method’s greenness can be determined.

Analytical GREEnness Metric Approach (AGREE)

Based on the 12 principles of green analytical chemistry (GAC) overviewed in Part 1 (also called the SIGNIFICANCE principles), the analytical GREEnness calculator is an assessment approach that generates a number from 0 to 1, 1 being a perfect greenness score. The result also includes a pictogram indicating the final score and the performance of the analytical method for each of the 12 GAC criteria. Figure 2 shows an example result of the assessment that truly met the developers’ goals. In it, each of the twelve input variables is transformed into a common scale in the 0 to 1 range and the output is a clock-like graph with the overall score and color of the assessment in the center. The example pictogram shows an assessment value of 0.57 as the overall score, a deep yellow score that shows room for improvement. This is the weighted average of the criteria scores following equation 2.

The color codes range from red and orange(undesirable), to yellow and green (more desirable), with shades of each color exhibited. The darker the color shade, the more favorable or less favorable it is. Each of the twelve principles is reflected in the final output; each principle, labeled 1 through 12, is color-shaded to show its individual contribution to the final numerical result. The software is straightforward, open-source, and can be downloaded from https://mostwiedzy.pl/AGREE. The calculation was developed in Python 3.7 using the Tkinter module.

The inventors of this assessment device rightfully explain that greenness is not easily defined or assessed, and as such, the system of assessment should include numerous aspects. They continue to explain that, to fully characterize greenness, input criteria should define both the quality and quantity of material requirements, waste generation, energy consumption, the analyst’s safety, and the general approach to the analytical procedure. This general approach is defined to include the number of pretreatment steps and the location of the analytical measurement device in relation to the object of the investigation. The input into the assessment tool can thus be a binary, i.e. yes or no, a discrete individual number, or a continuous function. In addition to the difference in the input values for individual criteria, not all have equal levels of importance. Simplicity might be essential for some of the 12 principles, while others may be primarily concerned with minimizing reagents and waste generation. Having the desire to have an easily readable and discernable output for the tool is diametrically opposed to the need to include a wide range of assessment variables. Table 2 shows a high-level explanation of how the twelve GAC principles have been converted into scores. For the overall final score in the pictogram and the score for the individual principles, the closer the resultant value is to “1”, the greener the analytical measurement technique is with this evaluation tool.

In the metrics review, published by Sajid and Plotka-Wasylka in 2022,⁵ the main criticism of AGREE was its lack of consideration of the synthesis part of an analytical measurement, i.e. the prework or before sample preparation.^5-7 Additional criticism was that the results do not always include information on the structure of the hazards. Both points are not clearly addressed in the other analytical metric systems.

Sample Preparation Metric of Sustainability (SPMS)

The Spanish authors and inventors of the sample preparation metric of sustainability (SPMS) assessment tool still see the need for a system that can evaluate the greenness sample preparation techniques alone. The SPMS is a relatively new (2023) technique that exclusively evaluates extraction and sample preparation without considering sampling or analytical instrumentation.⁸ Touted as a complementary tool for current green metrics, it gives a comparison of sample preparation techniques. It is based on the ten principles of green sample preparation (GSP) from Lopez-Lorente and colleagues.⁹ These principles are guidance for developing sustainable sample preparation procedures following the format of the twelve green analytical chemistry procedures (GAC), which the other metric techniques for the whole analytical procedure follow. The SPMS uses the assessment of nine parameters, which are typically used when extraction is performed. The parameters are divided into four categories according to the type of information that they provide. The categories are: first, sample information including sample amount (volume or weight); second, extraction information including amount, nature, and reusability of the extraction solvent; third, sample preparation procedure information comprised of number of steps, extraction time, need for additional steps after extraction, and sample throughput; and the final category, energy consumption and waste.

Table 3, taken directly from reference 8, presents a clear grid of the assessed parameters and criteria for assigning numerical scores according to SPMS. The setup of the resulting clock diagram is shown in Figure 3. Each parameter is represented by a small colored square, marked with a star if the extractant can be reused. The method’s total score is represented by a large square in the center. The small square is assigned a number according to their appearance in the diagram and are grouped into blocks by the categories outlined above that they belong to. The individual score for each parameter is not shown in the output “clock” for simplification, but the small squares are colored as shown in the bar code of Figure 3.

The colors represent red, orange, yellow, and green, meaning inadequate, tolerable, acceptable, and successful, respectively. The supplementary materials for the reference provide an Excel file that can be used to generate the proposed clock diagram if the values for the parameters established in Table 3 are introduced. Resultant examples of the output “clock” can be found in Figures 4a and 4b; Figure 4a shows a very high favorable score for a direct liquid-liquid microextraction (DLLME) technique for liquid samples. Figure 4b shows an inadequate score for solid phase extraction (SPE) with commercial cartridges.

The SPMS metric technique has novelty features that have not been presented in some of the other metrics. For example, it considers the extraction time of each step, and not only the number of steps or samples per time, but it also places a weight on the actual extraction solvent. SPMS presents an easy visualization of the sustainability of sample preparation, accounting for a wide variety of parameters. It clearly delineates the influence of micro-techniques and provides a complementary technique to many of the other presented metrics.

Summary and Series Completion

The overviews of the AMGS, AGREE, and the SPMS are concluded with this second section of the series.

As stated previously, the intent of this series is to gather the opinions of analytical, chromatography, and sample preparation experts on the optimal technique for evaluating our own methods. Towards that end, we will conclude the next article in the series with a summary comparison of the techniques presented. Subsequently, we will solicit your opinion on which technique you would prefer to use to evaluate the literature methodology. The overall objective is to evaluate our methods for their greenness as we publish and use them consistently, with the expectation that the scoring system will be used universally and serve our community.

References

1. Hicks, M.B.; Farrell, W.; Aurigemma, C. et al. Making the Move Towards Modernized Greener Separations: Introduction of the Analytical Method Greenness Score (AGMS) Calculator. Green Chem. 2019, 21 (7), 1816–1826. DOI: 10.1039/c8gc03875a
2. Power, F.; Ferguson, P.; Herbert, A. Utilization of Analytical Method Greenness Score to Drive Sustainable Chromatographic Method Development. Green Chem. 2025, 27, 14088-14100. DOI: 10.1039/d5gc01574j
3. Hartman, R.; Helmy, R.; Al-Sayah, M. et al. Analytical Method Volume Intensity (AMVI): A Green Chemistry Metric for HPLC Methodology in the Pharmaceutical Industry. Green Chem. 2011, 13, 934-939. DOI: 10.1039/c0gc00524j
4. Pena-Pereira, F.; Wojnowski, W.; Tobiszewski, M. AGREE—Analytical GREEnness Metric Approach and Software. Anal. Chem. 2020, 92, 10076–10082. DOI: 10.1021/acs.analchem.0c01887
5. Sajid, M.; Plotka-Wasylka, J. Green Analytical Chemistry Metrics: A Review. Talanta 2022, 238, 123046. DOI: 10.1016/j.talanta.2021.123046
6. Pena-Pereira, F.; Tobiszewski, M.; Wojnowski, W. et al. A Tutorial on AGREEprep an Analytical Greenness Metric for Sample Preparation. Advances in Sample Preparation, 2022, 3, 100025. DOI: 10.1016/j.sampre.2022.100025
7. Garrigues, S.; Esteve-Turrillas, F. A.; de la Guardia, M. Greening the Wastes. Curr. Opin. Green Sustain. Chem. 2019, 19, 24–29. DOI: 10.1016/j.cogsc.2019.04.002
8. Gonzalez-Martini, R.; Gutierrez-Serpa, A.; Pino, V. et al. A Tool to Assess Analytical Sample Preparation Procedures: Sample Preparation Metric of Sustainability, J. Chromatogr. A. 2023,1707, 464291. DOI: 10.1016/j.chroma.2023.464291
9. Lopez-Lorente, A.I.; Pena-Pereira, F.; Pedersen-Bjergaard, S. et al. The Ten Principles of Green Sample Preparation. TrAC Trends Anal. Chem. 2022, 148, 116530. DOI: 10.1016/j.trac.2022.116530