Toward greater adoption of more sustainable synthesis methods in research laboratories


Aldrich Chemical (a division of MilliporeSigma, a business of Merck KGaA, Darmstadt, Germany)
6000 N. Teutonia Ave., Milwaukee, WI 53209, USA


An analysis is presented on the sluggish adoption of “greener” synthetic technologies relative to older and more established methods that are regarded as less sustainable. This analysis describes how these more sustainable options experience a significant barrier to adoption by chemists because sustainability falls below confidence, cost, difficulty, availability, and quality in the common decision-making priority. A concept is then presented with examples for how this may be partially overcome by the development of more holistic workflow solutions employing sustainable reagents together with robust procedures and apparatus optimized for the best synthetic outcome and sustainable use of resources. This represents a compelling way to advance adoption of new more sustainable synthetic methods in research labs where new methods achieve adoption and where new chemists develop.

The starting point for any sustainability assessment of a chemical manipulation is Paul Anastas and John Warner’s pioneering 12 principles, definitively published in 1998 (1) (Figure 1).
The robustness of these principles is confirmed by the fact that they remain the standard today, widely adopted and cited without modification. The 12 Principles were clearly devised to prompt chemists to critically examine the chemistry they were doing from 12 different perspectives in order to reveal any opportunities for making it more sustainable.

A quick examination of the 12 Principles reveals some important characteristics. First, the principles encourage a holistic review of a chemical process, not just an assessment of individual reagents. Second, they are presented as guidelines for making choices or seeking substitutions or alternatives; there are no absolutes or commandments. As such, they are not presented as readily-quantified parameters. Third, they seem to be constructed with process-scale optimization very much in mind. At first glance, this makes sense from an opportunity standpoint, as this is where there ...