| Abstract Scope |
In today’s world, when we think about saving the planet, we are likely thinking about reducing ‘carbon footprint’ and the negative effects of climate change. While these are important goals, within the field of materials science and engineering, we need to also think about what I will refer to here as the ‘chemical footprint’. This chemical footprint arises from the consumption of goods, which require the production of materials, which requires the use of resources such as minerals, energy, and water, and can lead to emissions of toxic substances into the air, water, and soil. Consequences of this chemical footprint include increased exposure to humans, leading to cancer and other diseases, and increased damage to our environment. As experts in materials science and engineering, it is imperative that we not only strive for enhanced materials performance, thereby enabling technological development, but also endeavor to purposefully reduce the negative consequences of materials selection, design, and discovery. This multi-attribute objective function requires that we simultaneously address performance, economics, chemical safety (toxicity), energy demand, and materials circularity (waste). A rapidly evolving suite of decision tools and databases, including the strategic application of artificial intelligence and machine learning, can facilitate these essential sustainability-informed decisions. |