From Materials to Products

Across scales and domains: examining the foundations of sustainability

In my previous articles, I’ve discussed how important holistic understanding is for sustainable innovation via different perspectives. Fail to establish this understanding, and you may end up pursuing ideas that seem great in short-term, localized contexts, but actually have negative consequences when seen in a wider context, or over a longer timeframe.

So far, I’ve explored how these challenges manifest horizontally – how actions taken at one part of a value chain can have unexpected consequences at another. Now, I’d like to shift our perspective and look at how these challenges manifest vertically – how changes made at the microscopic scale can impact higher, more abstracted levels; the proverbial butterfly effect.

And there’s no better place to start on this micro-to-macro exploration of sustainability than?with materials. They are, quite literally, the fundamental building blocks of our world – and also the site of some of our most ambitious attempts to create a more livable sustainable future.

From the smallest beginnings

Materials science has incredible significance for sustainable innovation. That can be in the form of new materials: carbon nanotubes, for example, are being used to build solar photovoltaic cells that generate power at up to 70% lower cost than current solar power technologies. Or, it can be in the form of getting smarter about existing materials: for example, reformulation can help us reduce our reliance on the planet’s natural resources –?a critical issue when you consider that achieving #NetZero may entail a sixfold growth in some of our mineral demands by 2040 according to the International Energy Agency (IEA) .

However, whether you’re discovering new materials or reformulating old ones, the technical challenges are substantial. When you’re operating in the realm of the very tiny, a difference in scale can mean transitioning between levels where entirely different rules of physics apply, which need to be modelled, integrated, and turned into actionable data points for teams who may not know the science at each scale.

Let’s say we’re a consumer goods manufacturer, and want to design a new polymer for packaging our products . First, we need to know how it will perform, and find out, say, its strength, or its biodegradability. Because neither “strength” nor “biodegradability” are unitary properties, getting a coherent answer demands running different simulations –?perhaps accounting for the effects of quantum mechanics at the nanoscale, but classical mechanics analysis at the macro scale –?to see how structures at different levels combine to affect overall product performance.

But businesses have needs beyond the lab. So, in addition to evaluating these physical properties, they also need to determine how the new polymer will impact operations at a commercial level. For example, will this new material be compliant with regulations in target markets? What will its resource and emissions footprint be? How will this affect manufacturing costs, and how will those costs in turn impact customers and suppliers?

Answering these questions requires, then, solutions that can not only run purely physical tests,?but also requires the capacity to translate the scientific data generated into information that can be used to make informed commercial decisions about whether or not using this new material in products makes business sense.

Putting it all together

I believe this is one of the great benefits of virtual twins: letting us tie together multi-scale, multi-physics simulations so that we get both a holistic understanding of a material’s properties, and how those properties affect end-product performance, especially in the context of the business. And this is true once you take your materials out of the lab and start integrating them into actual products.

Let’s take batteries as another example. Batteries are a key strategic technology in global sustainability efforts, helping provide the grid-scale storage needed to support renewable energy, and powering the electric vehicle (EV) revolution .

To support this growing importance, battery makers must maintain an incredible pace of material innovation, where industrial chemists are continually tweaking the composition of electrolyte formulas to deliver batteries that are safer, longer-lasting, higher-performing, and more sustainable. For example, replacing lithium with sodium may result in batteries that are not only less of a fire risk, but also utilize more abundant materials.

However, translating these theoretical properties into an actual product can be complicated. A chemist may have developed an electrolyte formula that works well at the level of a single cell, but this formula may perform differently when it is integrated into a whole battery system, or fitted into an EV – whose designers face a constant trade-off between passenger comfort, EV range, and battery cost. For teams that are working at the level of the whole product, this is the kind of information they need.

Enabling the bigger picture

In order to deliver effective innovations, chemists, engineers, and other specialists need to find ways of speaking each other’s language. In the words of @David Epstein, author of the book Range: Why Generalists Triumph in a Specialized World, “Relying upon experience from a single domain is not only limiting; it can be disastrous.”

Formerly, the only way to see the result of the chemists’ and engineers’ work in practice was to run a physical test. Today, simulation capabilities can do that task, and more – integrating the findings into a single source of truth, and translating scientific insights in a way that allows each stakeholder group to effectively pursue its goals.

And this applies whether you are communicating between different nodes on a supply chain or –?as we have just explored – communicating findings between different scalar domains of expertise. Only by integrating this data in a single location will an accurate picture of performance be generated.

As our mastery of the world of science and technology deepens, these digital solutions will be critical for maintaining a holistic understanding of our discoveries and ensuring that innovation continues to be practical and sustainable.