Okay. Hello, everyone. Today, I'm going to go through our contribution to the Discovery Summit. The title is Utilizing Design of Experiments and the Prediction Profiler for Creating Sustainable Formulations. But first at all, who are we? My name is Pilar. I'm a research scientist at the Speciality Chemical Company, Johnson Matthey, and I'm the lead of a group called Statistical Thinking Team, which aim is to extend the use of statistical tools around Johnson Matthey. Here I have my colleagues, Patricia and Jenny, they are going to introduce themselves.

Hello, I'm Patricia Blanco. I also work at Johnson Matthey. I'm based at the research center. I am a chemist by background, and I've been working in the field of coding and formulations for about 20 years.

Hello, I'm Jenny. I work at the Technology Center, and I work on LCA within the sustainability team, helping scientists to make sustainable decisions during their research projects.

For those that don't know, Johnson Matthey, I'm going to introduce a little bit. As you know, the world is facing some of its biggest challenges yet, and we need to change, to transition to net zero and ensure a more sustainability future. That's where Johnson Matthey comes in. Through our inspiring science and continued innovation, we are catalyzing the net zero transition for millions of people every day. We have a 206-year history and over 12,000 employees all around the world. Our technology is based on advanced metals, chemistry, catalysis, and process engineer. These expertise have been established over many years and underpins all our businesses. In JM, we are committed to sustainability, and Jenny is going to tell us all about it.

One of our core values at Johnson Matthey is to protect the planet and the people. The way that we're looking at achieving this is by looking at the way that we develop our portfolio of technologies, about the operations that we include to produce those technologies, and how we can work with customers as well as suppliers to create a sustainable product within our portfolio. We're looking at protecting the climate. We want to protect nature and advance the circular economy, and we're looking at promoting a safe, diverse, and equitable society. We're using ESG ratings to show that we're tracking how we are becoming a more sustainable company.

Thank you. Today, we are going to talk about how we use Design of Experiments and sustainability. For us, Design of Experiments has an intrinsic contribution to sustainability since just by reducing the number of experiments, you are saving resources, materials, and energy. Also, if you're applying Design of Experiments, for example, to make one of your processes more efficient, then you are making less weight and using less energy. If you are applying your design of experiment to make it a more efficient product, then you might be saving resources in the application of that product, or you might be increasing their lifetime and therefore decreasing the amount of waste. All these applications of DoE to sustainability are more directed through the application of the products. In this presentation, we wanted to go also further and talk about DoE and sustainability through the formulation of that product. Going backwards before that product even exists. Formulations in JM.

Product formulation is one of JM's core capabilities, and it requires a multidisciplinary approach where chemistry, modeling, manufacturing, processing, and engineer need to work together to be able to deliver JM science in a form that is suited to the customer's needs. As I said in the introduction, I'm part of the Formulations team at JM's Technology Center in the UK. As a team, we provide support to all of the JM businesses in a range of projects that go from fundamental long term understanding to fast critical support for product manufacturing. We also build and extend our science base by developing new techniques and skills in our labs, but also collaborating with external experts.

We use statistical tools to design our experiments and to get the most value out of our data. Most of JM's products are formulated, and I'm just showing a few pictures here as examples. Starting from the left, we have catalytic converters that they are coated with alizarin that contains an active catalyst. The black picture in the middle is a fuel cell ink, both fuel cell and electrolyzer catalysts are deposited to produce coated layers from inks. Then powders are really important as well because they are used to produce granulated or pelletized catalysts that they are used in the production of chemicals.

In all of these examples, it's crucial to understand the materials and how they interact between each other or how they interact with each other. Formulations are complex systems, and using JMP tools like Mixture Design of Experiments are important, and they help us extract the most value and understanding when studying formulating products.

In this demonstration that we are going to do. We have run a Mixture DOE on a formulation and looking at performance of the formulation that will be introduced in the Profiler in JMP. But also we will introduce a model related to the sustainability based on life cycle analysis concept. Both of them were looking at it together in a Profiler to obtain an optimized formulation that is not only taking into account the performance, but also the sustainability of the formulation. If you haven't heard much about lifecycle analysis before, Jenny will go through it.

The lifecycle analysis is a method we can use to quantify any direct or indirect environmental impacts that may be associated across a full production chain. So that's from Cradle-to-Gate or Cradle-to-Grave. What LCA is doing, is it's helping to promote this design of any sustainable products or processes, and identifying any red flags that may appear, so we can do something about them sooner. This will lead to us having less environmental impacts, lower toxic materials released into the environment. LCA specifically will focus on three major targets. This is human health, ecosystem health, and resource availability. What we're doing at the moment is we're focusing on the carbon dioxide equivalent so that we can look at the carbon footprint of products from Cradle-to-Gate with Pilar in DOE.

I'm going to leave here. I'm going to do a demonstration directly in JMP. We have then a mixture of three components. A main component, a modifier, and a solvent. Let me open the ternary plot. See how it looks. It's the blue. This is our ternary plot, within the main component, modifier, solvent. Obviously, system formulations are a lot more complex like that, but we have done this little example with just three, so they can easily be visualized in the ternary plot. Normally very typical in formulations, the experimental space is very small compared to all the possible combinations.

We normally need to assume in a very small space. But actually, although it looks like a very small space, like very drastic kind of... The formulations can have very small changes in this formulation within this space can cause very drastic behavior in terms of performance. We designed a mixture design, in this sketch based on a [inaudible 00:09:50] filling designs with constraints, as you can see, in which it gives us a good distribution of the data points around the experimental space. The data was model. The data that we are collecting is some kind of property related with the performance that you can see, and the data was model.

We have now a model of how different formulations are giving us different performance properties. At the same time, we also have a model for the carbon footprint, the kilograms of CO₂ equivalent produced by kilogram of formulation taking into account the carbon footprint of each individual ingredient. We are going to visualize each of these models in a ternary plot. If we start with the carbon footprint, for example, we can see here I've done a zoom over the experimental space. The experimental space is in white. You can see here through the contour lines in red how the carbon footprint increases is going towards the left.

If we want to minimize the carbon footprint of this formulation of this product, we need to go… Moving towards the right in this direction. We also have… If we look at the model of the performance predictor, then you can see how the model is more complex, and you can see how the values move between 20 and 60. You can see how there is the 20, the 30. We do have as well the areas for the 40 on the dotted lines. If we want to have a performance property of 40, then we need to work there.

We also have the 50. If we want to work around 50, we need formulations that are around this area, and we also have a sweet spot of 60. Say we are looking at maximizing this property, then we need to choose formulations that are within these spaces, especially we will be then interested in this area for the formulations. If we look at it all together, then we have the contour lines for the performance property in red and the carbon footprint in blue. By seeing it all together, it allows us to choose then formulations in which we could be as closer within the area of interest to maximize the performance property, but also trying to minimize the carbon footprint.

We will probably choose a formulation that is in the right side of this area to be as closer as possible to lower values of carbon footprint. This is obviously just visually, and the visualization gets more complicated when you have more components. But anyway, you can do all through the Mixture Profiler, but you can also use other tools in JMP as the Prediction Profiler or the Design Space Profiler to help you to optimize the system, taking into account different outputs as it is in this case with the performance, but also with the carbon footprint, and find a solution that could compromise between the two aspects that we are looking into.

We are looking into a formulation of a product. In summary, the importance… There is a high importance of providing tools to a scientist to be able to consider sustainability and early stage research. If not, these decisions are very difficult to be made. JMP and with the sign of experiments and the profilers have proved for us a very useful tool for the scientists to be able to optimize the performance, the sustainability, and other many aspects we can consider in a product or a formulation, such as could be the cost, for example, in a simultaneous way to obtain the best solution all over. This is all for us. Thank you very much for your attention.