Experiments for Large Scale Materials Production

This article is part of a 5 article series ‘Scale up to Success’ published by the INSPIRED project.  The series tracks the essential aspects of scale up for nanomaterials in printed electronics, and articles will be published every 2 weeks until the end of 2018. You can read articles published to date through the INSPIRED website.

Design of Experiments in Large Scale Materials Production

The INSPIRED project aimed to help commercialise nano-formulated inks for a variety of applications. The complexity of these formulations and achieving the desired properties for functional applications means that inks can be much harder to scale-up than simpler nanomaterials. Efficient design of experiments (DOE) at every stage of the production process can help optimise  process parameters, identifying the ideal formulation ratios and tailor properties for a given application. A DOE also helps to increase efficiency across the whole production setup, reducing waste and lowering the cost of R&D scale up by minimising the time and materials used.

Many of the inks in the INSPIRED project are destined to be used in printed electronic applications. Therefore, how well the inks conduct and transmit (or resist) electricity are of utmost importance, as are the other formulation characteristics, such as transparency or the ability to print onto a wide variety of substrates. Knowing how to navigate all these different parameters is not easy, especially if trial and error protocols are being employed. A way to efficiently scale up these types of materials is to perform DOEs on the nanomaterial production itself and on the formulation of the nanomaterials into the ink product, alongside extensive characterisation and electrical measurement testing in-line with the different formulation ratios being explored.

Why Should Industry Undertake DOEs?

Think of a DOE as a controlled trial and error. If one process or parameter does not provide a usable outcome, another avenue can be trialled. The difference is that with a DOE, many parameters and process can be tested in one set of experiments, and by keeping certain parameters constant as a form of process control, statistical evaluations can determine the optimal parameters much more quickly and definitely  than trial and error. It also gives definitive answers, and not just an assumption which can sometimes be the case with trial and error.

In the case of ink formulations, there are three key areas where a DOE can be employed – optimisation of the nanomaterial that will be formulated into the ink, optimisation of the ink itself and optimisation of the printing process. All these processes are crucial to the performance of the finished product and one cannot take precedence over the other when scaling up the production capabilities. They also have different requirements in terms of what is considered optimal and so individual process cannot be excluded.

Nanomaterial Optimisation

The first process that should be optimised is the nanomaterial itself. This is generally more straightforward because it only involves tailoring the synthesis of the nanomaterial rather than targeting  multiple combined constituents. DOEs are useful to optimise yield, material purity, degree of functionalisation, changes to the surface chemistry, crystallinity and morphology  and for controlling the degree of agglomeration. Nanomaterials in inks can take many forms, including particles, 1D materials and layered nanomaterials, which also require the optimisation of the particle size and dispersity, aspect ratio and lateral dimensionality.


Formulation Optimisation 

The optimisation of a formulation is less simple, with multiple constituents that need to mix, interact and work in conjunction to perform a desired function. If any of the materials are not combined correctly, or in the right quantities, then the formulation may not fulfil its intended application. Some of the most important properties to control and optimise at this stage include formulation ratio for stability, usability and an ease of deposition, control of formulation dispersion, and optimisation of the specific functional properties of the formulation for the intended application (such as thermal resistance, electrical conductivity and optical transparency for use in electronic and optoelectronic devices).

The above process optimisation protocols require in-process characterisation methods (where possible) to give an accurate and real-time measurement of how optimal the material is during a given run-through. For the parameters that cannot be measured in real-time, samples must be characterised as quickly as possible after the process has finished (before it becomes contaminated or interacts with external environments). From a statistical perspective, there are various parameters (such as the particle size range, ratios and yield) that can be quantitatively measured, and the optimal processes can be deduced around these. However, with complex systems, qualitative data (such as imaging the structure) need to be considered, as both aspects are important and the processes selected should demonstrate the best results over both qualitative and quantitative areas.

Printing Process Optimisation

For most formulations, the DOEs would be finished when an optimal formulation has been produced. However, for inks, there is also a third type of optimisation process that should be considered, and this is the optimisation of the printing process itself.

There are many types of printing, so finding the ideal printing technique for the substrate being printed on is one important aspect. Another priority, once the printing method has been determined, is optimising the process to ensure a uniform deposition onto the intended substrate. At this stage, testing whether the ink adheres to the substrate effectively is another area that can be measured, as can the properties of the inked substrate as a single material. Whilst this acts as more of a quality control procedure to ensure that the previous DOEs have yielded the optimal materials, it is still necessary to prove that the end product is of the desired quality.

Overall, a DOE is useful for the production of any nanomaterial, but it helps to save a significant amount of time and money when the processes are complex, or there are a series of potential pathways that can be explored. Undertaking a DOE helps to identify and remove any inefficient pathways, and by finding the most optimal pathways early on, the likelihood of problems occurring down the production pathway are minimised and can be adressed more easily if complications arise.