Direct process exploration & optimization
Broadly, there are two modes of R&D with regard to nanomaterials synthesis. One of which is direct exploration of varying process input parameters upon nanomaterial growth. Such parameters could be input gas composition, flow rates, temperature, catalysts used, etc. The response to these comprise not just growth rate, but uniformity of structures, purity, lattice polymorphs, and nanostructure architecture.
This strategy is about characterizing the process through informed trial & improvement, whereby we aim to seek-out through parameter space the best global conditions to achieve the desired nanomaterial. Some practical process constraints could be: energy efficiency, safety, ease of use – not forgetting the degree of controllability of the process as well. An example would be to measure in situ the growth rate of vertically-aligned carbon nanotubes using an optical micrometer and observing how growth responds to the partial pressure of ethylene supplied to the CVD reactor.
Ex situ materials characterization
Although there is capability to measure certain variables in situ during synthesis – such as growth rate – many aspects of nanomaterials rely on ex situ materials characterization post synthesis. The figure above shows a range of scanning electron microscope (SEM) images of ZnO nanostructures that originate from placing a zinc probe in different regions of a counterflow diffusion flame. This demonstrates how the morphology of nano-ZnO formed depends very sensitively on local compositions – as illustrated above by the profiles within the flame.
Indirect exploration via modelling
An alternative strategy is through indirect exploration of varying process parameters upon nanomaterial growth. This works by gathering a suite of experimental data, such as the abovementioned growth rates and nanomaterial size/shapes from SEM, and then feeding such datasets into models. The data help to define the model’s parameters within the predetermined range of experimental conditions. Ultimately, the model will then be used to predict the nanomaterial properties and growth rates from preset process conditions. This constitutes an indirect method, since the modelling serves as an intermediate step between input variables and the observed (and desired) nanomaterial formed.
Moreover, owing to the inherent complexity of the microscopic processes that occur during bottom-up synthesis of nanomaterials, it is necessary to gather as much relevant experimental data to constrain models tightly to ensure maximum accuracy. In this regard, more exotic intermediate variables can also be used to assist model development. An example is the measurement of SiO vapour in flames where silica (SiO2) nanoparticles are being formed. This can be done by deploying laser induced fluorescence (LIF) to precisely measure how the SiO intermediate is spatially distributed throughout the flame. In the case of modelling growth rates of the nanomaterial, detailed reaction mechanisms form a pivotal part of the overall model, and therefore require more comprehensive species composition datasets.
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