Characterising heterogeneous catalysts with laser diffraction (TIA)
Heterogeneous catalysts, where the catalyst is in solid form, enhance many of the gas and liquid phase reactions that underpin routine chemical processing.
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Prime examples include Raney catalysts for the hydrogenation of liquid fats, fluid catalytic cracking (FCC) catalysts for hydrocarbon processing and three-way catalysts for the in situ treatment of car exhaust fumes. The extent to which such catalysts enhance reaction rates is directly dependent on the specific surface area they present to the reactants, which for particulate catalysts is a function of particle size – a decrease in particle size results in an inversely proportional increase in surface area, and hence reaction rate. While finer particles may be advantageous from the point of view of reaction potential, there are downsides too: health and safety issues; poor fluidisation properties; and a tendency to agglomerate, which inhibits reaction. It is therefore necessary to balance the need to maximise reaction efficacy with the need to reduce the risks associated with overly fine particle size, which means that there is usually an optimum particle size range for a catalyst.
A common analytical technique for measuring particle size is laser diffraction.1 This is an ensemble particle sizing technique that reports a particle size distribution for the entire sample. Particles illuminated by a laser beam scatter light over a range of angles depending on their size, and give a distinctive scattering pattern from which the particle size distribution can be determined using an appropriate scattering model, ideally Mie theory. For catalyst applications the particle size data generated can also be used to calculate a specific surface area (SSA) by converting the reported volume distribution into a surface area distribution using the Hatch-Choate equations, for example.2 The general equation linking SSA with particle size is:
where D[3,2] is the surface area moment mean or Sauter mean diameter (SMD).
As was touched upon earlier, SSA is a critical metric for defining catalyst activity and achieving an optimum particle size distribution. Traditionally this has been performed using Brunauer-Emmett-Teller (BET) physisorption techniques; however, catalyst specialists are increasingly turning to laser diffraction techniques for faster and more efficient assessment. To this end, laser diffraction particle sizing is finding widespread application for the characterisation and development of catalyst powders.
Case study: using laser diffraction to measure specific surface area
The particle size distributions of three different FCC catalysts were measured using wet dispersion laser diffraction on a Mastersizer 3000 (Malvern Panalytical) using dispersion conditions based on standard wet method development strategies.3
Figure 1 shows particle size results and indicates that all catalyst samples have a median particle size of 60-80 μm. Catalyst B, however, has a markedly narrower particle size distribution compared with A and C, which are relatively similar.
Table 1 shows the calculated SSA values for each catalyst as determined from laser diffraction data. These results indicate that the SSA of catalysts A and C are similar, whereas catalyst B has a much smaller SSA. The higher SSA for samples A and C are attributable to the higher levels of fine material present which is absent from catalyst B.
Figure 2 shows a comparison between the laser diffraction SSA results and analogous data produced using BET techniques. The results show excellent correlation with one another, indicating that laser diffraction can be used in place of BET to predict likely catalytic activity or provide a method for quality control analysis.
1 ISO 13320:2009 Particle size analysis – Laser diffraction methods.
2 Hatch T, Choate S P, Statistical description of the size properties of non-uniform particulate substances, J. Franklin Inst., 207, 369-387,1929.
3 Wet method development for laser diffraction measurements, Malvern Instruments Application Note available for download from
This short case study originally appeared in PTQ's Technology In Action feature - Q4 2018 issue.
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