(206a) Aspects of Good Experimental Practice in Surface Energy Measurements, of Particulate Materials, Using Fd-IGC

Surface energy, arising from the imbalance of the intermolecular forces of the molecules on a surface¹, is gaining momentum as the focus of industry shifts in particles with smaller and smaller dimensions, where surface properties overcome the effects of bulk properties. Traditionally, wettability-based techniques, grounded on Youngâ??s pioneer work on the cohesion of fluids², were employed in surface energy measurements. Nonetheless, the accuracy of these techniques proved to be limited when encountering particulate materials. The anisotropic nature of individual particles gives rise to a surface energy heterogeneity. This heterogeneous behaviour can not be described by single numerical values, but is more appropriately expressed in the form of surface energy distributions³. Inverse Gas Chromatography, an adsorption based technique introduced in mid-70â??s⁴, for surface energy measurements proved to be quite efficient in overcoming the aforementioned limitation. In its finite dilution mode (FD-IGC) it enables

Despite the progress in the field, both from an experimental and a computational perspective, difficulties still exist in the direct comparison of data obtained from different researchers. In this work, the influence of the amount of silanized wool used in the packing of the chromatographic column, the packing structure of the material and the temperature of the measurement were examined using both experimental and computational approaches. The aim was to unveil the mechanism and the conditions under which they influence the measurements and to propose a framework minimizing their effects.

The surface area and surface energy of silanized wool and lactose monohydrate were measured. The dispersive component of the surface energy of wool was determined to be about 35 mJ*m^-2, whereas the acid-base component was determined to be less than 5mJ*m^-2.Computational models^6-8  were used to determine the surface energy distributions of the two materials. Then, lactose monohydrate was packed with known quantities of silanized wool at different ratios. Surface energy measurements were performed in these mixtures and the corresponding surface energy distributions were calculated. The surface energy distributions agree well with the predicted surface energy distributions, obtained from direct combinations of the surface energy distributions of silanized wool and lactose monohydrate. The surface area (about 0.25 m²*g^-1) and surface energy of the silanized wool seem to affect the measurements of lactose monohydrate (with γ^dâ??40 mJ*m^-2), even when the surface area of silanized wool is more than the one tenth of the surface area of lactose monohydrate.

Using computational models, a series of in-silico experiments were performed in order to extend the understanding of the influence of silanized wool on IGC measurements. The results suggest that materials can be distinguished in three major categories. The first category includes very high surface energy materials (γ^d>70 mJ*m^-2). In these materials the effects of silanized wool even in large quantities is negligible. The second category includes the materials with similar surface energy as silanized wool. In this category the effects of silanized wool are masked. The third category includes very low surface energy materials (γ^d<20 mJ*m^-2). For this type of materials even a small amount of silanized wool (about 1%) influences the measurements. It is suggested that special care should be taken for this kind of materials. These findings highlight the importance of complimentary techniques which can provide with a rough idea for the surface energy of the material, in order to help the design of more advanced IGC experiments.

Mixtures of carbamazepine and delta-mannitol, at a 1:1 surface area ratio, were used to determine the effects of powder mixtures packing on IGC measurements. Four different packing structures of the mixtures were measured. The surface energy of all of them was found to be identical. Monte Carlo simulations^5,9performed at different lattice arrangements verify the experimental findings. The experimental and modelling data suggest that packing structure does not influence IGC measurements.

Carbamazepine was, also, used to investigate the effects of temperature on IGC measurements. Using the same column, IGC measurements were performed at four different temperatures. The changes in surface tension and surface area, of the solvent probes, with increasing temperature were taken into account while elucidating the experimental data. The results sow an increase of the measured surface energy. This is in accordance with the theoretical basis provided by the static component of the Lifshitz equation for dispersive interactions. According to this equation there is a possibility of stronger dispersive interactions at increasing temperatures, depending on the dieelectric properties of the material¹⁰.

Overall, this work provided some coherent analysis on parameters influencing IGC measurements, reinforcing the existing rules of good experimental practice. It proved that silanized wool can influence IGC measurements of low surface energy and surface area materials. In addition, it demonstrates that the packing structure of particulate materials does not affect IGC measurements and it, also, highlights the importance of experimental temperature on the measurements. Furthermore, it proved the applicability of computational tools in the field, highlighting their importance when it comes to more advanced types of measurements, including multicomponent mixtures.

Disclosure:

Imperial College London and AbbVie jointly participated in study design, research, data collection, analysis and interpretation of data, writing, reviewing, and approving the publication. Eftychios Hadjittofis is a graduate student at Imperial College London; Jerry Y. Y. Heng is a professor at Imperial College London. They all have no additional conflicts of interest to report. Geoff G. Z. Zhang is an employee of AbbVie and may own AbbVie stock.

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