The Open Analytical Chemistry Journal

2007, 1 : 21-27
Published online 2007 November 2. DOI: 10.2174/1874065000701010021
Publisher ID: TOACJ-1-21

Progress in Silica Chemistry – Determination of Physico-Chemical Parameters via Near-Infrared Diffuse Reflection Spectroscopy

C.W. Huck , N. Heigl , M. Najam-ul-Haq , M. Rainer , R.M. Vallant and G.K. Bonn
Institute of Analytical Chemistry and Radiochemistry, Leopold-Franzens University, Innrain 52a, 6020 Innsbruck, Austria.

ABSTRACT

In analytical chemistry, silica gel plays a pre-dominant role in separation science. It is the most important stationary phase in chromatography and electrophoresis. Separation efficiency is directly dependent on the quality and physical properties of the chromatographic bed. Therefore, methods for the physicochemical characterisation of silica stationary phases have been developed over the past decades to fulfil the necessity of pattern control: Brunnauer Emmet Teller (BET) for determination of surface area, mercury intrusion porosimetry (MIP) and size exclusion chromatography (SEC) for pore-size measurement and light-scattering (LS) to evaluate the particle size. Beside that these methods are elaborate and time-consuming and the use of MIP is awkward due to the necessity to ply with poisonous mercury.

Therefore, we introduce near-infrared reflection spectroscopy (NIRS) in the fibre-optics mode for a fast (few seconds), easy to handle and highly reproducible new analytical technique to characterise surface area, particle size in µm- and porosity in lower nm- range. This new analytical NIRS tool is suitable for high sample throughput and therefore aims at high interests in the nano-field. Determination of particle size, porosity and surface area are achieved with a linear correlation coefficient R2 > 0.98, BIAS < 1.26 × 10-14. Beside these advantages, our introduced NIRS approach allows physicochemical characterisation with high precision, output and performance.

Keywords:

Near infrared spectroscopy, reflection, chemometrics, multivariate data analysis.