This review covers recent applications of near infrared (NIR) spectroscopy in the determination of physico-chemical and morphological parameters of polymeric materials. Near infrared measurements in the diffuse reflection mode are highlighted, which analyse the structural parameters such as porosity, surface area and particle size. Fundamentals and applications of the technique are discussed and examples of quantitative and qualitative analysis are explained. Various approaches like on-and in-line techniques, bulk measurements and kinetic studies for recording spectra are discussed. Furthermore, this review addresses the development of calibrations, which allow for the differentiation and quantification of materials with varying physical and morphological properties. Parameters like constitution, composition and crystallinity have a strong affect on the material characteristics. Therefore, chemical, physical and mechanical properties of synthetic as well as natural substances, such as polymeric composites and cotton or wool, need to be studied in-depth. To sum up, NIR spectroscopy has been developed as a flexible, robust and high-throughput analytical method that can be combined with chemometric and multivariate data analysis for fast and reliable screening in material science.

The use of non-invasive methods for detecting biomarkers opens a new era in patient care, since clinical investigators have long been searching for accurate and reproducible measurements of putative biomarkers. There are many factors which make this research challenging, beginning with lack of standardization of sample collection and continuing through the entire analytical procedure. Among the variety of methods so far used for biomarker screening, capillary electrophoresis represents a robust, reliable, and widely used analytical tool. This review will focus on recent applications of CE to the analysis of body fluids and tissues for identification of biomarkers.

A method based on near-infrared spectroscopy (NIRS) was developed for the rapid and non-destructive determination and quantification of solid and dissolved amino acids. The statistical results obtained after optimisation of measurement conditions were evaluated on the basis of statistical parameters, Q-value (quality of calibrations), R(2), standard error of estimation (SEE), standard error of prediction (SEP), BIAS applying cluster and different multivariate analytical procedures. Experimental optimisation comprised the selection of the highest suitable optical thin-layer (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 mm), sample temperature (10-30 degrees C), measurement option (light fibre, 0.5 mm optical thin-layer; boiling point tube; different types of cuvettes) and sample concentration in the range between 100 and 500 ppm. Applying the optimised conditions and a 115-QS Suprasil cuvette (V = 400 microl), the established qualitative model enabled to distinguish between different dissolved amino acids with a Q-value of 0.9555. Solid amino acids were investigated in the transflectance mode, allowing to differentiate them with a Q-value of 0.9155. For the qualitative and quantitative analysis of amino acids in complex matrices NIRS was established as a detection system directly onto the plate after prior separation on cellulose based thin-layer chromatography (TLC) sheets employing n-butanol, acetic acid and distilled water at a ratio of 8:4:2 (v/v/v) as an optimised mobile phase. Due to the prior separation step, the established calibration curve was found to be more stable than the one calculated from the dissolved amino acids. The found lower limit of detection was 0.01 mg/ml. Finally, this optimised TLC-NIRS method was successfully applied for the qualitative and quantitative analysis of L-lysine in apple juice. NIRS is shown not only to offer a fast, non-destructive detection tool but also to provide an easy-to-use alternative to more complicated detection methods such as mass spectrometry (MS) for qualitative and quantitative TLC analysis of amino acids in crude samples.

No abstract

In the bioanalytical era, novel nano-materials for the selective extraction, pre-concentration and purification of biomolecules prior to analysis are vital. Their application as affinity binding in this regard is needed to be authentic. We report here the comparative application of derivatised materials and surfaces on the basis of nano-crystalline diamond, carbon nanotubes and fullerenes for the analysis of marker peptides and proteins by material enhanced laser desorption ionisation mass spectrometry MELDI-MS. In this particular work, the emphasis is placed on the derivatization, termed as immobilised metal affinity chromatography (IMAC), with three different support materials, to show the effectiveness of MELDI technique. For the physicochemical characterisation of the phases, near infrared reflectance spectroscopy (NIRS) is used, which is a well-established method within the analytical chemistry, covering a wide range of applications. NIRS enables differentiation between silica materials and different fullerenes derivatives, in a 3-dimensional factor-plot, depending on their derivatizations and physical characteristics. The method offers a physicochemical quantitative description in the nano-scale level of particle size, specific surface area, pore diameter, pore porosity, pore volume and total porosity with high linearity and improved precision. The measurement takes only a few seconds while high sample throughput is guaranteed.

This article evaluates the applicability of near infrared (NIR) reflection spectroscopy for the physico-chemical and morphological characterisation of matter on a scale smaller than 1 micrometre (normally between 1 and 100 nanometres). The investigated materials comprise porous and non-porous silica particles, carbon based materials such as C 60 fullerenes, nano-crystalline diamond (NCD) and dendrimers, all of them having diameters and/or pore sizes in the nanometre range, respectively. In case of the silica packings and differently derivatised C 60 fullerenes, absorbance signals could be clearly assigned to corresponding surface modifications. Identification or classification of the material can be achieved successfully by principal component analysis. Nano crystalline diamond surfaces, whether H-or O-terminated, could be differentiated by a computed partial least squares (PLS) regression model with around 80% precision. Generations 0-7 of poly(amidoamine) (PAMAM) dendrimers with functionalised surface amine groups are characterised in respect of particle diameter and molecular weight. The established PLS models showed a standard error of prediction of only 0.43 nm and 12.30 kDa, respectively. NIR spectroscopy has developed as a flexible analytical method that can be utilized for fast, reliable and highly reproducible screening of matter even in the nanometer range.

No abstract

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 m-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 R 2 > 0.98, BIAS < 1.26 10 -14 . Beside these advantages, our introduced NIRS approach allows physicochemical characterisation with high precision, output and performance.