Solid-state NMR studies of inclusion compounds

The work contained within this thesis is a study of inclusion compounds using solid-state NMR. Such compounds typically exhibit static and/or dynamic disorder, which precludes the use of diffraction-based techniques to obtain detailed structural information. Hence, due to its ability to probe local environments, solid- state NMR can be used to provide information which would otherwise be inaccessible. However, the dynamic nature of the guest molecules within inclusion compounds can yield unusual results for routinely applied experiments, such as cross polarisation, heteronuclear dipolar decoupling and dipolar dephasing. Therefore, some of the more fundamental aspects of solid-state NMR have first been explored. The inclusion compounds of particular interest are those which contain urea or thiourea as the host species. The ordering of guest molecules and host dynamics have been investigated via both one- and two-dimensional (^13)C and (^1)H NMR experiments for the 2-hydroxyalkane/urea inclusion compounds. For the 1-fluorotetradecane/urea inclusion compound, an approach involving a combination of (^1)H→(^13)C and (^19)F→(^13)C cross-polarisation experiments, with both single-channel (^H) and double-channel decoupling ((^1)H,(^19)F) has been devised to assign (^13)C resonances and hence deduce guest ordering. Steady-state and transient (^19)F MAS NOE experiments have been used to probe the dynamics of the 1-fluorotetradecane/urea inclusion compound. Using the considerable sensitivity advantage of (^19)F NMR, over that of (^13)C, a detailed study of the conformational dynamics exhibited by fluorocyclohexane molecules included within thiourea has been performed via bandshape analysis, selective polarisation inversion and 2D exchange experiments. Intermolecular distance measurements have been determined for adjacent fluoroalkane molecules within urea tunnels using a series of static (^19)F NMR experiments. From the results obtained, conclusions regarding the mutual orientations of adjacent end-groups in such compounds have been made

Classifying green teas with near infrared hyperspectral imaging

Tea products analysis is currently limited to high-end analytical techniques such as high-performance liquid chromatography, gas chromatography and isotope analysis. However, these techniques are time-consuming, expensive, destructive and require trained experts to perform the experiments. In the present work, an application of near infrared hyperspectral imaging for the classification of similarly appearing green tea products is demonstrated. The tea products were classified based on their origin utilising a support vector machine classifier. Results showed good accuracy (96.36 ± 0.17%) for the classification of green tea products from seven different countries of origin

Validity of particle size analysis techniques for measurement of the attrition that occurs during vacuum agitated powder drying of needle-shaped particles

Analysis of needle-shaped particles of cellobiose octaacetate (COA) obtained from vacuum agitated drying experiments was performed using three particle size analysis techniques: laser diffraction (LD), focused beam reflectance measurements (FBRM) and dynamic image analysis. Comparative measurements were also made for various size fractions of granular particles of microcrystalline cellulose. The study demonstrated that the light scattering particle size methods (LD and FBRM) can be used qualitatively to study the attrition that occurs during drying of needle-shaped particles, however, for full quantitative analysis, image analysis is required. The algorithm used in analysis of LD data assumes the scattering particles are spherical regardless of the actual shape of the particles under evaluation. FBRM measures a chord length distribution (CLD) rather than the particle size distribution (PSD), which in the case of needles is weighted towards the needle width rather than their length. Dynamic image analysis allowed evaluation of the particles based on attributes of the needles such as length (e.g. the maximum Feret diameter) or width (e.g. the minimum Feret diameter) and as such, was the most informative of the techniques for the analysis of attrition that occurred during drying

Comparison of the determination of a low-concentration active ingredient in pharmaceutical tablets by backscatter and transmission raman spectrometry

A total of 383 tablets of a pharmaceutical product were analyzed by backscatter and transmission Raman spectrometry to determine the concentration of an active pharmaceutical ingredient (API), chlorpheniramine maleate, at the 2% m/m (4 mg) level. As the exact composition of the tablets was unknown, external calibration samples were prepared from chlorpheniramine maleate and microcrystalline cellulose (Avicel) of different particle size. The API peak at 1594 cm(-1) in the second derivative Raman spectra was used to generate linear calibration models. The API concentration predicted using backscatter Raman measurements was relatively insensitive to the particle size of Avicel. With transmission, however, particle size effects were greater and accurate prediction of the API content was only possible when the photon propagation properties of the calibration and sample tablets were matched. Good agreement was obtained with HPLC analysis when matched calibration tablets were used for both modes. When the calibration and sample tablets are not chemically matched, spectral normalization based on calculation of relative intensities cannot be used to reduce the effects of differences in physical properties. The main conclusion is that although better for whole tablet analysis, transmission Raman is more sensitive to differences in the photon propagation properties of the calibration and sample tablets

Automated cosmic spike filter optimized for process Raman spectroscopy

Despite the existence of various methods to remove cosmic spikes from Raman data, only a few of them are suitable for process Raman spectroscopy. The disadvantages of these algorithms include increased analysis time, low accuracy of spike detection, or reliance on variable parameters that must be chosen by trial and error in each case. We demonstrate a novel approach to detecting cosmic spikes in process Raman data and validate it using a wide range of experimental data. This new method features a multistage spike recognition algorithm that is based on tracking sharp changes of intensity in the time domain. The algorithm effectively distinguishes cosmic spikes from random spectral noise and abrupt variations of Raman peaks, allowing accurate detection of both high and low intensity cosmic spikes. The procedure is free from variable user-defined parameters and operates reliably in a fully automated manner with a wide range of time-series process Raman data sets containing more than 40 to 50 spectra

Close-range hyperspectral imaging of whole plants for digital phenotyping : recent applications and illumination correction approaches

Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high-end non-destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI). The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close-range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted

The Effect of Particle Size and Concentration on Low-Frequency Terahertz Scattering in Granular Compacts

Fundamental knowledge of scattering in granular compacts is essential to ensure accuracy of spectroscopic measurements and determine material characteristics such as size and shape of scattering objects. Terahertz time-domain spectroscopy (THz-TDS) was employed to investigate the effect of particle size and concentration on scattering in specially fabricated compacts consisting of borosilicate microspheres in a polytetrafluoroethylene (PTFE) matrix. As expected, increasing particle size leads to an increase in overall scattering contribution. At low concentrations, the scattering contribution increases linearly with concentration. Scattering increases linearly at low concentrations, saturates at higher concentrations with a maximum level depending on particle size, and that the onset of saturation is independent of particle size. The effective refractive index becomes sublinear at high particle concentrations and exceeds the linear model at maximum density, which can cause errors in calculations based on it, such as porosity. The observed phenomena are attributed to the change in the fraction of photons propagating ballistically versus being scattered. At low concentrations, photons travel predominately ballistically through the PTFE matrix. At high concentrations, the photons again propagate ballistically through adjacent glass microspheres. In the intermediate regime, photons are predominately scattered

Homogenising and segmenting hyperspectral images of plants and testing chemicals in a high-throughput plant phenotyping setup

Use of hyperspectral imaging (HSI) for automated characterisation of plants in a high-throughput plant phenotyping setup (HTPPS) is a challenging task. A challenge arises when the same plant is being monitored automatically during the experiment as it might not be in the same orientation as it was imaged last time. Such changes in orientation result in variations in illumination, which affects the signals recorded by the HSI setup. In addition, there are challenges with the use of threshold-based segmentation approaches such as normalised difference vegetation index (NDVI) for distinguishing between old and dead leaves, which might be observed in the later stages of experiments, from the soil background. Therefore, the potential of spectral normalisation for homogenising HS images and the use of supervised spectral set for plant segmentation is presented. Further, the effects of testing chemicals on plants were visualised using PCA of the HS images

Early detection of drought stress in Arabidopsis thaliana utilsing a portable hyperspectral imaging setup

Close-range hyperspectral imaging (HSI) of plants is now a potential tool for non-destructive extraction of plant functional traits. A major motivation is the plant phenotyping related applications where different plant genotypes are explored for different environmental conditions. HSI of Arabidopsis thaliana is of particular importance as it is a model organism in plant biology. In the present work, a portable HSI setup has been used for the monitoring of a set of 6 Arabidopsis thaliana plants. The plants were monitored under controlled watering conditions where 3 plants were watered as normal and the other 3 plants were given 50% of the normal volume of water. The images were pre-processed utilising the standard normal variate (SNV) and changes over time were evaluated using unsupervised clustering over the time series. The results showed an early detection of stress from day 4 onwards compared to the commonly used normalised difference vegetation index (NDVI), which provided detection from day 9

Ultrasonic array imaging through reverberating layers for industrial process analysis

In this paper, ultrasonic phased arrays are investigated as an imaging tool for industrial process analysis. Noninvasive process measurement, via transmission of information through a vessel wall, typically requires a window to create an optical path between the sensor and the process. Ultrasonic array imaging provides a means to overcome this barrier as it is specifically used to image into optically opaque structures. However, the large acoustic impedance mismatch between the steel process vessel and water load results in reverberations clouding the image scene containing reflections from within the process fluid. A methodology to identify and remove this reverberation interference from the image scene is proposed using subspace analysis coupled with phase coherence imaging. A 32 element, 5 MHz finite element array model mounted to the outside of a steel vessel wall is used to demonstrate the application of this methodology to a typical industrial process environment. The final image is free of reverberation artifacts, providing a means to accurately extract quantitative information about the process from these images