Preparation and characterization of sol-gel derived KTiOPO4 thin films for integrated optical applications

A sol gel route has been developed to produce stoichiometric, dense and crack free KTiOPO₄ (KTP) thin films which exhibit waveguiding. This method involved the deposition and pyrolysis of hydrolyzed solutions of K, Ti and P alkoxides on an appropriate substrate. The final ceramic film morphology is greatly dependent on the preparation chemistry, apparently as a result of the formation of polymetallic complexes. The solution chemistry, processing conditions and their influence on the final ceramic film microstructure will be discussed in detail.Optical properties such as indices of refraction and optical losses have been found to be dependent on the film microstructures. Deposition on different substrates has been undertaken to promote preferential orientation and thus to try to minimize the optical losses in the films. This correlation between the optical losses and microstructures will be discussed in detail.     

Cryogenic 35 GHz pulse ENDOR probehead accommodating large sample sizes: Performance and applications

The construction and performance of a cryogenic 35 GHz pulse electron nuclear double resonance (ENDOR) probehead for large samples is presented. The resonator is based on a rectangular TE₁₀₂ cavity in which the radio frequency (rf) B₂-field is generated by a two turn saddle ENDOR coil crossing the resonator along the sample axis with minimal distance to the sample tube. An rf power efficiency factor is used to define the B₂-field strength per square-root of the transmitted rf power over the frequency range 2–180 MHz. The distributions of the microwave B₁- and E₁-field, and the rf B₂-field are investigated by electromagnetic field calculations. All dielectrics, the sample tube, and coupling elements are included in the calculations. The application range of the probehead and the advantages of using large sample sizes are demonstrated and discussed on a number of paramagnetic samples containing transition metal ions.     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.