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Variants of spatial interpolation and data-driven methods to fill gaps in daily precipitation records are developed and evaluated in this study. The evaluated methods include variations of inverse distance and correlation weighting procedures, linear weight optimization and artificial neural networks. An already existing method, support vector logistic regression-based copula, is also assessed. Optimal weights are estimated using inverse distance and correlation-based weighting methods, post-corrections of spatially interpolated estimates for rain or no rain classifications using support vector machine (SVM), and variations of a single best classifier (SBC) are used. The optimal number of gauges for use in spatial interpolation methods and for artificial neural network-based method are selected. Three benchmark methods provide a basis against which all the methods are compared: single best estimator (SBE), and spatial and climatological mean estimators (SME and CME). All of the methods are tested for estimating varying amounts of missing precipitation data at 53 rain gauges located in South Florida, USA. Results show that the linear weight optimization method with an SBE provides the best estimates of daily precipitation values based on several performance metrics. Results from evaluation of different methods and their variants indicate use of optimized exponents in distance and correlation-based weighting methods, classifiers for rain or no rain conditions, and an optimal number of neighbours in spatial interpolation improve estimates of missing data. Corrections to missing data estimates using nearest neighbours can help in improving the accuracy of rain and no rain state determinations with a possibility of introducing bias in estimates.

III-Nitride semiconductors face the issue of localized surface states, which causes fermi level pinning and large leakage current at the metal semiconductor interface, thereby degrading the device performance. In this work, we have demonstrated the use of a Self-Assembled Monolayer (SAM) of organic molecules to improve the electrical characteristics of Schottky barrier diodes (SBDs) on n-type Gallium Nitride (n-GaN) epitaxial films. The electrical characteristics of diodes were improved by adsorption of SAM of hydroxyl-phenyl metallated porphyrin organic molecules (Zn-TPPOH) onto the surface of n-GaN. SAM-semiconductor bonding via native oxide on the n-GaN surface was confirmed using X-ray photoelectron spectroscopy measurements. Surface morphology and surface electronic properties were characterized using atomic force microscopy and Kelvin probe force microscopy. Current-voltage characteristics of different metal (Cu, Ni) SBDs on bare n-GaN were compared with those of Cu/Zn-TPPOH/n-GaN and Ni/Zn-TPPOH/n-GaN SBDs. It was found that due to the molecular monolayer, the surface potential of n-GaN was decreased by $350 mV. This caused an increase in the Schottky barrier height of Cu and Ni SBDs from 1.13 eV to 1.38 eV and 1.07 eV to 1.22 eV, respectively. In addition to this, the reverse bias leakage current was reduced by 3-4 orders of magnitude for both Cu and Ni SBDs. Such a significant improvement in the electrical performance of the diodes can be very useful for better device functioning.

Scattering of light by molecules can be elastic, Rayleigh scattering, or inelastic, Raman scattering. In the elastic scattering, the photon’s energy and the state of the molecule after the scattering events are unchanged. Hence, Rayleigh scattered light does not contain much information on the structure of molecular states. In inelastic scattering, the frequency of monochromatic light changes upon interaction with the vibrational states, or modes, of a molecule. With the advancement in the laser sources, better and compact spectrometers, detectors, and optics Raman spectroscopy have developed as a highly sensitive technique to probe structural details of a complex molecular structure. However, the low scattering cross section (10−31) of Raman scattering has limited the applications of the conventional Raman spectroscopy. With the discovery of surface-enhanced Raman scattering (SERS) in 1973 by Martin Fleischmann, the interest of the research community in Raman spectroscopy as an analytical method has been revived. This chapter aims to familiarize the readers with the basics of Raman scattering phenomenon and SERS. This chapter will also discuss the latest developments in the SERS and its applications in various fields.

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