In this paper we investigate the surface chemistry, including surface contaminations, of SnO2 nanowires deposited on Ag-covered Si substrate by vapor phase deposition (VPD), thanks to x-ray photoelectron spectroscopy (XPS) in combination with thermal desorption spectroscopy (TDS). Air-exposed SnO2 nanowires are slightly non-stoichiometric, and a huge amount of C contaminations is observed at their surface. After the thermal physical desorption (TPD) process, SnO2 nanowires become almost stoichiometric without any surface C contaminations. This is probably related to the fact that C contaminations, as well as residual gases from air, are weakly bounded to the crystalline SnO2 nanowires and can be easily removed from their surface. The obtained results gave us insight on the interpretation of the aging effect of SnO2 nanowires that is of great importance for their potential application in the development of novel chemical nanosensor devices.

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When training Convolutional Neural Networks (CNNs) there is a large emphasis on creating efficient optimization algorithms and highly accurate networks. The stateof-the-art method of optimizing the networks is done by using gradient descent algorithms, such as Stochastic Gradient Descent (SGD). However, there are some limitations presented when using gradient descent methods. The major drawback is the lack of exploration, and over-reliance on exploitation. Hence, this research aims to analyze an alternative approach to optimizing neural network (NN) weights, with the use of population-based metaheuristic algorithms. A hybrid between Grey Wolf Optimizer (GWO) and Genetic Algorithms (GA) is explored, in conjunction with SGD; producing a Genetically Modified Wolf optimization algorithm boosted with SGD (GMW-SGD). This algorithm allows for a combination between exploitation and exploration, whilst also tackling the issue of high-dimensionality, affecting the performance of standard metaheuristic algorithms. The proposed algorithm was trained and tested on CIFAR-10 where it performs comparably to the SGD algorithm, reaching high test accuracy, and significantly outperforms standard metaheuristic algorithms.