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The doping of lighter non-metals like boron and nitrogen into graphene represents a promising advancement in the field of nano-electronic devices, particularly in the development of field-effect transistors (FETs). These doped two-dimensional (2D) materials offer improved stability and enhanced adsorption characteristics compared to pure graphene. Notably, It displays semiconducting behavior, resulting in higher conductivity and carrier mobility. In this study, we investigate the structural, electronic, optical, and conductivity/carrier transport properties of 2D polymer sheets made of fullerene, both with and without boron and nitrogen doping. We employ density functional theory (DFT) with PBE and HSE functionals, considering the inclusion of van der Waals (vdW) interactions. The research findings indicate that the 2D sheets of C60, C58B1N1, and C54B3N3 exhibit band gaps of approximately 0.97 eV (1.5 eV), 1.08 eV (1.9 eV), and 1.05 eV (1.6 eV), respectively, as obtained from PBE (HSE) calculations. Moreover, according to the deformation potential theory, both doped sheets exhibit ultra-high conductivity at elevated temperature). These results are highly promising and underscore the significance of a single pair of BN dopants in fullerene (C58B1N1) monolayers for the advancement of next-generation 2D nanoelectronic and photonics applications.

This study provides a comprehensive time series analysis of daily industry-specific, country-wise CO$_2$ emissions from January 2019 to February 2023. The research focuses on the Power, Industry, Ground Transport, Domestic Aviation, and International Aviation sectors in European countries (EU27 & UK, Italy, Germany, Spain) and India, utilizing near-real-time activity data from the Carbon Monitor research initiative. To identify regular emission patterns, the data from the year 2020 is excluded due to the disruptive effects caused by the COVID-19 pandemic. The study then performs a principal component analysis (PCA) to determine the key contributors to CO$_2$ emissions. The analysis reveals that the Power, Industry, and Ground Transport sectors account for a significant portion of the variance in the dataset. A 7-day moving averaged dataset is employed for further analysis to facilitate robust predictions. This dataset captures both short-term and long-term trends and enhances the quality of the data for prediction purposes. The study utilizes Long Short-Term Memory (LSTM) models on the 7-day moving averaged dataset to effectively predict emissions and provide insights for policy decisions, mitigation strategies, and climate change efforts. During the training phase, the stability and convergence of the LSTM models are ensured, which guarantees their reliability in the testing phase. The evaluation of the loss function indicates this reliability. The model achieves high efficiency, as demonstrated by $R^2$ values ranging from 0.8242 to 0.995 for various countries and sectors. Furthermore, there is a proposal for utilizing scandium and boron/aluminium-based thin films as exceptionally efficient materials for capturing CO$_2$ (with a binding energy range from -3.0 to -3.5 eV). These materials are shown to surpass the affinity of graphene and boron nitride sheets in this regard., Comment: 38 pages, 16 figures

Deep Learning (DL) , a variant of the neural network algorithms originally proposed in the 1980s, has made surprising progress in Artificial Intelligence (AI), ranging from language translation, protein folding, autonomous cars, and more recently human-like language models (CHATbots), all that seemed intractable until very recently. Despite the growing use of Deep Learning (DL) networks, little is actually understood about the learning mechanisms and representations that makes these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and of course the large scale of the data, since not much has changed since 1987. But the nature of deep learned representations remain largely unknown. Unfortunately training sets with millions or billions of tokens have unknown combinatorics and Networks with millions or billions of hidden units cannot easily be visualized and their mechanisms cannot be easily revealed. In this paper, we explore these questions with a large (1.24M weights; VGG) DL in a novel high density sample task (5 unique tokens with at minimum 500 exemplars per token) which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping, From these results we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.