Searching for new types of electrocatalysts with high stability, activity, and selectivity is essential for the production of ammonia via electroreduction of nitrogen. Using density functional theory (DFT) calculations, we explore the stability of single metal atoms (M 1 ) supported on nitrogen-doped graphene (N 3 -G); the competitive adsorption of dinitrogen and hydrogen; and the potential competition of first dinitrogen protonation and hydrogen adsorption on metal sites. Consequently, we identify Mo 1 /N 3 -G and Cr 1 /N 3 -G as candidate electrocatalysts for nitrogen reduction reaction (NRR). The theoretically predicted selectivities (overpotentials) are 40% (0.34 V) and 100% (0.59 V) on Mo 1 /N 3 -G and Cr 1 /N 3 -G, respectively. The electroreduction of nitrogen proceeds via distal-toalternating hybrid mechanism with two spectator dinitrogen molecules. The high stability, high selectivity to ammonia, and relatively low overpotentials for NRR suggest Mo 1 (Cr 1 )/N 3 -G as the most promising electrocatalyst among those studied for electroreduction of nitrogen.

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Development of highly active and durable oxygen-evolving catalysts in acid media is a major challenge to design proton exchange membrane water electrolysis for producing hydrogen. Here, we report a quadruple perovskite oxide CaCu 3 Ru 4 O 12 as a superior catalyst for acidic water oxidation. This complex oxide exhibits an ultrasmall overpotential of 171 mV at 10 mA cm −2 geo , which is much lower than that of the state-of-the-art RuO 2 . Moreover, compared to RuO 2 , CaCu 3 Ru 4 O 12 shows a significant increase in mass activity by more than two orders of magnitude and much better stability. Density functional theory calculations reveal that the quadruple perovskite catalyst has a lower Ru 4d-band center relative to RuO 2 , which effectively optimizes the binding energy of oxygen intermediates and thereby enhances the catalytic activity.

Spin and valley degrees of freedom in materials without inversion symmetry promise previously unknown device functionalities, such as spin-valleytronics. Control of material symmetry with electric fields (ferroelectricity), while breaking additional symmetries, including mirror symmetry, could yield phenomena where chirality, spin, valley, and crystal potential are strongly coupled. Here we report the synthesis of a halide perovskite semiconductor that is simultaneously photoferroelectricity switchable and chiral. Spectroscopic and structural analysis, and first-principles calculations, determine the material to be a previously unknown low-dimensional hybrid perovskite (R)-(−)-1-cyclohexylethylammonium/(S)-(+)-1 cyclohexylethylammonium) PbI3. Optical and electrical measurements characterize its semiconducting, ferroelectric, switchable pyroelectricity and switchable photoferroelectric properties. Temperature dependent structural, dielectric and transport measurements reveal a ferroelectric-paraelectric phase transition. Circular dichroism spectroscopy confirms its chirality. The development of a material with such a combination of these properties will facilitate the exploration of phenomena such as electric field and chiral enantiomer–dependent Rashba-Dresselhaus splitting and circular photogalvanic effects.

Cost‐effective carbon‐based catalysts are promising for catalyzing the electrochemical N2 reduction reaction (NRR). However, the activity origin of carbon‐based catalysts towards NRR remains unclear, and regularities and rules for the rational design of carbon‐based NRR electrocatalysts are still lacking. Based on a combination of theoretical calculations and experimental observations, chalcogen/oxygen group element (O, S, Se, Te) doped carbon materials were systematically evaluated as potential NRR catalysts. Heteroatom‐doping‐induced charge accumulation facilitates N2 adsorption on carbon atoms and spin polarization boosts the potential‐determining step of the first protonation to form *NNH. Te‐doped and Se‐doped C catalysts exhibited high intrinsic NRR activity that is superior to most metal‐based catalysts. Establishing the correlation between the electronic structure and NRR performance for carbon‐based materials paves the pathway for their NRR application.

Abstract:Spatial-temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial-temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA-Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA-Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations.

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Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.