The metal to insulator transition of NbO2 has been predicted to be a result of a structural phase transition (SPT) governed by Peierls physics. However, direct observation of the SPT using experimental techniques is still restricted by the extremely high transition temperature (810 °C) and the proclivity for NbO2 to oxidize into Nb2O5 above 400 °C when exposed to air. Here, we address these issues and employ temperature-dependent X-ray spectroscopy to describe the SPT of NbO2 from the bulk to surface. Temperature-dependent extended X-ray absorption fine structure spectroscopy (T-EXAFS) reveals a gradual weakening of the bulk Nb dimers over a large temperature range, which is indicative of a second-order Peierls mechanism. From these measurements, we determine the critical dimer distance to be 2.77 Å. Our T-EXAFS observations are supported by density functional theory of the phonon dispersion and the electronic density of states of NbO2, which conclude that the dimerization is responsible for the insulating phase. The dimerization does not extend to the topmost layers, where an oxygen rich surface reconstruction is preferred irrespective of temperature even in extremely reducing environments; changes in the low-energy electron diffraction patterns are attributed to oxygen concentration and are independent of the underlying bulk phase transitions of NbO2.

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Introduction: Excessive demand, environmental problems, and shortages in market-leader countries have led the citrus (essential) oil market price to drift to unprecedented high levels with negative implications for citrus oil-dependent secondary industries. However, the high price conditions have promoted market incentives for the incorporation of new small-scale suppliers as a short-term supply solution for the market. Essential oil chemical extraction via steam distillation is a valuable option for these new suppliers at a lab and small-scale production level. Nevertheless, mass-scaling production requires prediction tools for better large-scale control of outputs.Methods: This study provides an intelligent model based on a multi-layer perceptron (MLP) artificial neural network (ANN) for developing a highly reliable numerical dependency between the chemical extraction output from essential oil steam distillation processes (output vector) and orange peel mass loading (input vector). In a data pool of 25 extraction experiments, 14 output–input pairs were the in training set, 6 in the testing set, and 5 cross-compared the model’s accuracy with traditional numerical approaches.Results and Discussion: After varying the number of nodes in the hidden layer, a 1–9–1 MLP topology best optimizes the statistical parameters (coefficient of determination (R2) and mean square error) of the testing set, achieving a precision of nearly 97.6%. Our model can capture non-linearity behavior when scaling-up production output for mass production processes, thus providing a viable answer for the scalability issue with a state-of-the-art computational tool for planning, management, and mass production of citrus essential oils.

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<span lang="ES-TRAD">Con el fin de estimar el tiempo requerido para alcanzar la temperatura promedio de un material altamente viscoso con la cual se obtiene la viscosidad óptima de bombeo, se llevó a cabo una simulación del calentamiento de Heavy Fuel Oil (HFO) ecuatoriano mediante serpentines en tanques a bordo. Se estudia la transmisión y transferencia de calor a través de un fluido altamente viscoso como el HFO, se incluyen procesos de conservación energética, conservación de masa, y conservación de momento lineal sobre fluidos viscosos, adicionalmente se incluyen procesos de difusión y convección energética. Finalmente, se determina que el proceso de distribución energético posee una alta dependencia con el perfil de calentamiento del vapor de agua en serpentines.</span>