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Solid state UV-vis reflectance spectra reveal the distinct electronic structure of POM@MOF materials obtained by synthetic encapsulation of mono- and di-vanadium substituted Keggin polyoxotungstates in HKUST-1. 

Abstract Large-scale geo-sequestration of anthropogenic carbon dioxide (CO2) is one of the most promising methods to mitigate the effects of climate change without significant stress on the current energy infrastructure. However, the successful implementation of CO2 sequestration projects in suitable geological formations, such as deep saline aquifers and depleted hydrocarbon reservoirs, is contingent upon the optimal selection of decision parameters constrained by several key uncertainty parameters. This study performs an in-depth parametric analysis of different CO2 injection scenarios (water-alternating gas, continuous, intermittent) for aquifers with varying petrophysical properties. The petrophysical properties evaluated in this study include aquifer permeability, porosity, relative permeability, critical gas saturation, and others. Based on the extensive data collected from the literature, we generated a large set of simulated data for different operating conditions and geological settings, which is used to formulate a proxy model using different machine learning methods. The injection is run for 25 years with 275 years of post-injection monitoring. The results demonstrated the effectiveness of the machine learning models in predicting the CO2 trapping mechanism with a negligible prediction error while ensuring a low computational time. Each model demonstrated acceptable accuracy (R2 >0.93), with the XGBoost model showing the best accuracy with an R2 value of 0.999, 0.995, and 0.985 for predicting the dissolved, trapped, and mobile phase CO2. Finally, a feature importance analysis is conducted to understand the effect of different petrophysical properties on CO2 trapping mechanisms. The WAG process exhibited a higher CO2 dissolution than the continuous or intermittent CO2 injection process. The porosity and permeability are the most influential features for predicting the fate of the injected CO2. The results from this study show that the data-driven proxy models can be used as a computationally efficient alternative to optimize CO2 sequestration operations in deep saline aquifers effectively. 

Ag_xMnO₂nanowires offer a potential solution to improve conductivity and stability of supercapacitor electrode. 

Abstract Organofunctionalized tetranuclear clusters [(M^IICl)₂(V^IVO)₂{((HOCH₂CH₂)(H)N(CH₂CH₂O))(HN(CH₂CH₂O)₂)}₂] (1, M=Co,2: M=Zn) containing an unprecedented oxometallacyclic {M₂V₂Cl₂N₄O₈} (M=Co, Zn) framework have been prepared by solvothermal reactions. The new oxo‐alkoxide compounds were fully characterized by spectroscopic methods, magnetic susceptibility measurement, DFT and ab initio computational methods, and complete single‐crystal X‐ray diffraction structure analysis. The isostructural clusters are formed of edge‐sharing octahedral {VO₅N} and trigonal bipyramidal {MO₃NCl} units. Diethanolamine ligates the bimetallic lacunary double cubane core of1and2in an unusual two‐mode fashion, unobserved previously. In the crystalline state, the clusters of1and2are joined by hydrogen bonds to form a three‐dimensional network structure. Magnetic susceptibility data indicate weakly antiferromagnetic interactions between the vanadium centers [J_iso(V^IV−V^IV)=−5.4(1); −3.9(2) cm⁻¹], and inequivalent antiferromagnetic interactions between the cobalt and vanadium centers [J_iso(V^IV−Co^II)=−12.6 and −7.5 cm⁻¹] contained in1. 

The typical student mind-set is focused on getting the ‘right’ answer for a problem with certainty that every problem has one and only one correct answer. However, this viewpoint is not consistent with real life problems as the information available for solving a real-life problem can be stochastic and incomplete. As a result, many correct answers could be possible and the acceptable one would depend on several factors. Students must therefore be exposed to such ambiguous problem spaces. This paper presents a comparison of undergraduate students’ tolerance of ambiguity. The modified Rydell-Rosen Ambiguity Tolerance scale was administered to a cross-section of students to measure their responses. Differences between engineering and non-engineering students were observed. The influence of academic classification and gender were also observed. 

The traditional educational paradigm encourages the development of dualistic intellectual mental models of the world view. Students strive to get the correct answer as expected by the teacher. With the development of understanding of the world view and student agency, the mental models move towards multiplicity and finally to a relativistic understanding. This paper discusses the cognitive development of undergraduate students and the impact of duration of stay in college. A validated instrument was used to measure anchoring of student mental models across the spectrum of duality, multiplicity, relativity, and commitment. Data were analyzed to determine the differences between engineering and non-engineering students. The influence of gender was studied. The effect of the academic standing was also investigated. Results of these analyses are shared. 

Human skeleton data provides a compact, low noise representation of relative joint locations that may be used in human identity and activity recognition. Hierarchical Co-occurrence Network (HCN) has been used for human activity recognition because of its ability to consider correlation between joints in convolutional operations in the network. HCN shows good identification accuracy but requires a large number of samples to train. Acquisition of this large-scale data can be time consuming and expensive, motivating synthetic skeleton data generation for data augmentation in HCN. We propose a novel method that integrates an Auxiliary Classifier Generative Adversarial Network (AC-GAN) and HCN hybrid framework for Assessment and Augmented Identity Recognition for Skeletons (AAIRS). The proposed AAIRS method performs generation and evaluation of synthetic 3-dimensional motion capture skeleton videos followed by human identity recognition. Synthetic skeleton data produced by the generator component of the AC-GAN is evaluated using an Inception Score-inspired realism metric computed from the HCN classifier outputs. We study the effect of increasing the percentage of synthetic samples in the training set on HCN performance. Before synthetic data augmentation, we achieve 74.49% HCN performance in 10-fold cross validation for 9-class human identification. With a synthetic-real mixture of 50%-50%, we achieve 78.22% mean accuracy, significantly