While lab-scale synthesis of trigonal-Zr2N2S, hexagonal-Zr2N2S and hexagonal-Zr2N2Se has been reported, meaningful data on the photophysical properties of IV-nitride chalcogenides in general are scarcely available. The first-principles calculations and genetic algorithm modeling in our work reveal the existence of remarkably stable, indirect gap trigonal-Zr2N2Se and trigonal-Hf2N2Se phases, which progress to direct gap, monoclinic materials in monolayer form. These structures display the desired optoelectronic properties, such as exceptionally high visible-UV absorption spectra (105-106 cm-1) and exciton binding energy below 0.02 eV. Strong hybridization between the Zr-d, N-p and Se-p orbitals is accounted for by the polysilicon comparable Vickers hardness (10.64-12.77 GPa), while retaining ductile nature.
Crystalline ZnTeO thin films are promising materials for next generation photovoltaics. However, their structural stability and optical nonlinearity potential in bulk form have not been reported. Here, structural, electronic and optical properties of ZnTeO composites have been thoroughly studied using genetic algorithm and density functional theory (DFT). Energetically, mechanically and dynamically stable O-rich phases, namely Zn2Te2O6 and ZnTeO4, were obtained. Ground-state properties such as lattice constants and simulated XRD were analyzed and compared to the experimental literature wherever possible. With a G 0 W 0 corrected band gap, these semiconducting phases display several desirable features, namely, Jahn-Teller distorted cations, hardness and shear anisotropy-induced optical nonlinearity that increase monotonically as O concentration elevates. Such trends appear to be consistent with that seen in the experimental study of ZnTeO thin film. It is observed that Zn-d, Te-p and O-p states have immense influence towards the electronic properties of these structures. Both phases exhibit steep elevation of absorption throughout the ultraviolet (UV) range, hitting peak value of ~5.0 × 105 cm-1. Of particular interest, the non-centrosymmetric ZnTeO4 has second harmonic generation coefficients (9.84 pm V-1 and 2.33 pm V-1 at static limit) greater than borates crystal and large birefringence that exceeds 0.08 in deep UV region, thus highlighting its potential pedigree as new optical materials in UV range.
Based on first-principles calculations, we propose a new two-dimensional (2D) van der Waals (vdW) heterostructure that can be used as a photocatalyst for water splitting. The heterostructure consists of vertically stacked 2D NbSe2H and graphene-like ZnO (g-ZnO). Depending on the stacking orders, we identified two configurations that have high binding energies with an energy band gap of >2.6 eV. These 2D systems form a type-II heterostructure which enables the separation of photoexcited electrons and holes. The presence of a strong electrostatic potential difference across the 2D NbSe2H and g-ZnO interface is expected to suppress the electron-hole recombination leading to an enhancement in the efficiency of the photocatalytic activity. Our study also shows that the 2D NbSe2H/g-ZnO vdW heterostructure has good thermodynamic properties for water splitting. Furthermore, the optical absorption of the 2D NbSe2H/g-ZnO vdW heterostructure extends into the visible light region. Our results suggest that the 2D NbSe2H/g-ZnO vdW heterostructure is a promising photocatalytic material for water splitting.
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weight-to-height ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, F-score of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is well-generalized. Using Bayes' Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes' Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening high-risk adolescents of developing hypertension.
Theoretical investigations of the thermoelectric and piezoelectric characteristics in the AlxIn1-xN system have been carried out based on a first principles approach in combination with the semi-classical Boltzmann transport concept and density functional perturbation theory. Based on our previous work, herein, the study specimens Al5InN6, Al6In2N8, Al4In2N6, Al3In3N6, Al2In4N6, and AlIn7N8 have been predicted to be stable phases. These novel phases intrinsically exhibit moderate positive Seebeck curves (199.1-284.6 μV K-1) and a ZT close to unity that varies marginally over a broad temperature range of 200-800 K, demonstrating the sign of good bipolar effect tolerance. Addition of heftier elements, such as In, results in lower thermal conductivity, which in turn generates a high power factor (0.019-0.345 W m-1 K-2) in these alloys. While hole doping enhances the peak Seebeck coefficient of each phase, the electrical conductivity has been greatly compromised, resulting in a lower power factor. These composites also exhibit large piezoelectric constants, in which their respective largest piezoelectric tensor is several orders higher than that of quartz. The decomposition process shows that In and N are the main contributors of the internal piezoelectric term. Overall results indicate that AlxIn1-xN show bright prospects in thermoelectric and piezoelectric applications.
The electromechanical properties of monolayer 1-T NiTe2 under charge actuation were investigated using first-principles density functional theory (DFT) calculations. Monolayer 1-T NiTe2 in its pristine form has a work area density per cycle of up to 5.38 MJ m-3 nm upon charge injection and it can generate a strain and a stress of 1.51% and 0.96 N m-1, respectively. We found that defects in the form of vacancies can be exploited to modulate the electromechanical properties of this material. The presence of Ni-vacancies can further enhance the generated stress by 22.5%. On the other hand, with Te-vacancies, it is possible to improve the work area density per cycle by at least 145% and also to enhance the induced strain from 1.51% to 2.92%. The effect of charge polarity on the contraction and expansion of monolayer 1T-NiTe2 was investigated. Due to its excellent environmental stability and good electromechanical properties, monolayer NiTe2 is considered to be a promising electrode material for electroactive polymer (EAP) based actuators.
The search for high-performance catalysts to improve the catalytic activity for an oxygen reduction reaction (ORR) is crucial for developing a proton exchange membrane fuel cell. Using the first-principles method, we have performed computational screening on a series of transition metal (TM) atoms embedded in monolayer Nb2S2C to enhance the ORR activity. Through the scaling relationship and volcano plot, our results reveal that the introduction of a single Ni or Rh atom through substitutional doping into monolayer Nb2S2C yields promising ORR catalysts with low overpotentials of 0.52 and 0.42 V, respectively. These doped atoms remain intact on the monolayer Nb2S2C even at elevated temperatures. Importantly, the catalytic activity of the Nb2S2C doped with a TM atom can be effectively correlated with an intrinsic descriptor, which can be computed based on the number of d orbital electrons and the electronegativity of TM and O atoms.
Amidst the digital transformation of education, the essence of the human touch in online teaching remains pivotal. Despite growing literature, there remains a significant gap in understanding how the human element in online teaching directly influences student engagement and learning outcomes, especially in diverse educational contexts. This study develops a quantifiable index capturing the essence of humanized online teaching and investigates the determinants influencing this humanization. Additionally, an index encapsulating students' online learning experiences, as perceived by their instructors, has been constructed. Bridging these indices, the research unravels the intricate relationship between the humanization of online teaching and the resulting student experiences in the virtual realm. Sourced from a self-constructed questionnaire and encompassing responses from 152 instructors across 22 Malaysian institutions, the data revealed an average incorporation of 81.38% humanized online teaching elements. Key determinants, such as subject matter, teaching experience, Internet quality, and platform choices, emerged as significant influences. A regression model showed approximately 31.7% (R-squared = 0.317, p<0.001) of the variation in the dependent variable. A significant moderate positive correlation (r = 0.423, p<0.001) between the Humanized Online Teaching Index and the Students' Online Learning Experiences Index highlights the intertwined nature of humanized instructional methodologies and enhanced student engagement in online settings. Though contextualised during the coronavirus disease-2019 pandemic, the study's implications transcend the immediate circumstances, offering transformative insights for future online teaching methodologies and enhancing student experiences in the evolving digital age.