Design of High-Performance Lead-Free Quaternary Antiperovskites for Photovoltaics via Ion Type Inversion and Anion Ordering.

peer reviewedThe emergence of halide double perovskites significantly increases the compositional space for lead-free and air-stable photovoltaic absorbers compared to halide perovskites. Nevertheless, most halide double perovskites exhibit oversized band gaps (>1.9 eV) or dipole-forbidden optical transition, which are unfavorable for efficient single-junction solar cell applications. The current device performance of halide double perovskite is still inferior to that of lead-based halide perovskites, such as CH3NH3PbI3 (MAPbI3). Here, by ion type inversion and anion ordering on perovskite lattice sites, two new classes of pnictogen-based quaternary antiperovskites with the formula of X6B2AA' and X6BB'A2 are designed. Phase stability and tunable band gaps in these quaternary antiperovskites are demonstrated based on first-principles calculations. Further photovoltaic-functionality-directed screening of these materials leads to the discovery of 5 stable compounds (Ca6N2AsSb, Ca6N2PSb, Sr6N2AsSb, Sr6N2PSb, and Ca6NPSb2) with suitable direct band gaps, small carrier effective masses and low exciton binding energies, and dipole-allowed strong optical absorption, which are favorable properties for a photovoltaic absorber material. The calculated theoretical maximum solar cell efficiencies based on these five compounds are all larger than 29%, comparable to or even higher than that of the MAPbI3 based solar cell. Our work reveals the huge potential of quaternary antiperovskites in the optoelectronic field and provides a new strategy to design lead-free and air-stable perovskite-based photovoltaic absorber materials

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content

A new machine-learning prediction model for slope deformation of an open-pit mine: An evaluation of field data

Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners

A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data

Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners

Natural convection and entropy generation of a nanofluid around a circular baffle inside an inclined square cavity under thermal radiation and magnetic field effects

© 2020 Elsevier Ltd A numerical investigation of the free convection of the Al2O3/water nanofluid was carried out in a square cavity. The cavity was tilted and exposed to a constant horizontal magnetic field. Radiation also occurred in the cavity, and entropy generation was also investigated. The left-hand and right-hand walls of the cavity were kept at a fixed temperature (Tc), while the upper and lower walls were insulated. A circular baffle with a radius of R and a temperature of Th was placed in the middle of the cavity. According to the results, amplifying the Rayleigh number (Ra) improved the Nusselt number (Nu) by 4.5 times. Amplifying the Ra also promoted entropy generation but diminished the Bejan number (Be). The heat transfer rate and generated entropy increased also by amplifying the aspect ratio. An amplification of the Hartmann number (Ha) reduced the heat transfer rate and generated entropy by 45% and 35%, respectively. Furthermore, Be was also augmented by growing the Ha. The maximum entropy generation and Be were observed at 0 and 60° inclination angles, respectively. Incorporating heat transfer by radiation and adding nano-particles to the base fluid increased both the heat transfer rate and entropy generation

A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine : An Evaluation of Field Data

Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners

First principles study of dissolved oxygen water adsorption on Fe (001) surfaces

In order to study the mechanism of dissolved oxygen content on the surface corrosion behavior of Fe-based heat transfer, the first principle is used to study the adsorption of O2 monomolecular, H2O monolayer and dissolved oxygen system on Fe-based heat transfer surface. The GGA/PBE approximation is used to calculate the adsorption energy, state density and population change during the adsorption process. Calculations prove that when the dissolved oxygen is adsorbed on the Fe-based surface, the water molecule tends to adsorb at the top sites, and the oxygen molecule tends to adsorb at Griffiths. When the H2O molecule adsorbs and interacts on the Fe (001) surface, the charge distribution of the interfacial double electric layer changes to cause the Fe atoms to lose electrons, resulting in the change of the surface potential. When the O2 molecule adsorbs on the Fe (001) crystal surfaces, the electrons on the Fe (001) surface are lost and the surface potential increases. O2 molecule and the surface of the Fe atoms are prone to electron transfer, in which O atom's 2p orbit for the adsorption of O2 molecule on Fe (001) crystal surface play a major role. With the increase of the proportion of O2 molecule in the dissolved oxygen water, the absolute value of the adsorption energy increases, and the interaction of the Fe-based heat transfer surface is stronger. This study explores the influence law of different dissolved oxygen on the Fe base heat exchange surface corrosion, and the base metal corrosion mechanism for experimental study provides a theoretical reference

Effect of Stiffeners on Mechanical Behavior of T-Stubs Based on Experiment and Numerical Simulations

T-stubs are important components in the application of the component method; hence it is crucial to clarify the T-stub mechanism for the analysis of the mechanical properties of steel joints. In this study, the mechanical behavior of T-stubs was assessed via 6 static tests and 18 finite element analysis models. The influences of flange thickness, bolt spacing, bolt diameter, and stiffener rib on initial stiffness, ultimate bearing capacity, and bolt force of T-stubs were analyzed. Furthermore, the development process and relations of the T-stub bolt force with and without stiffeners were analyzed. The results show that an effective stiffener arrangement can reduce the bending moment and prying force to a certain degree; however, offsetting the bending moment and prying force entirely is difficult. Furthermore, the influence of bending moment and prying force on the bearing capacity should be considered in the design

Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement

A new digital twin (DT) framework with optimal sensor placement (OSP) is proposed to accurately calculate the modal responses and identify the damage ratios of the offshore jacket platforms. The proposed damage identification framework consists of two models (namely one OSP model and one damage identification model). The OSP model adopts the multi-objective Lichtenberg algorithm (MOLA) to perform the sensor number/location optimization to make a good balance between the sensor cost and the modal calculation accuracy. In the damage identification model, the Markov Chain Monte Carlo (MCMC)-Bayesian method is developed to calculate the structural damage ratios based on the modal information obtained from the sensory measurements, where the uncertainties of the structural parameters are quantified. The proposed method is validated using an offshore jacket platform, and the analysis results demonstrate efficient identification of the structural damage location and severity

A gradient weighted extended finite element method (GW-XFEM) for fracture mechanics

In this study, a gradient weighted extended finite element method (GW-XFEM) is presented for the analysis of fracture problems. For this method, the domain discretization is the same as the standard XFEM. However, the gradient field is constructed by considering the influences of the element itself and its adjacent elements. Based on the Shepard interpolation, the weighted strain filed can be obtained, which will be utilized to construct the discretized system equations. The validity of the presented method is fully investigated through several numerical examples. From these results, it is shown that compared with standard XFEM, the presented method can achieve much better accuracy, efficiency and higher convergence, when dealing with fracture analysis