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In order to analyze and test the corrosion resistance of passivation film of 316L stainless steel in electrolyte, the corrosion resistance of the 316L passivation film was detected by Tafel corrosion polarization curve, electrochemical impedance spectroscopy (EIS) and Mott - Schottky. The results of polarization curve showed that the corrosion potential was -0.955 V, and the corrosion current density was 10-4.02 A/cm2. The results of EIS indicated that when the film forming potential was 0.3 V, the corrosion resistance of the film was superior to that of the films formed under other potentials. From the Mott - Schottky analysis, it could be obtained that the donor density of the passivation film decreased roughly as the formation potential increased, whereas the thickness of the passivation film increased roughly as the increase of formation potential. Meanwhile, according to PDM analysis, the film would be corroded to a depth of 4.5 mm in 100 a under this electrolyte.

Solving the oxidation resistance problem of refractory high-entropy alloy at high temperature is the premise of application in engineering.The preparation of anti-oxidation coating on the surface of refractory metal is an effective way to improve its oxidation resistance.A complex silicide anti-oxidation coating(Si-20Cr-20Fe)was prepared on the surface of MoNbTaTiW refractory high-entropy alloy by slurry fusion sintering method.The microstructure, phase and composition of the initial silicide coating and the coating after oxidation at 1 300 ℃ were studied by SEM, EDS and XRD.The diffusion law of Si, Fe and Cr in the coating and the anti-oxidant mechanism of the coating were explored.Results showed that the initial silicide coating after melting was composed of disilicide, 5/3 silicide and ternary silicide Cr4Nb2Si5 formed by the reaction between metal and silicon.A good diffusion reaction interface was formed between the silicide coating and the substrate.After oxidation at 1 300 ℃, Si, Fe and Cr diffused to the substrate, and the diffusion reaction interface layer moved to the substrate, consequently the coating thickness was increased.The microstructure and composition of the diffusion reaction interface layer were almost the same as the diffusion layer of the complex initial silicide coating.After oxidation, a crack-free, dense oxide layer was formed on the surface of the coating.The layer was composed of CrNbO4, SiO2, WO3 and Fe2SiO4, and it could prevent the further diffusion of oxygen to the inside of substrate.Si, Fe and Cr had different diffusion rates in MoNbTaTiW refractory high-entropy alloy.The diffusion rate of Si was the fastest, Fe was the second, and Cr was the slowest.Therefore, Fe and Cr were enriched in the low silicon content diffusion layer and silicide layer.The complex silicide coating prepared by slurry fusion sintering could provide good anti-oxidation protection for MoNbTaTiW refractory high-entropy alloy at high temperatures.

Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

The complicated trilateral relationships among molecular structures, properties, and photovoltaic performances of electron donor and acceptor materials hinder the rapid improvement of power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, the database of 310 donor and non‐fullerene acceptor pairs is constructed and 39 molecular structure descriptors are selected. Four kinds of machine learning (ML) algorithms random forest (RF), extra trees regression, gradient boosting regression trees, and adaptive boosting are applied to predict photovoltaic parameters. The coefficient of determination, Pearson correlation coefficient, mean absolute error, and root mean square error are adopted to evaluate ML performance. The results show that the RF model exhibits the best prediction accuracy. The Gini important analysis suggests the fused ring and aromatic heterocycles are critical fragments in determining PCE. The molecular unit sets are constructed by cutting each donor and acceptor molecules in database. The 31 752 D‐π‐A‐π type donor molecules and 5 455 164 A‐π‐D‐π‐A type acceptor molecules are designed by recombination of molecular units, and 173 212 367 328 donor–acceptor pairs are generated by combining the newly designed donor and acceptor molecules. Based on the predicted PCE using the trained RF model, 42 donor–acceptor pairs exhibit the predicted PCE > 16%, in which the highest PCE is 16.24%. [ABSTRACT FROM AUTHOR]

The selection of electron donors and nonfullerene acceptors (NFAs) in organic solar cells (OSCs) is crucial for improving photovoltaic performance. Machine learning (ML) has brought a breakthrough solution. Herein, 292 donor‐NFA pairs with experimental OSC parameters from the reported articles are collected. The ML descriptors include device processing parameters, molecular properties, and molecular structure. The five ML regression models, random forest (RF), extra tree regression, gradient boosting regression tree, adaptive boosting, and artificial neural network (ANN) are trained. GridSearchCV is used for hyperparameter optimization of ML regression models. The SHapley Additive exPlanation approach is employed to analyze descriptor importance. Among the trained five ML models, the RF model shows superior performance, achieving Pearson's correlation coefficient (r) of 0.81 on the test set. Based on the donors and NFAs in constructed dataset, the 9779 donor–NFA pairs for OSCs are generated by randomly combining donor and acceptor molecules. The trained RF model is utilized to predict the power conversion efficiency (PCE) of new donor–acceptor pairs for OSCs. The results indicate that the OSC composed of PBDB‐TF as donor and L8‐BO as acceptor can achieve the remarkable PCE of 17.9%. [ABSTRACT FROM AUTHOR]

The modification of fused rings in the central backbone, side chains, and end groups of the non‐fullerene acceptor (NFA) for organic solar cells (OSCs) can modulate properties and photovoltaic performance. In order to investigate the effect of fused rings and side chains on photovoltaic performance, PBDB‐T is selected as electron donor and IDTIC, IDIC, IDIC‐PhC6, IDIC‐C4Ph, 4TIC, ITIC‐OE, IDTTIC, ITC6‐IC, ITIC, and C8‐ITIC are selected as NFAs. Based on quantum chemistry calculations, the geometries, electronic structures, excitation properties, excited‐state lifetimes, and electrostatic potentials (ESPs) of the monomer and the PBDB‐T:NFA complexes are studied, and the rate constants of charge transfer (CT), exciton dissociation (ED) and charge recombination (CR) processes are analyzed. The results show that the increase of fused‐ring in NFA's backbone by substituting phenyl or thienyl with bithiophene can elevate the highest occupied molecular orbital energies, reduce the gap of frontier molecular orbital energies, induce red‐shift of absorption spectrum, increase CT energy, improve CT and ED rates. This work provides a detailed understanding of tuning optoelectronic properties and photovoltaic performance by modifying NFA's side chains and extending backbones. [ABSTRACT FROM AUTHOR]

Molecular descriptors are critical for determining the accuracy of machine learning (ML) study on organic photovoltaics (OPV). To unravel the complex relationship between molecular properties and device performance, on the basis of 510 donor‐acceptor pairs in OPV active layer, the open‐circuit voltage loss (VOC‐loss), dielectric constants of donor and acceptor (ε‐D and ε‐A) were firstly implemented into property descriptor set that includes 41 quantities totally. Then, the five ML algorithms were applied to compare the property descriptor sets with and without VOC‐loss, ε‐D and ε‐A (coded as new and old sets) in the prediction of photovoltaic parameters. The ML results of Pearson's correlation coefficient and the slope of regression lines indicate the performances of new molecular descriptor set are prevailing to that of old set. Furthermore, the Gini important analysis indicates that the ε‐D, ε‐A and VOC‐loss are very important parameters for determining device performance. Higher dielectric constants and lower VOC‐loss will be more beneficial to the performance of OPV devices. [ABSTRACT FROM AUTHOR]

The optoelectronic properties of formamidinium CH(NH2)2+ (FA) doped double perovskite Cs2AgBiBr6 with corresponding formula Cs1.875FA0.125AgBiBr6 were investigated based on electronic structures and supercell models. The dopant formation energies, tolerance and octahedral factors support the doped structures are stable. FA dopant increases band gap about 0.15 eV without introducing dopant states in gap, but also increases the reduced effective masses, exciton binding energies, and elastic constants. However, it decreases exciton energies and charge carrier mobilities. Though the energy barriers among the different FA orientations in Cs1.875FA0.125AgBiBr6 are quite small (<30 meV), FA orientations in doped systems can remarkably impact on electron and hole mobilities. [ABSTRACT FROM AUTHOR]