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The efficient and stable electroluminescence of quantum dots (QDs) is of great importance in their applications in new display technologies. The short service life of blue QDs, however, hinders their development and commercialization. Different mechanisms have been proposed for the destabilization of QDs in electroluminescent processes. Based on real-time time-dependent density functional theory studies on the QD models covered by Z-type ligands (XAc2, X = Cd, Zn, Mg), the structural evolution is simulated to reveal the mechanism of the reduction reactions induced by electron injection. Our simulations reproduce the experimental observations that the reduction reactions occur at the QD–ligand interface, and the reduced Cd atom is almost in a zero valence state. However, different sites are predicted for the reactions in which the surface metal atom of the QD instead of the metal atom in the ligands is reduced. As a result, one of the arms of the chelate ligand leaves the QD, which tends to cause damage to its electroluminescent performance. Our findings contribute to a mechanistic understanding of the reduction reactions that occurred at the QD–ligand interface. [ABSTRACT FROM AUTHOR]

The rapid development of key technologies for the intelligent research and development (R&D) of new materials has significantly promoted the R&D efficiency and industrialization of materials and attracted global attention. China’s development in this field lags behind the advanced international level in terms of key technologies and infrastructures, which has restricted the original innovation and industrial development of the material sector. This study summarizes the key technologies involving the intelligent R&D of new materials, explores the developing status of these key technologies in China and abroad, and analyzes the challenges faced by China. Moreover, the intelligent R&D technology system is summarized including intelligent computing and design technologies and software, autonomous/intelligent experiment technologies and equipment, artificial-intelligence-driven basic algorithms and technologies, digital twins, intelligent R&D platforms and collaborative innovation networks. Furthermore,countermeasures are proposed from the aspects of innovative ecology construction, industrial environment improvement, standards system establishment, talent training, and international cooperation.

How to accurately design a personalized matching implant that can induce skull regeneration is the focus of current research. However, the design space for the porous structure of implants is extensive, and the mapping relationships between these structures and their mechanical and osteogenic properties are complex. At present, the forward design of skull implants mainly relies on expert experience, leading to high cost and a lengthy process, while the existing inverse design approaches face challenges due to data dependence and manufacturing process errors. This study presents an efficient inverse design method for personalized multilevel structures of skull implants using a machine learning pipeline composed of a finite element method, topological optimization, and neural networks. Based on the mechanical response of the human body falls, this method can tailor multi-level structures for implants in various defect positions. The results show that the proposed method establishes a bidirectional relationship between topological parameters and mechanical properties, enabling the customization of mechanical behavior at low computational cost while accounting for manufacturing errors in the 3D printing process. Additionally, the design results are also mutually consistent with analytical relationships between lattice parameters and the elastic modulus obtained from experiments and finite element simulations. Thus, this study provides a general and practical approach to rapidly design skull osteoinductive implants. [ABSTRACT FROM AUTHOR]

Accurate shale gas reserves estimation is essential for development. Existing machine learning (ML) models for predicting gas isothermal adsorption are limited by small datasets and lack verified generalization. We constructed an "original dataset" containing 2112 data points from 11 measurements on samples from 8 formations in 3 countries to develop ML‐based prediction models. Similar to previous ML models, total organic matter, pressure, and temperature are characterized as the three most significant features using the mean impurity method. In contrast to previous ML models, the study reveals that these three features are inadequate to be used to make reasonable predictions for the datasets from the measurements different from those used to train the models. Instead, the extreme gradient boosting decision trees (XGBoost) model with two more features (specific surface area and moisture) exhibits good robustness, generalization, and precision in the prediction of gas isothermal adsorption. Overall, An XGBoost model with optimal input features is developed in this work, which exhibits both good performance in gas adsorption prediction and good potential for the estimation of gas storage in shale gas development. [ABSTRACT FROM AUTHOR]

To solve the problems of information loss and nonlinear transformation of hyperspectral image dimensionality reduction methods and the problem that recognition methods are sensitive to noise and cannot be trained online, a new hyperspectral image identification method based on locality sensitive discriminant analysis (LSDA) and online sequential extreme learning machine (OSELM) is proposed. Firstly, LSDA is used to reduce the redundant information and data noise, and the local structure and discriminant information of the hyperspectral image are extracted on the basis of reducing the data dimension. Then, a real‐time recognition model based on OSELM is constructed to identify various terrain targets in hyperspectral images through online real‐time updates of data. According to the experimental results of the Pavia University dataset and Salinas dataset, the proposed LSDA‐OSELM not only achieved the maximum overall accuracy (OA) of 97.56 and 97.88 but also obtained the maximum kappa coefficient of 0.9726 and 0.9774. Compared with several new dimensionality reduction methods and identification methods, the proposed LSDA‐OSELM has achieved a leading position in both OA and kappa indicators. The proposed method improves the generalization ability and accuracy of hyperspectral image target recognition models. In the future, its computational complexity and parameter robustness are expected to be further improved and applied in practical application scenarios. [ABSTRACT FROM AUTHOR]

The application of nanomaterials in different plants exerts varying effects, both positive and negative. This study aimed to investigate the influence of carbon nanoparticles (CNPs) on the growth and development of Ficus tikoua Bur. plant. The morphological characteristics, photosynthetic parameters, and chlorophyll content of F. tikoua Bur. plants were evaluated under four different concentrations of CNPs. Results indicated a decreasing trend in several agronomic traits, such as leaf area, branching number, and green leaf number and most photosynthetic parameters with increasing CNPs concentration. Total chlorophyll and chlorophyll b contents were also significantly reduced in CNPs-exposed plants compared to the control. Notably, variations in plant tolerance to CNPs were observed based on morphological and physiological parameters. A critical concentration of 50 g/kg was identified as potentially inducing plant toxicity, warranting further investigation into the effects of lower CNPs concentrations to determine optimal application levels. [ABSTRACT FROM AUTHOR]

Context: The solubility variations of elemental sulfur are of great importance in the prevention of sulfur deposition during the development of high-sulfur gas formations. It has been observed that the solubility varies with H2S content, which is the main solvent of elemental sulfur in natural gas. Moreover, the addition of small amounts of CH4 and/or CO2 in H2S leads to a dramatic solubility reduction of which the mechanism remains unclear. Using a modified direct coexistence method, molecular dynamics simulations are conducted to uncover the molecular mechanism of the solubility reduction. The observed solubility variations with H2S content are reproduced, and the solubility reduction is interpreted by the antisolvent effect of CH4 and CO2. While the H2S content varies in a wide range in the known high-sulfur gas formation, our simulations provide useful information for controlling the sulfur deposition in gas development. Methods: Molecular dynamics simulations are carried out using the LAMMPS package. The initial models are constructed with the Packmol software. The Ballone and Jones' potential is used for S8, the Galliero's potential for H2S and CO2, and the transferable potentials for phase equilibria-united atom (TraPPE-UA) force field for CH4. The time step is set to 1 fs, and the molecular trajectories of additional 2 ns after equilibrium are collected for analysis. [ABSTRACT FROM AUTHOR]