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Verified CAS / Academic Author2 Decoded Studies

Prof. HU Xiaoying

North China Electric Power University

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Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4112-6

Highly Efficient Removal of Sr2+ by a Layered Potassium Phosphatoantimonate under Neutral and Acidic Conditions

Radiostrontium remediation is crucial for ecological protection and sustainable development of nuclear energy. However, efficient removal of 90Sr from complex radioactive liquid waste, especially under acidic conditions, remains challenging due to material instability and intense proton competition. Herein, the rapid and highly selective capture of Sr2+ in neutral and even acidic solutions has been achieved by a layered potassium phosphatoantimonate KSbP2O8 with excellent radiation and thermal stability. Under neutral conditions, it possesses high maximum adsorption capacity (qmSr = 110.25 mg g−1), rapid adsorption kinetics (the removal rate (RSr) of 91.54% within 30 min), and excellent selectivity for Sr2+, and facile regeneration. Particularly, even under acidic conditions (pH 2.0), KSbP2O8 still maintains excellent Sr2+ removal capacity (qmSr = 79.38 mg g−1), fast kinetics, and high selectivity. A mechanism study by multiple characterizations reveals that the efficient Sr2+ removal of KSbP2O8 mainly stems from ion exchange between Sr2+ and interlayer K+ in KSbP2O8, which is attributed to the synergy between the Sb5+-induced Brønsted acidity and the high charge density of the anionic framework. This study demonstrates the exceptional capability of phosphatoantimonates to selectively capture Sr2+ under acidic conditions, highlighting the potential of phosphatoantimonates as effective scavengers for radiostrontium remediation.

Journal of Fuel Chemistry and Technology2026DOI: 10.1016/S1872-5813(26)60680-9

High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks

Low-temperature chemical looping air separation (CLAS) is a promising technology for producing oxygen-enriched gas streams, utilizing the redox properties of solid oxygen carriers to selectively capture and release oxygen from air. Oxygen vacancy formation energy (Eovf) is a key descriptor for evaluating the ease of oxygen release. In this study, the applicable range of Eovf for CLAS oxygen carriers was determined to be <2.3 eV via thermodynamic calculations. A graph neural network (GNN) model, specifically the ALIGNN architecture, was trained to predict Eovf with a mean absolute error (MAE) of 0.26 eV on the test set. Using this model, a high-throughput screening of 3,649 compositions of SrxA1−xFeyB1−yO3 perovskites was conducted to identify promising CLAS oxygen carriers. The predictions revealed that doping with Ba and Ca at the A-site and Co at the B-site effectively reduces Eovf. The screening criterion of Eovf < 2.3 eV successfully rediscovered several previously reported low-temperature CLAS oxygen carriers, validating the approach. This work demonstrates that GNN-based Eovf prediction can significantly accelerate the discovery of CLAS materials, with broader implications for other chemical looping applications such as full oxidation and syngas production.