SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4257-y
Hafnia-based ferroelectrics exhibit a distinctive reverse size effect and exceptional scalability, positioning them as critical candidates for CMOS-compatible non-volatile memory and ferroelectric transistors, with substantial promise for advancing hardware acceleration in artificial intelligence and large-data storage technologies. However, their practical deployment is constrained by a longstanding dilemma: the difficulty in simultaneously stabilizing metastable polar phases and ensuring long-term reliability under the high electric fields required for polarization switching. This review reinterprets this challenge through the lens of defect physics and advocates a paradigm shift from stochastic, disorder-mediated defect incorporation toward ordered, multiscale defect engineering. We systematically discuss the collective influence of point defects, line defects, planar defects, and defect-coupled structures on the phase stability, switching kinetics, and failure mechanisms in hafnia-based ferroelectrics. Controlling oxygen-vacancy states, engineering dopants via Fermi-level and chemical pressure, deploying periodic dislocation arrays, designing topological domain walls, functionalizing interfaces, and leveraging flexoelectric strain gradients constitute the core strategic toolkit. Through such ordered defect architectures, scalable performance metrics, including high remanent polarization, low coercive field, fast switching speed, and endurance exceeding 10^12 cycles, become attainable. These approaches establish a set of design principles for next-generation low-power, high-reliability ferroelectric electronics.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202506020
The rotary kiln roasting of lepidolite for lithium extraction faces challenges of unstable lithium conversion rates and high energy consumption. To address this, a multi-objective optimization method coupling improved neural network simulation with a multi-objective genetic algorithm was proposed, targeting the synergistic optimization of lithium conversion rate (TRLi) and natural gas consumption intensity (EIng). Using long-term industrial time-series data of batching parameters and kiln operating variables, back-propagation (BP) neural network and its particle swarm optimization (PSO) improved variant were developed to model TRLi and EIng. The PSO-BP model demonstrated superior accuracy in capturing the complex nonlinear relationships, reducing mean absolute percentage errors (MAPE) to 0.278 and 0.284 for TRLi and EIng, respectively. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to construct a multi-objective optimization model, yielding a Pareto-optimal set of process parameters that maximize TRLi and minimize EIng. The results revealed that under NSGA-II optimized conditions, TRLi could be stabilized between 82.45% and 87.96%, an average increase of 3.61 percentage points over baseline operations, while EIng could be reduced to 53.7 m3 per ton of clinker. For an annual processing capacity of 3.2×105 tons of lepidolite concentrate and sulfate mixture, this corresponds to an additional 127.1 tons of lithium metal recovery, a reduction of 1,964,912 m3 in natural gas consumption, and a decrease of 3,763.84 tons in CO2 emissions annually. This study provides theoretical and technical support for the green, high-quality, and low-carbon supply of critical raw materials for the lithium battery new energy industry.
The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.226111
Efficient capture of the greenhouse gas nitrous oxide (N2O) is critical for climate change mitigation and resource recovery. In this study, two guanidinium-based hydrogen-bonded organic frameworks (HOFs) with pyridyl nitrogen site isomerism, namely G-5,5'-BPyDC and G-4,4'-BPyDC, were constructed using 2,2'-bipyridine-5,5'-dicarboxylic acid and 2,2'-bipyridine-4,4'-dicarboxylic acid as ligands. The effects of ligand structure on hydrogen-bonded network, pore environment, and N2O/N2 adsorption separation performance were systematically investigated via single-crystal X-ray diffraction, thermogravimetric analysis, Hirshfeld surface analysis, and gas adsorption experiments. Both frameworks are built via N-H...O hydrogen bonds. The asymmetric unit of G-5,5'-BPyDC contains two methanol molecules, resulting in larger free volume and surface area compared to G-4,4'-BPyDC, which exhibits more compact packing. Both materials show decomposition temperatures above 290°C, indicating good thermal stability. Hirshfeld surface analysis reveals that the total contribution of O-H/H-O and N-H/H-N hydrogen bonds in G-5,5'-BPyDC (32.0%) is higher than that in G-4,4'-BPyDC (29.7%). At 25°C and 4.0 MPa, the N2O adsorption capacity of G-5,5'-BPyDC is 2.32 mmol/g, surpassing that of G-4,4'-BPyDC (2.02 mmol/g). IAST calculations show that the selectivities of G-5,5'-BPyDC for N2O/N2 (50:50 and 10:90) mixtures reach 29.26 and 111.32, respectively, significantly superior to those of G-4,4'-BPyDC (6.61 and 17.03). Pyridyl nitrogen site isomerism effectively optimizes N2O/N2 adsorption and separation by modulating pore polarity and hydrogen-bonded network, offering a new strategy for isomer design.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202510013
To investigate the spatiotemporal distribution of nutrients and chlorophyll-a (Chl-a) in Dongping Lake, a coupled hydrodynamic-water quality-ecological model was developed using Delft3D. The model simulated total nitrogen (TN), nitrate nitrogen (NO3-N), ammonia nitrogen (NH4-N), total phosphorus (TP), soluble reactive phosphorus (SRP), and Chl-a. After validation, the model systematically analyzed the spatiotemporal patterns and influencing factors, revealing nitrogen and phosphorus transformation pathways. Results showed three temporal phases: relatively stable concentrations from January to April, significant fluctuations from May to August, and gradual stabilization from September to December, with peak timing varying among indicators. Spatially, concentrations were generally higher in the south and lower in the north, but NH4-N, TP, and Chl-a exhibited reverse patterns (higher in north) during certain periods. External inputs, primarily from the Dawen River, dominated the overall distribution, while water temperature, dissolved oxygen, and hydrodynamic conditions further modulated internal variability. Nitrogen and phosphorus showed distinct fates: nitrogen was primarily removed via denitrification and anammox, whereas phosphorus tended to transform into particulate forms and remained in the lake for extended periods. These findings provide scientific support for precise water quality management in Dongping Lake.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202509124
To reveal the spatiotemporal evolution and driving mechanisms of water quality in the Hanjiang River Basin, this study utilized monthly water quality monitoring data from 54 sections from January 2021 to April 2024. Methods including single-factor index, comprehensive water quality index (WQI), principal component analysis (PCA), and optimal parameters-based geographical detector (OPGD) were employed. Results indicated significant spatiotemporal differences, with total nitrogen (TN), chemical oxygen demand (COD), and permanganate index (CODMn) as major pollutants, TN being the most critical. Temporally, agricultural non-point source organic pollution dominated in wet season, while comprehensive organic pollution with industrial point source characteristics prevailed in dry season. Spatially, water quality deteriorated along the main stream, with tributary downstream areas showing severe pollution, forming a pattern of 'mountainous areas good, plains poor'. OPGD revealed combined effects of natural conditions and human activities, proposing a 'zonal control and targeted treatment' strategy.