SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-4012-4
Stimuli-responsive fluorescent hydrogels, owing to their tunable optical properties and unique smart response characteristics, have significant potential in encryption applications and information security. However, most current systems are limited to single-stimulus responsiveness and lack the capability for programmable information erasure or multi-modal dynamic synergy. Hence, we propose a multi-stimuli-responsive phase-change hydrogel incorporating aggregation-induced emission hydrophobic carbon dots (AIE-HCDs) and polyethylene glycol (PEG)-cellulose network, demonstrating dynamic fluorescence chromism under various external triggers. The hydrogel exhibits solvent-exchange-triggered fluorescence color changes from blue to red, enabled by the concentration modulation of AIE-HCDs through the exchange between PEG and water. Additionally, the temperature-induced phase transition of PEG from crystalline to molten state modulates the aggregation and dispersion of AIE-HCDs, thereby enabling dynamic fluorescence color changes. The phase transition further confers excellent shape-memory behavior and adjustable mechanical properties, with the tensile modulus varying from 6.28 MPa in the molten state to 36.23 MPa in the crystalline state, while maintaining high transparency (~88% in the molten state). By utilizing micro-contact printing and the multi-stimulus response, an encryption platform enables information to be hidden, selectively read under sequential stimuli (thermal, UV, and solvent), and completely erased upon demand. This strategy demonstrates significant potential for advancing high-level information encryption and anti-counterfeiting technologies.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202608006
Organic waste is a potential phosphorus reservoir, and understanding the dynamics of available phosphorus (AP) during its resource utilization is critical for efficient phosphorus recovery. Composting, a key route for organic waste valorization, involves complex transformations of phosphorus alongside organic matter degradation and humification. However, the long duration and high cost of composting experiments, coupled with multifactorial influences, hinder efficient elucidation of AP dynamics via conventional methods. This study compiled data from 33 publications, constructing a dataset of 647 samples. Data preprocessing included iterative imputation, one-hot encoding, and standardization. A stacking ensemble learning model was developed to predict AP generation during composting. The optimal ensemble comprised XGBoost and SVR as base learners and ElasticNet as the meta-learner, achieving R² values of 0.954 and 0.928 on training and test sets, respectively, with low overall error. SHAP analysis revealed that key factors influencing AP content, in descending order of importance, were feedstock type, bulking agent type, turning interval, pH, electrical conductivity (EC), and C/N ratio. Notably, livestock manure as feedstock and straw-based bulking agents contributed positively to AP predictions. Partial dependence plots indicated that lower pH and C/N ratios generally favored AP accumulation throughout composting. During the initial stage, higher moisture content and lower EC enhanced AP; in the thermophilic phase, higher temperatures corresponded to higher AP; and during cooling and maturation, maintaining moisture below 48% and C/N below 14, while extending composting beyond 43 days, promoted AP accumulation. This study demonstrates accurate AP prediction via stacking ensemble learning and identifies critical factors, offering support for optimizing phosphorus management in composting engineering.