An adequate water quality prediction mobile system is crucial for real-time, proactive, and convenient water environment monitoring through mobile devices to reduce or prevent water environmental threats. After exploring the feasibility and superiority of the LSTM-seq2seq model for predicting various water quality indicators, the optimal time step range for different length predictions was proposed. To verify the generalizability and reusability of the model, the performance differences of migrating models was investigated. Based on the entire process, we have developed a cost-effective, widely applicable, and sustainable operational prediction system framework. It was successfully applied in the Huangshui River Basin for two years. Results indicated that the model can achieve an NSE of above 0.5 for indicators with high coefficient of variation and above 0.75 for more stable indicators. When carrying out transfer applications, the model can achieve an NSE performance of above 0.5 for most sites in short to medium-term forecasting.

Graphene foams are promising three-dimensional (3D) architectures with the combination of the intrinsic nature of graphene and unique cellular structures for various realms. Herein, a facile technique is developed by combining supramolecular assembly with lyophilization to functionalize graphene with donor−π-acceptor (D−π-A) molecules and then massively transform the two-dimensional (2D) plane nanosheets into 3D foams. The as-prepared gas sensors work at room temperature (RT) and reveal comprehensive gas sensing performance with an ultrahigh response (R _a/R _g = 3.2, 10 ppm), excellent selectivity, and reliable repeatability toward NO ₂. Notably, a gas sensing enhancement mechanism with density functional theory (DFT) calculations is proposed to unravel the synergetic effect of the “Greater Electron Transferring Area” and the specific 3D foam structure for the enhancement of charge transfer and NO ₂ adsorption. The combination of supramolecular assembly and the lyophilization technique provides a strategy to prepare 3D architectural graphene-based materials for high-performance gas sensors and chemical trace detectors.