Silver nanoparticles (AgNPs) were synthesized through an environmentally friendly method with tea extract as a reduction agent. By immobilizing them on the surface of a low-cost pencil graphite electrode (PGE) with the aid of a simple and well-controlled in-situ electropolymerization method, a novel nanosensing interface for nitrite was constructed. The film-modified PGE showed good electrocatalytic effects on the oxidation of nitrite and was characterized through scanning electron microscopy, X-ray photoelectron spectroscopy, and electrochemical techniques. Characterization results clearly show that the successful modification of AgNPs improved the surface area and conductivity of PGEs, which is beneficial to the high sensitivity and short response time of the nitrite sensor. Under the optimal detection conditions, the oxidation peak current of nitrite had a good linear relationship with its concentration in the range of 0.02–1160 μmol/L with a detection limit of 4 nmol/L and a response time of 2 s. Moreover, the sensor had high sensitivity, a wide linear range, a good anti-interference capability, and stability and reproducibility. Additionally, it can be used for the determination of nitrite in food.

In the leveling process of ultra-high-strength steel plates, sample scarcity—driven by high prototyping costs and small-batch production—coupled with a narrow and unevenly distributed feasible region due to high yield-to-tensile ratios and limited ductility, impedes the balanced optimization of plate shape quality and energy consumption. To address this issue, this paper develops an optimization framework for the balanced trade-off between these two objectives. First, a high-precision response surface model based on Box–Behnken experimental design and finite element simulation was constructed using initial wave height, entry roll reduction, exit roll reduction, and leveling speed as key process parameters; peak residual stress difference (characterizing potential sheet quality) and leveling energy consumption as co-optimization objectives; and post-leveling flatness as a constraint. Next, by introducing the NSGA-II multi-objective genetic algorithm, the Pareto optimal solution set for the quality and energy efficiency objectives was obtained, clearly revealing the trade-off relationship between the two; furthermore, the TOPSIS decision-making method was employed to select the comprehensive optimal process scheme that achieves a balance between quality and energy efficiency from the Pareto solution set. An adaptive recommendation curve for the leveling process parameters of MS1500 ultra-high-strength steel plates was established, covering an initial wave height range of 10.5–14.6 mm, thereby enabling intelligent parameter matching based on different incoming material conditions. Finally, industrial validation demonstrated that this optimized scheme significantly reduced leveling energy consumption while ensuring that post-leveling flatness meets the high-quality requirement of less than 3.5 mm·m⁻¹. This achieves a balanced optimization of quality and energy efficiency. This study provides a reliable theoretical basis and practical engineering solution for the efficient and environmentally friendly leveling production of ultra-high-strength steel.