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The purpose of this paper is to study the effect of nano polypyrrole-modified boron nitride on the performance of phosphate film.

By adding polypyrrole-modified boron nitride to the phosphate solution, a phosphate film is formed on the metal surface, improving its corrosion resistance. The effect of different concentrations of polypyrrole-modified boron nitride on the corrosion resistance of Q235 carbon steel surface was studied. The corrosion resistance of the phosphate film was evaluated using the copper sulfate drop test. The electrochemical corrosion performance of the phosphate film was assessed using the weak polarization curve method and electrochemical impedance spectroscopy. The surface of the samples was characterized using scanning electron microscopy and X-ray diffraction analysis.

The results show that samples containing polypyrrole-modified boron nitride have a denser and more uniform phosphate film. When the concentration of polypyrrole-modified boron nitride is 0.6 g/L, the drop time of copper sulfate on the formed phosphate film can reach 219 s, which is a 189% increase compared to the performance of the sample without the additive. The current density is 1.06 × 10⁻⁶ A/cm² lower than that of the pure phosphate film, indicating the best corrosion resistance. Polypyrrole-modified boron nitride effectively promotes the formation of the phosphate film.

This study used the modification of phosphate solution using nanoparticles to investigate the influence of different nanoparticle concentrations on the phosphate film. The corrosion resistance of the phosphate film was enhanced, providing a method and theoretical guidance for the improvement of phosphate solution formulation.

This paper aims to study the present situation of Tianjin industrial energy consumption carbon emissions and put forward constructive suggestions for future energy-saving emission reduction work.

Using the energy consumption data form the Tianjin’s Industrial Energy Efficiency Guide (TJBS, 2009-2013) and Tianjin’s Statistical Yearbook (NBS, 2006-2012), some models were able to predict the future with a high degree of accuracy.

With an average error of 3.06 per cent for the logistic regression model and an average error of 2.03 per cent for the gray model, the R2 for the energy elasticity model is 0.99158. It also indicated that between 2008 and 2012, the energy consumption per unit of industrial added value decreased by approximately 33.61 per cent. These results show that energy-saving efforts and the optimization of the industrial structure have increased the energy efficiency of Tianjin.

The authors think that their contribution refers to a combination between methodology of forecasting and industrial energy consumption.