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The corrosion inhibition performance of three choline amino acid ionic liquids ([Ch][AA]ILs) for mild steel in 1 M HCl solution was evaluated by electrochemical analysis, gravimetric measurement, and surface morphological characterization in the temperature range of 298–328 K in this paper. The results of the polarization experiments show that [Ch][AA]ILs affect the cathode and anode reaction processes, proving that [Ch][AA]ILs are mixed corrosion inhibitors. Choline phenylalanine ionic liquids ([Ch][Phe]) exhibit the highest corrosion prevention efficiency among the three chosen corrosion inhibitors, as proven by SEM/EDS scanning, XPS analysis, and molecular dynamics simulation. The values of standard free energy of adsorption Δ G ads range between − 20 kJ·mol-1 and − 40 kJ·mol-1, showing that inhibitor molecule adsorption on the metal surface involves both physical and chemical adsorption. According to the results of XPS research and quantum chemical calculations, the adsorption of corrosion inhibitor molecules on the metal surface is strongly related to the anions. The corrosion inhibitor molecules form a thick monolayer adsorption layer on the metal surface, preventing metal-corrosive media interaction. The molecular dynamics simulation findings reveal that the corrosion inhibitor molecules replace the solvent water or any other ions pre-adsorbed on the metal surface during the adsorption process, protecting the metal from corrosion. [Display omitted] [ABSTRACT FROM AUTHOR]

To address climate change, the Chinese government has committed to achieving carbon peaking by 2030. Projecting the wind power and photovoltaic installed capacity is essential for China's low carbon transition as these renewables have been widely recognized as the major energy sources in future. This study proposes a long-term strategic planning approach for wind power and photovoltaic by simulating multiple policies and market scenarios for the national-level energy transitions and incorporating the feedback effects of market development on technology readiness level. The proposed approach simulates the national energy consumption and energy mix under various transition scenarios based on the Long-range Energy Alternatives Planning System (LEAP). The dynamics of market development, technology readiness level, and future renewable costs are further incorporated into a nonlinear optimization model to generate economically optimal planning solutions for wind power and photovoltaic under different policy and market scenarios. Valuable insights are obtained from three perspectives, i.e., (1) the economic and emission reduction co-benefits under different scenarios, (2) the evolution of the Levelized Cost of Energy, and (3) the impact on the transition of power sector. In particular, the simulation and optimization results reveal that appropriate acceleration of wind power and photovoltaic development can promote technology readiness level, reduce overall transition costs, and effectively reduce the peak value of emissions. The maximum peak reduction in carbon dioxide emissions is 16.06%, and the maximum cumulative reduction in carbon dioxide emissions is 14.54%. The total project cost can be reduced by a maximum of 3.23%. Thus, more active supporting policies for renewables can further enhance long-term economic and environmental co-benefits under current conditions. [Display omitted] • Quantify the market's dynamic feedback on renewable costs through learning curves. • Incorporate technology readiness level into energy long-term renewable planning. • Optimize long-term renewable planning for different policy and market conditions. • Active wind and solar power investments now offer larger co-benefits in future. • CO 2 peak down 16.06% and the cumulative CO 2 reduction 14.54%. [ABSTRACT FROM AUTHOR]