Electrospun porous carbon nanofibers derived from bio-based phenolic resins as free-standing electrodes for high-performance supercapacitors

Phenolic resins were employed to prepare electrospun porous carbon nanofibers with a high specific surface area as free-standing electrodes for high-performance supercapacitors.However,the sustainable development of conventional phenolic resin has been challenged by petroleum-based phenol and formaldehyde.Lignin with abundant phenolic hydroxyl groups is the main non-petroleum resource that can provide renewable aromatic compounds.Hence,lignin,phenol,and furfural were used to synthesize bio-based phenolic resins,and the activated carbon nanofibers were obtained by electrospinning and one-step carbonization activation.Fourier transform infrared and differential scanning calorimetry were used to characterize the structural and thermal properties.The results reveal that the apparent activation energy of the curing reaction is 89.21 kJ·mol–1 and the reaction order is 0.78.The activated carbon nanofibers show a uniform diameter,specific surface area up to 1100 m^(2)·g^(-1),and total pore volume of 0.62 cm^(3)·g^(-1).The electrode demonstrates a specific capacitance of 238 F·g^(-1)(0.1 A·g^(-1))and good rate capability.The symmetric supercapacitor yields a high energy density of 26.39 W·h·kg^(-1)at 100 W·kg^(-1)and an excellent capacitance retention of 98%after 10000 cycles.These results confirm that the activated carbon nanofiber from bio-based phenolic resins can be applied as electrode material for high-performance supercapacitors.

Study on the resolution of multi-aircraft flight conflicts based on an IDQN

With the rapid growth of flight flow,the workload of controllers is increasing daily,and handling flight conflicts is the main workload.Therefore,it is necessary to provide more efficient conflict resolution decision-making support for controllers.Due to the limitations of existing methods,they have not been widely used.In this paper,a Deep Reinforcement Learning(DRL)algorithm is proposed to resolve multi-aircraft flight conflict with high solving efficiency.First,the characteristics of multi-aircraft flight conflict problem are analyzed and the problem is modeled based on Markov decision process.Thus,the Independent Deep Q Network(IDQN)algorithm is used to solve the model.Simultaneously,a’downward-compatible’framework that supports dynamic expansion of the number of conflicting aircraft is designed.The model ultimately shows convergence through adequate training.Finally,the test conflict scenarios and indicators were used to verify the validity.In 700 scenarios,85.71%of conflicts were successfully resolved,and 71.51%of aircraft can reach destinations within 150 s around original arrival times.By contrast,conflict resolution algorithm based on DRL has great advantages in solution speed.The method proposed offers the possibility of decision-making support for controllers and reduce workload of controllers in future high-density airspace environment.