Hierarchical N-Doped Porous Carbons for Zn–Air Batteries and Supercapacitors

Nitrogen-doped carbon materials with a large specific surface area,high conductivity,and adjustable microstructures have many prospects for energy-related applications.This is especially true for N-doped nanocarbons used in the electrocatalytic oxygen reduction reaction(ORR)and supercapacitors.Here,we report a low-cost,environmentally friendly,large-scale mechanochemical method of preparing N-doped porous carbons(NPCs)with hierarchical micro-mesopores and a large surface area via ball-milling polymerization followed by pyrolysis.The optimized NPC prepared at 1000°C(NPC-1000)offers excellent ORR activity with an onset potential(Eonset)and half-wave potential(E1/2)of 0.9 and 0.82 V,respectively(vs.a reversible hydrogen electrode),which are only approximately 30 mV lower than that of Pt/C.The rechargeable Zn–air battery assembled using NPC-1000 and the NiFe-layered double hydroxide as bifunctional ORR and oxygen evolution reaction electrodes offered superior cycling stability and comparable discharge performance to RuO2 and Pt/C.Moreover,the supercapacitor electrode equipped with NPC prepared at 800℃ exhibited a high specific capacity(431 F g^−1 at 10 mV s^−1),outstanding rate,performance,and excellent cycling stability in an aqueous 6-M KOH solution.This work demonstrates the potential of the mechanochemical preparation method of porous carbons,which are important for energy conversion and storage.

Co_(3)Mo_(3)N—An efficient multifunctional electrocatalyst

Efficient catalysts are required for both oxidative and reductive reactions of hydrogen and oxygen in sustainable energy conversion devices.However,current precious metal-based electrocatalysts do not perform well across the full range of reactions and reported multifunctional catalysts are all complex hybrids.Here,we show that singlephase porous Co3Mo3N prepared via a facile method is an efficient and reliable electrocatalyst for three essential energy conversion reactions;oxygen evolution reaction(OER),oxygen reduction reaction(ORR),and hydrogen evolution reaction(HER)in alkaline solutions.Co3-Mo3N presents outstanding OER,ORR,and HER activity with high durability,comparable with the commercial catalysts RuO2 for OER and Pt/C for ORR and HER.In practical demonstrations,Co3Mo3N gives high specific capacity(850 mA h gZn^(-1) at 10 mA cm^(-2))as the cathode in a zinc-air battery,and a low potential(1.63 V at 10 mA cm^(-2))used in a water-splitting electrolyzer.Availability of Co and Mo d-states appear to result in high ORR and HER performance,while the OER properties result from a cobalt oxide-rich activation surface layer.Our findings will inspire further development of bimetallic nitrides as cost-effective and versatile multifunctional catalysts that will enable scalable usage of electrochemical energy devices.

Prediction of nature of band gap of perovskite oxides (ABO_(3)) using a machine learning approach

A material's electronic properties and technological utility depend on its band gap value and the nature of band gap(i.e.direct or indirect).This nature of band gaps is notoriously difficult to compute from first principles.In fact it is computationally intense to approximate and also rather time consuming.Hence its prediction represents a challenging problem.Machine learning based approach offers a promising and computationally efficient means to address this problem.Here we predict the nature of band gap for perovskite oxides(ABO_(3))with elemental composition,ionic radius,ionic character and electronegativity.We do this by training machine learning models on computationally generated datasets.Knowing the nature of the band gap of the perovskite oxides(whether direct or indirect)plays a pivotal role in determining whether the perovskite can be used for photovoltaic or photocatalytic applications.A total of 5329 perovskite oxides are considered in this study.Here,we determine the correlation between the nature of band gap and the composition of the perovskite oxide.A Random Forest algorithm is used for predicting the same since it yielded higher accuracy(~91%)compared to the other Machine Learning models.The approach suggested here can be used to predict the nature of bandgap and can also aid in novel materials discovery within the family of perovskites.This is a robust,quick,and low-cost strategy to find novel materials for light harvesting applications in particular.Also we present feature ranking as it pertains to prediction of nature of bandgap and also discuss correlation between the features.We also show feature importance graphs and SHapley Additive exPlanations(SHAP)as is relevant for prediction of nature of band gaps.Using the approach reported,NaPuO_(3) and VPbO_(3) are discovered to be good candidates for solar cell materials(direct band gap~1.5 eV).Novel composition predictions for targeted applications are the future and our model is a step ahead in this direction.