Enhanced photocatalytic N2 fixation by promoting N2 adsorption with a co-catalyst

Photocatalytic N2 fixation involves a nitrogen reduction reaction on the surface of the photocatalyst to convert N2 into ammonia.Currently,the adsorption of N2 is the limiting step for the N2 reduction reaction on the surface of the catalyst.Based on the concept of photocatalytic water splitting,the photocatalytic efficiency can be greatly enhanced by introducing a co-catalyst.In this report,we proposed a new strategy,namely,the loading of a NiS co-catalyst on CdS nanorods for photocatalytic N2 fixation.Theoretical calculation results indicated that N2 was effectively adsorbed onto the NiS/CdS surface.Temperature programmed desorption studies confirmed that the N2 molecules preferred to adsorb onto the NiS/CdS surface.Linear sweep voltammetry results revealed that the overpotential of the N2 reduction reaction was reduced by loading NiS.Furthermore,transient photocurrent and electrochemical impedance spectroscopy indicated that the charge separation was enhanced by introducing NiS.Photocatalytic N2 fixation was carried out in the presence of the catalyst dispersed in water without any sacrificial agent.As a result,1.0 wt% NiS/CdS achieved an ammonia production rate of 2.8 and 1.7 mg L-1 for the first hour under full spectrum and visible light(λ>420 nm),respectively.The catalyst demonstrated apparent quantum efficiencies of 0.76%,0.39% and 0.09% at 420,475 and 520 nm,res pectively.This study provides a new method to promote the photocatalytic efficiency of N2 fixation.

A precise theoretical method for high-throughput screening of novel organic electrode materials for Li-ion batteries

Organic electrode materials have gained significant attention due to their flexibility,lightweight characteristics,abundant resources in nature,and low CO_(2) emission.It's urgently needed for setting up an accurate high-throughput screening theoretical scheme that could find out possible candidates of electrode materials.Currently,the error between the theoretical potentials calculated by the PBE-D2(DFT-D2,dispersion-corrected density functional theory)method and the experimental values is larger than 12%.Thus,it's essential to finding a more accurate method.In the present work,hybrid functionals and vdW correction methods are applied to investigate six reported organic electrode materials for Li-ion batteries.The results show that the hybrid functional combined with the D2 dispersion corrected method,i.e.,HSE06-D2(Heyd,Scuseria,and Ernzerhof,dispersion-corrected),is able to predict the potential of the organic material precisely with an average error of approximately 5%.This method occupies much hardware resources and being very time consuming,but it could be applied as the final ultrafine step in the high-throughput screening program.

Interface engineering of plasmonic induced Fe/N/C-F catalyst with enhanced oxygen catalysis performance for fuel cells application

The low intrinsic activity of Fe/N/C oxygen catalysts restricts their commercial application in the fuel cells technique;herein,we demonstrated the interface engineering of plasmonic induced Fe/N/C-F catalyst with primarily enhanced oxygen reduction performance for fuel cells applications.The strong interaction between F and Fe-N4 active sites modifies the catalyst interfacial properties as revealed by X-ray absorption structure spectrum and density functional theory calculations,which changes the electronic structure of Fe-N active site resulting from more atoms around the active site participating in the reaction as well as super-hydrophobicity from C–F covalent bond.The hybrid contribution from active sites and carbon support is proposed to optimize the three-phase microenvironment efficiently in the catalysis electrode,thereby facilitating efficient oxygen reduction performance.High catalytic performance for oxygen reduction and fuel cells practical application catalyzed by Fe/N/C-F catalyst is thus verified,which offers a novel catalyst system for fuel cells technique.

Predicting metal-organic frameworks as catalysts to fix carbon dioxide to cyclic carbonate by machine learning

The process of discovering and developing new materials currently requires considerable effort,time,and expense.Machine learning(ML)algorithms can potentially provide quick and accurate methods for screening new materials.In the present work,the features of the metal organic frameworks(MOFs)as a catalyst for fixing carbon dioxide into cyclic carbonate were extracted to build a data set,which were collected from the experimental results of approximately 100 published papers.Classifiers were trained with the data set with various ML algorithms,including support vector machine(SVM),K-nearest neighbor classification(KNN),decision trees(DT),stochastic gradient descent(SGD),and neural networks(NN),to predict the catalytic performance.The ML models were trained on 80% of the data set and then tested on the remaining 20%to predict the carbon dioxide fixation ability.The trained ML model was extended to explore 1311 hypothetical MOFs,and some structures displayed a strong catalytic ability.Finally,the six best metal ions(Mn,V,Cu,Ni,Zr and Y)and four best ligands(tactmb,tdcbpp,TCPP,H_(3)L)were determined.These six metals and four ligands could be combined into 24 MOFs,which are strongly potential catalysts for carbon dioxide fixation.Using machine learning methods can speed up the screening of materials,and this methodology is promising for application not only to MOFs as catalysts but also in many other materials science projects.