Introducing Ag in Ba_(0.9)La_(0.1)FeO_(3-δ):Combining cationic substitution with metal particle decoration

BaFeO_(3-δ)-derived perovskites are promising cathodes for intermediate temperature solid oxide fuel cells.The activity of these perovskites depends on the number of oxygen vacancies in their lattice,which can be tuned by cationic substitution.Our first-principle calculations show that Ag is a promising substitute for the Fe site,resulting in a reduced oxygen vacancy formation energy compared with the pristine BaFeO_(3-δ).Ag has limited solubility in perovskites,and its introduction generates an Ag metal secondary phase,which influences the cathode performances.In this work,we investigate the matter,using a Ba0:9La0:1Fe_(1-x)AgxO_(3-δ)series of materials as a case study.Acknowledging the limited solubility of Ag in Ba0:9La0:1Fe_(1-x)AgxO_(3-δ),we aim to distinguish the effects of Ag substitution from those of the Ag secondary phase.We observed that Ag substitution increases the number of oxygen vacancies,confirming our calculations,and facilitates the oxygen incorporation.However,Ag substitution lowers the number of holes,in this way reducing the electronic p-type conductivity.On the other hand,Ag metal positively affects the electronic conductivity and helps the redistribution of the electronic charge at the cathode-electrolyte interface.

Editorial for the special issue“Machine learning and artificial intelligence for energy materials”

With the development of advanced methods in the area of machine learning(ML),artificial intelligence(AI)is rapidly revolutionizing many fields and is starting to change the landscapes of physics and chemistry.Over the past decade,AI-assisted designs of novel materials have been gradually reshaping how researchers explore new chemistries for energy conversion and storage.In addition,innovative data-driven techniques have led to unprecedented perspectives on the physics and chemistries of the reactions/processes involved.With this background in mind,this themed issue of“Machine Learning and Artificial Intelligence for Energy Materials”presents a collection of 5 highly selected papers in machine learning and artificial intelligence for energy materials.1–5 By including comprehensive reviews,and original research report,this issue aims at providing a broad overview of the theoretical advancements and applications of related methods in the areas of energy storage and conversion.

Preface

The energy shortage and environment deterioration are fundamental challenges for humanity in this century. Therefore, creating the next generation of highly efficient energy production and storage technologies is becoming crucially important. Among the sustainable energy options,

Sodium-rich NASICON-structured cathodes for boosting the energy density and lifespan of sodium-free-anode sodium metal batteries

Rechargeable sodium metal batteries(SMBs)have emerged as promising alternatives to commercial Li-ion batteries because of the natural abundance and low cost of sodium resources.However,the overuse of metallic sodium in conventional SMBs limits their energy densities and leads to severe safety concerns.Herein,we propose a sodium-free-anode SMB(SFA-SMB)configuration consisting of a sodium-rich Na superionic conductor-structured cathode and a bare Al/C current collector to address the above challenges.Sodiated Na_(3)V_(2)(PO_(4))_(3)in the form of Na_(5)V_(2)(PO_(4))_(3)was investigated as a cathode to provide a stable and controllable sodium source in the SFA-SMB.It provides not only remarkable Coulombic efficiencies of Na plating/stripping cycles but also a highly reversible three-electron redox reaction within 1.0–3.8 V versus Na/Na+confirmed by structural/electrochemical measurements.Consequently,an ultrahigh energy density of 400 Wh kg^(-1)was achieved for the SFA-SMB with fast Na storage kinetics and impressive capacity retention of 93%after 130 cycles.A narrowed voltage window(3.0–3.8 V vs.Na/Na+)further increased the lifespan to over 300 cycles with a high retained specific energy of 320 Wh kg^(-1).Therefore,the proposed SFA-SMB configuration opens a new avenue for fabricating next-generation batteries with high energy densities and long lifetimes.

Positive/Negative Phototropism:Controllable Molecular Actuators with Different Bending Behavior

Herein,a series of molecular actuators based on the crystals of(E)-2-(4-fluorostyryl)benzo[d]oxazole(BOAF4),(E)-2-(2,4-difluorostyryl)benzo[d]oxazole(BOAF24),(E)-2-(4-fluorostyryl)benzo[d]thiazole(BTAF4),and(E)-2-(2,4-difluorostyryl)benzo[d]thiazole(BTAF24)showed unique bending behavior under UV irradiation.The one-dimensional(1D)crystals of BOAF4 and BTAF4 bent toward light,whereas those of BOAF24 and BTAF24 bent away from light.Although the chemical structures of these compounds are similar,the authors found that F···H–C interaction played a key role in the different molecular packing in structures crystals,which led to the positive/negative phototropism of the actuators.Moreover,theoretical calculations were carried out to reveal the mechanical properties of the crystals.Taking advantage of these photomechanical properties,the authors achieved the potential application in pushing objects,as well as enriching and removing pollutants.Hence,the molecular actuators with different bending behavior could be fabricated by introducing different number of F atom,which may open a novel gate for crystal engineering.

Heterointerface of all-alkynyl-protected Au_(28) nanoclusters anchored on NiFe-LDHs boosts oxygen evolution reaction:a case to unravel ligand effect

Ultrasmall Au nanoclusters have been proven to effectively enhance the catalytic performance of NiFe layered double hydroxides(NiFe-LDHs)toward oxygen evolution reaction(OER),yet the surface ligand effect of the Au nanoclusters still remains elusive.Herein,a systematic study is reported to examine the OER performance of NiFe-LDHs supported atom-precise all alkynyl-protected[Au_(28)(~tBuC≡C)17]~-nanoclusters(Au_(28)-Alkynyl in short)and thiolate-protected Au_(28)(TBBT)_(20)(TBBT=4-tert-butylbenzene thiol)counterp arts(Au_(28)-Thiolate in short).The Au_(28)-Alkynyl cluster has characteristic absorbance feature,and its composition is verified by mass spectrometry.It possesses a drastically different structure from the reported mixed ligand protected Au_(28)nanoclusters.Interestingly,the NiFe-LDHs loaded with Au_(28)-Alkynyl exhibited a superior OER performance than the sample loaded with Au28-Thiolate under the same conditions,evidenced by a smaller overpotential of 205 mV at the current density of 10 mA·cm^(-2)and a lower Tafel slope value of 41.0 mV·dec^(-1)in 1 mol·L^(-1)KOH.Such excellent performance is attributed to the interfaces created between the NiFe-LDHs and the Au nanoclusters,as density functional theory calculations reveal that more significant charge transfer occurs in Au_(28)-Alkynyl/NiFeLDHs catalyst,and more importantly,the energy barrier of the potential-determining step in the OER process for Au28-Alkynyl/NiFe-LDHs is much lower than that of Au28-Thiolate/NiFe-LDHs hence favors the electrocatalytic reaction.