Synthesizing polynitrogen compounds that remain stable at ambient conditions is particularly challenging because species beyond the N≡N triple bond are inherently unstable.In this study,we combine first-principles ca...Synthesizing polynitrogen compounds that remain stable at ambient conditions is particularly challenging because species beyond the N≡N triple bond are inherently unstable.In this study,we combine first-principles calculations with a machine-learning potential(MLP)to investigate the ambient stability of planar cyclo-N_(4) units embedded in a two-dimensional t-FeN_(4) monolayer.Our results show that strong Fe–N coordination inhibits N≡N reformation,enabling the square cyclo-N_(4) motif to remain dynamically stable and covalently bonded without high-pressure synthesis.Furthermore,this structure exhibits tunable magnetic anisotropy and a Néel temperature above 600 K,indicating potential for room-temperature spintronic applications.The MLP also enables the simulation of systems comprising over 100,000 atoms,including periodic sheets,nanoribbons,nanomatrices and nanosheets,revealing their structural integrity under thermal fluctuations.These results demonstrate that two-dimensional confinement provides a promising route to stabilize exotic nitrogen topologies,linking quantum-mechanical accuracy with mesoscale modelling for future spinbased technologies.展开更多
基金support from Chongqing Jiaotong University (No. F1240018)Chongqing Graduate Tutor Team Construction Project (JDDSTD2022006)+1 种基金P.L. would like to thank the support from the Hunan Provincial Natural Science Foundation of China (2023JJ40621)Computing resources were provided by the National Supercomputer Centre (TianHe-3K) in Tianjin. The work also partially supported by e-INFRA CZ (ID:90140) for providing computational resources.
文摘Synthesizing polynitrogen compounds that remain stable at ambient conditions is particularly challenging because species beyond the N≡N triple bond are inherently unstable.In this study,we combine first-principles calculations with a machine-learning potential(MLP)to investigate the ambient stability of planar cyclo-N_(4) units embedded in a two-dimensional t-FeN_(4) monolayer.Our results show that strong Fe–N coordination inhibits N≡N reformation,enabling the square cyclo-N_(4) motif to remain dynamically stable and covalently bonded without high-pressure synthesis.Furthermore,this structure exhibits tunable magnetic anisotropy and a Néel temperature above 600 K,indicating potential for room-temperature spintronic applications.The MLP also enables the simulation of systems comprising over 100,000 atoms,including periodic sheets,nanoribbons,nanomatrices and nanosheets,revealing their structural integrity under thermal fluctuations.These results demonstrate that two-dimensional confinement provides a promising route to stabilize exotic nitrogen topologies,linking quantum-mechanical accuracy with mesoscale modelling for future spinbased technologies.
