Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is...Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.展开更多
建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证...建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证，同位素内标法定量。在1～40μg / L 范围内氟乐灵农药的线性关系良好；方法定量限（LOQ）为0.02μg / kg；对鳗鱼、烤鳗、梭子蟹、小龙虾、猪油和橄榄油等6种复杂基质进行1.0、2.0和3.0μg / kg 等3个水平的添加回收试验，平均回收率均处于80％～100％之间，RSD≤10.3％；无干扰现象出现。该方法可作为水产品及食用油中氟乐灵残留检测的确证方法。展开更多
基金support from National Natural Science Foundation of China(Grant Nos.22275145,22305189and 21875184)Natural Science Foundation of Shaanxi Province(Grant Nos.2022JC-10 and 2024JC-YBQN-0112).
文摘Two-dimensional energetic materials(2DEMs),characterized by their exceptional interlayer sliding properties,are recognized as exemplar of low-sensitivity energetic materials.However,the diversity of available 2DEMs is severely constrained by the absence of efficient methods for rapidly predicting crystal packing modes from molecular structures,impeding the high-throughput rational design of such materials.In this study,we employed quantified indicators,such as hydrogen bond dimension and maximum planar separation,to quickly screen 172DEM and 16 non-2DEM crystal structures from a crystal database.They were subsequently compared and analyzed,focusing on hydrogen bond donor-acceptor combinations,skeleton features,and intermolecular interactions.Our findings suggest that theπ-πpacking interaction energy is a key determinant in the formation of layered packing modes by planar energetic molecules,with its magnitude primarily influenced by the strongest dimericπ-πinteraction(π-π2max).Consequently,we have delineated a critical threshold forπ-π2max to discern layered packing modes and formulated a theoretical model for predictingπ-π2max,grounded in molecular electrostatic potential and dipole moment analysis.The predictive efficacy of this model was substantiated through external validation on a test set comprising 31 planar energetic molecular crystals,achieving an accuracy of 84%and a recall of 75%.Furthermore,the proposed model shows superior classification predictive performance compared to typical machine learning methods,such as random forest,on the external validation samples.This contribution introduces a novel methodology for the identification of crystal packing modes in 2DEMs,potentially accelerating the design and synthesis of high-energy,low-sensitivity 2DEMs.
文摘建立了一种可用于水产品及食用油中氟乐灵残留量分析的分散型固相萃取-气相色谱-负化学离子源质谱方法。水产品及食用油经乙腈提取，4℃冷藏后，采用分散型固相萃取法净化，由气相色谱-负化学离子源质谱选择离子监测技术进行测定与确证，同位素内标法定量。在1～40μg / L 范围内氟乐灵农药的线性关系良好；方法定量限（LOQ）为0.02μg / kg；对鳗鱼、烤鳗、梭子蟹、小龙虾、猪油和橄榄油等6种复杂基质进行1.0、2.0和3.0μg / kg 等3个水平的添加回收试验，平均回收率均处于80％～100％之间，RSD≤10.3％；无干扰现象出现。该方法可作为水产品及食用油中氟乐灵残留检测的确证方法。
