C60高聚物复合固体材料的反饱和吸收过程研究

本文报导了新型材料C_(60)α-甲基苯乙烯/苯乙烯共聚物薄板的制作,并利用530nm,10ns激光研究此固体材料的激光透过特性.观测到反饱和吸收过程,即实现了光限制效应.     

机器学习与分子模拟协同的CH4/H2分离金属有机框架高通量计算筛选

在减少CO₂排放、实现碳中和的背景下, 金属有机框架(MOFs)在清洁能源领域展现出广阔应用前景. 提出一种机器学习和分子模拟协同的分层筛选策略, 快速、准确地从134185个假设MOFs中识别出具有最佳CH₄/H₂分离性能的吸附剂. 首先, 根据MOFs的结构性质, 筛掉孔径或体积比表面积不恰当的吸附剂, 初筛后MOFs的数量减至62278个. 接下来, 抽取10% MOFs将结构和化学混合描述符作为特征, 利用随机森林分别构建变压吸附和真空变压吸附过程中其对CH₄的吸附剂性能得分(APS)预测模型. 相比于其他模型构建策略, 基于本策略构建的模型具有最优预测准确性, 可用于余下MOFs的性能预测. 随后根据APS预测值排序, 筛选出Top 1000的MOFs并利用分子模拟修正预测结果, 进一步确定了10个最佳MOFs. 最后, 对描述符的重要性进行解释, 揭示了实现模型在不同操作场景下的迁移具有潜力, 为未来开发适用于多操作场景下的高性能MOFs筛选方法提供了一条高效的研究路径和方法.     

pH-Responsive Near-Infrared Emitting Gold Nanoclusters

Near-infrared (NIR) fluorophores with pH-responsive properties suggest merits in biological analyses. This work establishes a general and effective method to obtain pH-responsive NIR emissive gold nanoclusters by introducing aliphatic tertiary amine (TA) groups into the ligands. Computational study suggests that the pH-responsive NIR emission is associated with electronic structure change upon protonation and deprotonation of TA groups. Photo-induced electron transfer between deprotonated TA groups and the surface Au-S motifs of gold nanoclusters can disrupt the radiative transitions and thereby decrease the photoluminescence intensity in basic environments (pH=7–11). By contrast, protonated TA groups curb the electron transfer and restore the photoluminescence intensity in acidic environments (pH=4–7). The pH-responsive NIR-emitting gold nanoclusters serve as a specific and sensitive probe for the lysosomes in the cells, offering non-invasive emissions without interferences from intracellular autofluorescence.     

表面吸附碱金属原子的超短碳纳米管体系的结构和非线性光学性质

采用密度泛函理论(DFT)方法系统研究了表面吸附碱金属Li原子的超短碳纳米管([8]cyclophenacene)体系的结构和非线性光学性质.在这些体系中,Li原子均能稳定地吸附在超短碳纳米管表面,其吸附能高达84.0~106.2 kJ/mol.当吸附1~2个Li原子时,Li原子和碳纳米管之间发生了明显的电荷转移过程,使体系的一阶超极化率(β0)值明显改善,β0值从0迅速增加到3.42×103~8.29×103a.u.;当吸附的Li原子数增加到3时,体系内部产生了额外电子,有效地降低了体系最主要跃迁的跃迁能,使体系的一阶超极化率进一步提升(高达2.59×106a.u.).此外,Li原子之间的距离也是影响吸附体系β0值的重要因素.     

Theoretical investigation on structures,stability and properties of [P,X, Y] (X=C,Si; Y = O,S) isomers

The structures, energetics and stability of the [P, X, Y] (X?=?C, Si; Y?=?O, S) radicals are explored by means of the density functional theory and ab initio levels. Seventeen [P, X, Y] isomers and 14 interconversion transition states are obtained at the B3LYP/6-311G(d) level. At the CCSD(T)/6-311?+?G(2df)//QCISD/6-311G(d)?+?ZPVE level, the lowest-lying isomers are the linear PCO 1a (0.0?kcal/mol), PCS 1b (0.0) and the three-membered ring cPSiO 1c (0.0), cPSiS 1d (0.0) on their respective potential energy surfaces. These four isomers exhibit considerably not only thermodynamic but also kinetic stabilities. Additionally, the cyclic cPCS 2b (32.8) and linear PSiS 2d (18.6) possess also high kinetic stability. All of six isomers 1a, 1b, 2b, 1c, 1d and 2d are considerably stabilized by a barrier of at least 20?kcal/mol, and may be detected in the laboratory or interstellar space. Their valence bond structures and possible formation strategies in the laboratory and space are discussed in detail. Finally, the similarities and discrepancies on structures and stabilities between [P, X, Y] (X?=?C, Si; Y?=?O, S) isomers are compared. These predicted results are highly expected to be informative for the future identification of [P, X, Y] (X?=?C, Si; Y?=?O, S) in the laboratory and space.     

碱金属原子吸附PPV及其衍生物体系的结构和非线性光学性质的理论研究

采用密度泛函理论(DFT)方法系统研究了碱金属Li原子吸附亚苯基-1,2-亚乙烯基(Phenylenevinylene)聚合物(PPV)及其衍生物(具有给受体基团修饰的)体系的结构和非线性光学性质.Li原子能稳定地吸附在PPV及其衍生物的表面,吸附能高达62.3~78.2 kJ/mol.当碱金属Li原子吸附在[PPV]n(n=2~4)表面时,锂盐效应导致了Li原子和[PPV]n之间发生了明显的电荷转移过程,使体系的一阶超极化率β0从249~756 a.u.明显增加到1.16×104~1.37×105a.u..当碱金属Li原子吸附在只有给体(—NH2)或只有受体(—CN)基团修饰的PPV衍生物{[NH2-(PPV)n]/[(PPV)n-CN]}时,体系的一阶超极化率值进一步提升,分别高达1.61×105a.u.(n=4)和2.85×105a.u.(n=4).这主要源于锂盐效应和Donor-π-Acceptor之间的协同作用导致跃迁能进一步降低所致.在Li原子吸附的具有给受体基团同时修饰的PPV衍生物(Li@[NH2-(PPV)n-CN])体系中,这种协同作用得到进一步加强,显著改善了体系的一阶超极化率(高达3.56×105a.u.,n=4).     

预测金属有机骨架甲烷和氢气输送能力的迁移学习建模

基于深度神经网络(DNN)和迁移学习(TL),使用少量数据建立TL模型,精准预测了金属有机骨架(MOFs)的甲烷和氢气输送性能.首先,使用8414个MOFs在298 K/65 bar～298 K/5.8 bar(1 bar=0.1 MPa)条件下的甲烷输送数据训练一个决定系数(R²)为0.973的DNN[源任务(ST)模型].随后,将ST模型的部分参数冻结,使用100个MOFs在233 K/65 bar～358 K/5.8 bar条件下的甲烷输送数据和100个MOFs在198 K/100 bar～298 K/5 bar条件下的氢气输送数据分别微调ST模型,进行TL建模.结果表明,两个TL模型的R²分别为0.968和0.945,均高于其它5个传统的ML模型.所开发的TL模型在预测小数据集时具有高精度与高稳定性.最后,使用排列特征重要度方法来计算描述符重要度,明确了模型之间的“知识”共享情况,并在此基础上探讨了重要描述符和输送能力之间的关系.