The Role of Radical Bridges in Polynuclear Single-Molecule Magnets

Employing radical bridges between anisotropic metal ions has been a viable route to achieve high-performance single-molecule magnets (SMMs). While the bridges have been mainly considered for their ability to promote exchange interactions, the crystal-field effect arising from them has not been taken into account explicitly. This lack of consideration may distort the understanding and limit the development of the entire family. To shed light on this aspect, herein we report a theoretical investigation of a series of N -radical-bridged diterbium complexes. It is found that while promoting strong exchange coupling between the terbium ions, the N -radical induces a crystal field that interferes destructively with that of the outer ligands, and thus reduces the overall SMM behavior. Based on the theoretical results, we conclude that the SMM behavior in this series could be further maximized if the crystal field of the outer ligands is designed to be collinear with that of the radical bridge. This conclusion can be generalized to all exchange-coupled SMMs.     

高陡横坡段桩柱式桥梁双桩基础受力分析

基于高陡横坡段桩柱式桥梁双桩基础承载特性,提出了一种改进有限杆单元计算分析方法.分析了高陡横坡段桩柱式桥梁双桩基础承载机理及受力特性,建立了双桩基础计算分析模型.其次,根据前、后桩与边坡相对位置关系,给出了后桩所受剩余下滑力与前桩所受土压力的比例关系.在传统有限杆单元分析方法基础上,结合陡坡桩受力特征,导得了考虑桩土共同作用与"P-Δ"效应的单元刚度矩阵修正方法,并在此基础上编制了适用于高陡横坡段桩柱式桥梁双桩基础的有限杆单元分析MATLAB计算程序.采用室内模型试验对本文计算方法进行验证,给出了适用于陡坡段桥梁桩基的设计流程图.研究结果表明:本文理论计算值与模型试验实测结果吻合良好,表明本文计算方法正确可行,可为同类工程设计提供参考.     

Functional Binding Surface of a β‐Hairpin VEGF Receptor Targeting Peptide Determined by NMR Spectroscopy in Living Cells

In this study, the functional interaction of HPLW peptide with VEGFR2 (Vascular Endothelial Growth Factor Receptor 2) was determined by using fast ¹⁵N‐edited NMR spectroscopic experiments. To this aim, ¹⁵N uniformly labelled HPLW has been added to Porcine Aortic Endothelial Cells. The acquisition of isotope‐edited NMR spectroscopic experiments, including ¹⁵N relaxation measurements, allowed a precise characterization of the in‐cell HPLW epitope recognized by VEGFR2.     

LaMnO3不同晶位掺杂的磁电阻效应

基于研究钙钛矿型氧化物磁电阻效应的目的,采用固相反应法制备了实验所需的单相多晶样品,主要就La位二价金属离子掺杂和稀土互掺及Mn位掺杂对LaMnO3磁电阻影响的实验结果作比较研究,发现La位稀土互掺对磁电阻的影响可以用晶格效应来解释,并明确指出La位和Mn位掺杂是提高和调制钙钛矿型锰氧化物超大磁电阻效应的一种有效途径.     

The mechanism of ethylene polymerization by nickel salicylaldiminato catalysts - Agostic interactions and their kinetic isotope effects

The ethylene polymerization reaction of a neutral nickel catalyst was studied by DFT calculations at the Becke3LYP/6-31G(d) level of theory. As in related cases a β-agostic bond stabilizes the nickel alkyl ground states. Transition states for the insertion of the olefin show a distinct α-agostic interaction, which has not been observed for late metal polymerization catalysts before. An ethylene-alkyl complex was identified as the resting state of the reaction. The overall barrier height of the reaction amounts to 17.54 kcal/mol, which slightly increases to 17.60 kcal/mol for the polymerization of deuterated ethylene. Therefore, a small positive kinetic isotope effect (k_H/k_D = 1.09) can be calculated, which is caused by the α-agostic interaction in the transition state. A comparison to other late metal based polymerization systems reveals that the ethylene coordination step of highly active catalysts is significantly lower in energy compared to catalysts which are only moderately active.     

Scattering of Laser Light from Mixed Micelles

Dynamic and static scattering of light was employed to investigate mixed micelles of two homologous anionic surfactants-sodium octyl sulfate and sodium hexadecyl sulfate, above the phase boundary temperature and critical micelle concentrations (cmc). The results indicate that the mixed micelles change from prolate to sphcrical as the molar ratio SOS/SHS increases from 1 to 8. Below 1 or above 8, the formation of micelles is due to one surfactant dissolving the other.     

The one-dimensional Hubbard model for large or infiniteU

The magnetic properties of the one-dimensional Hubbard model with a hardcore interaction on a ring (periodic boundary conditions) are investigated. At finite temperatures it is shown to behave up to exponentially small corrections as a pure paramagnet. An explicit expression for the ground-state degeneracies is derived. The eigenstates of this model are used to perform a perlurbational treatment for large but finite interactions. In first order inU ¹ an effective Hamiltonian for the one-dimensional Hubbard model is derived. It is the Hamiltonian of the one-dimensional Hcisenberg model with antiferromagnetic couplings between nearest neighbor spins. An asymptotic expansion for the ground-state energy is given. The results are valid for arbitrary densities of electrons.     

Interaction Dynamics in AEC Global Teamwork

Architecture, engineering, and construction (AEC) global project teamwork is communication intensive and relies heavily on synchronous and asynchronous information and collaboration technologies (ICT). We explore in this paper how an asynchronous ICT, called ThinkTank, reshaped the work practice of design-construction global teams, and how the interaction with this ICT reshaped the purpose and benefits of its use. ThinkTank is a web-based asynchronous collaboration and discussion forum. We introduce the influence diffusion model (IDM) that formalizes the process of identifying the influence of people, messages, and terms mathematically. Discovering who the influence leaders in project teams are can be beneficial and critical from a corporate management perspective, since they can guide or motivate the team towards successful actions and outcomes. We present the ThinkTank-IDM integrated system and its validation with a testbed of 53 AEC global team project archived in ThinkTank over 8 years.     

Characterization of a 4-miktoarm star copolymer of the (PS-b-PI)3 PS type by temperature gradient interaction chromatography

Temperature gradient interaction chromatography (TGIC) was applied for the separation of a complex miktoarm star copolymer which has one polystyrene (PS) arm and three polystyrene-b-polyisoprene (PS-b-PI) diblock copolymer arms. Such miktoarm star polymers are much more difficult to characterize than branched homopolymers since the byproduct, typically polymers with missing arm(s) or coupled products, have not only different molecular weights but also different compositions. TGIC was able to fully separate the byproducts, and the composition of the molecular species corresponding to the different separated elution peaks was determined by two methods, fractionation/NMR and multiple detection (UV and RI). A reasonable agreement between the results of the two methods was obtained. By using the composition found, the corresponding molecular weights were determined by multi-angle light scattering detection. Based on the composition and the molecular weight we were able to identify the structure of the different molecular species.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.