Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.     

Bonding,Aromaticity and Isomerization of Furfuraldehyde through Off-Nucleus Isotropic Magnetic Shielding

Off-nucleus isotropic magnetic shielding (σ_iso(r)) and multi-points nucleus independent chemical shift (NICS(0-2 Å)) index were utilized to find the impacts of the isomerization of gas-phase furfuraldehyde (FD) on bonding and aromaticity of FD. Multidimensional (1D to 3D) grids of ghost atoms (bqs) were used as local magnetic probes to evaluate σ_iso(r) through gauge-including atomic orbitals (GIAO) at density functional theory (DFT) and B3LYP functional/6-311+G(d,p) basis set level of theory. 1D σ_iso(r) responses along each bond of FD were examined. Also, a σ_iso(r) 2D-scan was performed to obtain σ_iso(r) behavior at vertical heights of 0–1 Å above the FD plane in its cis, transition state (TS) and trans forms. New techniques for evaluating 2D σ_iso(r) cross-sections are also included. Our findings showed that bonds in cyclic and acyclic parts of FD are dissimilar. Unlike the C−O bond of furanyl, the C=O bond of the formyl group is magnetically different. C−C and C−H bonds in furanyl are found similar to those in aromatic rings. A unique σ_iso(r) trend was observed for the C₂−C₆ bond during FD isomerization. Based on NICS(0-2 Å) values, the degree of aromaticity follows the order of cis FD<trans FD<furan<TS FD.     

Can a Procedure for the Growth of Single-layer Graphene on Copper be used in Different Chemical Vapor Deposition Reactors?

In the last decade, catalytic chemical vapor deposition (CVD) has been intensively explored for the growth of single-layer graphene (SLG). Despite the scattering of guidelines and procedures, variables such as the surface texture/chemistry of catalyst metal foils, carbon feedstock, and growth process parameters have been well-scrutinized. Still, questions remain on how best to standardize the growth procedure. The possible correlation of procedures between different CVD setups is an example. Here, two thermal CVD reactors were explored to grow graphene on Cu foil. The design of these setups was entirely distinct, one being a “showerhead” cold-wall type, whereas the other represented the popular “tubular” hot-wall type. Upon standardizing the Cu foil surface, it was possible to develop a procedure for cm²-scale SLG growth that differed only by the carrier gas flow rate used in the two reactors.     

The chemical bonding and spectral assignments of rhodium(III)‐catalyzed closo‐dodecaborate complexes: Ab initio study

The chemical bonding and spectral assignments of rhodium(III)‐catalyzed closo‐dodecaborate (RhCD) complex are systematically studied using the density functional theory calculations. It is found that the calculated main bond lengths of framework are in good agreement with experimental X‐ray observation, and the pronounced hybridization of B‐2p and Rh‐4d states is responsible for the structural stability, reflected by the large dissociation energy and HOMO–LUMO energy gap. The AdNDP chemical bonding analysis indicates that the RhCD complex can be stabilized by two H‐bridged 3c‐2e σ‐bonds (B‐H‐Rh triangles). Additionally, the theoretical calculations reproduce well the main experimental IR spectrum, and the characteristic peaks are properly assigned. These results will be helpful for further insight into the unique electronic structure of the species, and provide valuable references for potential applications in novel materials.     

自发过程总熵判据的讨论式教学实践

将讨论式教学法的基本思想应用到物理化学的课堂教学中,对物理化学的教学难点--热力学自发过程总熵判据的适用条件问题进行了深入的讨论,最终归纳总结,得出重要结论：总熵判据只能用来判断过程的可逆性,不能直接判断过程的自发性。要想进一步判断过程的自发性质,须结合系统与环境之间交换的总功进行判断。通过讨论式教学实践,学生对热力学自发过程总熵判据的适用条件有了更加深入的理解,给后续课程的学习打下了良好的基础。     

恒温恒容平衡体系中的反应物转化率随浓度变化规律的探讨

对恒温恒容条件下的可逆平衡体系在增大反应物浓度（即增大反应物的投料）后自身转化率的变化规律,进行了数学推导,得到了其转化率可能减小、不变或增大的结论。通过实例分析佐证了该结论的正确性,以避免在课堂教学和命题中出现错误。     

Electrochemical Selective Determination of Uranyl Ions Using PVC Membrane Sensor

A UO₂²⁺‐PVC membrane electrode was constructed using 2‐thenoyltrifluoroacetone as ionophore and its electrochemical response performance was characterized. The effect of membrane composition on the electrode performance was studied and best results were obtained using dioctylsebacate as a mediator and potassium tetrakis(4‐chlorophenyl)borate as anion excluder. The optimized UO₂²⁺‐sensor exhibited a Nernstian response with a slope of 29.5±0.5 mV decade⁻¹ over the concentration range 5.0×10⁻⁷−1.0×10⁻¹ mol L⁻¹ at 25 °C with a detection limit of 3.1×10⁻⁷ mol L⁻¹. The optimized electrode showed very good selectivity towards UO₂²⁺ relative to a wide variety of other cations and fast response time. Surface morphology of the optimized membrane electrode at different stages of its development and use was investigated and discussed. Quantum chemical calculations for geometrical optimization of the ionophore were carried out to investigate the interaction between the ionophore and UO₂²⁺ using DFT B3LYP/6‐31++G(d,p) level of theory and the obtained data confirmed the proposed response mechanism. The developed sensor was successfully applied for UO₂²⁺ selective determination in real water samples and the obtained results were compared to those obtained by spectrophotometric method indicating no significant difference.     

Bismuth Nanoparticles@Porous Silicon Nanostructure,Application as a Selective and Sensitive Electrochemical Sensor for the Determination of Thioridazine

A bismuth@porous silicon (Bi/PSi) nanostructure is fabricated and used as a new highly sensitive electrochemical sensor for measurement of thioridazine. For this purpose, commercial silicon powder is converted to porous silicon using metal‐assisted chemical etching method. Then, bismuth nanoparticles are deposited on the surface of the porous silicon that synthesized in the previous step. The effects of pH and instrumental parameters are studied on the sensor response. After optimization of the parameters, differential pulse voltammetry is used to determine sub‐micro molar amounts of thioridazine. The Linear region of the electrochemical sensor is in the range of 0.1 to 260 μmol L⁻¹ thioridazine with a detection limit of 0.03 μmol L⁻¹, when Bi/PSi/CNTPE is used as an electrochemical sensor. The precision and accuracy of the sensor is evaluated. The Bi/PSi/CNTPE is used as an appropriated tool for accurate measurement of low amounts of thioridazine in real samples with satisfactory results.     

Toward amino acid typing for proteins in FFLUX

Continuing the development of the FFLUX, a multipolar polarizable force field driven by machine learning, we present a modern approach to atom‐typing and building transferable models for predicting atomic properties in proteins. Amino acid atomic charges in a peptide chain respond to the substitution of a neighboring residue and this response can be categorized in a manner similar to atom‐typing. Using a machine learning method called kriging, we are able to build predictive models for an atom that is defined, not only by its local environment, but also by its neighboring residues, for a minimal additional computational cost. We found that prediction errors were up to 11 times lower when using a model specific to the correct group of neighboring residues, with a mean prediction of ∼0.0015 au. This finding suggests that atoms in a force field should be defined by more than just their immediate atomic neighbors. When comparing an atom in a single alanine to an analogous atom in a deca‐alanine helix, the mean difference in charge is 0.026 au. Meanwhile, the same difference between a trialanine and a deca‐alanine helix is only 0.012 au. When compared to deca‐alanine models, the transferable models are up to 20 times faster to train, and require significantly less ab initio calculation, providing a practical route to modeling large biological systems. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.