Manipulating energy landscapes to tune ordering in biotemplated nanoparticle arrays

Two-dimensional non-close-packed crystals of the protein streptavidin, grown on phospholipid membranes, can serve as nanoscale templates capable of directing the formation of ordered nanoparticle arrays through site-specific electrostatic adsorption. Here we examine the effects of both interparticle and nanoparticle/lipid membrane electrostatic interactions on the degree of structural order exhibited by the templated nanoparticle array. Interparticle electrostatic repulsion is shown to have only marginal influence on nanoparticle ordering. In contrast, the degree of order exhibited by the templated array can be tuned by controlling the charge on the lipid membrane. Analysis of the local and global structure of arrays generated with negatively charged gold nanoparticles (～6 nm) indicate improved long-range order when the lipid membrane supporting the protein crystal is derived from cationic lipid molecules as opposed to zwitterionic phospholipids. Furthermore, as nanoparticle size is reduced (～3 nm), the presence of a charged lipid membrane is found to be essential, as smaller particles do not adhere to streptavidin crystals grown on zwitterionic membranes. These findings demonstrate that the composition of the lipid support can influence the efficacy of directed-assembly processes which utilize protein templates and are important results toward enhancing control over bottom-up nanofabrication applications.     

生物质碳材料的孔道分析

生物质碳材料的孔道类型和孔径大小制约着材料有效的活性位点数量,影响材料的性能。孔道分类又是孔径分析的前提条件。因此,建立孔道分类的方法非常有意义。随着生物质碳材料的深入研究,研究者对其孔道分析的要求逐渐提高。他们用实际的吸脱附等温线与IUPAC规范中的吸脱附等温线进行匹配,来分类生物质碳材料的孔道。然而实际的吸脱附等温线具有不规则性,难以匹配IUPAC规范中的吸脱附等温线。所以,本文提出了孔隙率和比表面积占有率的孔道分类新方法。自制生物质碳材料,运用物理吸附仪和TEM (Transmission electron microscope)对其进行表征,采用BET方程（Brunauer-Emmett-Teller）、t-plot方法(Thickness-plot)、DFT方法（Non-local Density Functional Theory）、BJH（Barrett Joyner and Halenda）方法对其孔道进行分析。研究表明,采用孔隙率和比表面积占有率对其进孔道分类,可以准确的定义出微孔生物质碳材料、介孔生物质碳材料和微介孔生物质碳材料。本文用标准样品对孔隙率和比表面积占有率的孔道分类新方法进行论证,结果一致。因此,本文提出的孔隙率和比表面积占有率的孔道分类新方法准确可靠,实用性高。     

Lattice defects and magnetic ordering in plutonium oxides: a hybrid density-functional-theory study of strongly correlated materials

Experimental studies of actinide oxides are challenging, and conventional electronic structure calculations fail to qualitatively reproduce the scarce data. We employ a new generation of hybrid density functionals to model a defective plutonium dioxide lattice. The procedure is first tested against stoichiometric bulk PuO2 and Pu2O3, for which predictions agree well with experiment where known. The interstitial oxygen in PuO2.25 is found to be singly charged, consistent with experimental observations and contrary to the O2- previously proposed theoretically.     

The ionic model applied to vacancy ordering in NaCl-type materials

An order-disorder transition in Sc_0.8S (NaCl structure type) was studied by high-temperature X-ray diffraction and 700 ± 20°C was found to be the ordering temperature. A group of ordered sublattices on the rock-salt lattice was generated and a Madelung energy and a configurational entropy were calculated for each assuming the lattice was made up of point charges. Mean field and pair interaction approximations were used to model long-range and short-range orderings, respectively. Results indicate that the ionic model is seriously in error in predicting short-range or long-range orderings of the type observed.     

Electro-oxidation of hydrazine at gold nanoparticle functionalised single walled carbon nanotube network ultramicroelectrodes

Networks of pristine single walled carbon nanotubes (SWNTs) grown by catalysed chemical vapour deposition (cCVD) on an insulating surface and arranged in an ultramicroelectrode (UME) format are insensitive to the electro-oxidation of hydrazine (HZ) in aqueous solution, indicating a negligible metallic nanoparticle content. Sensitisation of the network towards HZ oxidation is promoted by the deliberate and controlled electrodeposition of "naked" gold (Au) nanoparticles (NPs). By controlling the deposition conditions (potential, time) it is possible to control the size and spacing of the Au NPs on the underlying SWNT network. Two different cases are considered: Au NPs at a number density of 250 ± 13 NPs μm(-2) and height 24 nm ± 5 (effective surface coverage, θ = 92%) and (ii) Au NPs of number density ~ 22 ± 3 NPs μm(-2) and height 43 nm ± 8 nm (θ = 35%). For both morphologies the HZ oxidation half-wave potential (E(1/2)) is shifted significantly negative by ca. 200 mV, compared to a gold disc UME of the same geometric area, indicating significantly more facile electron transfer kinetics. E(1/2) for HZ oxidation for the higher density Au NP-SWNT structure is shifted slightly more negative (by ~25 mV) than E(1/2) for the lower density Au NP electrode. This is attributed to the lower flux of HZ at NPs in the higher number density arrangement (smaller kinetic demand). Importantly, using this approach, the calculated HZ oxidation current density sensitivities for the Au NP-SWNT electrodes reported here are higher than for many other metal NP functionalised carbon nanotube electrodes.     

In situ synthesis and characterization of multiwalled carbon nanotube/Au nanoparticle composite materials

An effective and facile in situ reduction approach for the fabrication of carbon nanotube-supported Au nanoparticle (CNT/Au NP) composite nanomaterials is demonstrated in this article. Linear polyethyleneimine (PEI) is ingeniously used as both a functionalizing agent for the multiwalled carbon nanotubes (MWNTs) and a reducing agent for the formation of Au NPs. This method involves a simple mixing process followed by a mild heating process. This approach does not need the exhaustive surface oxidation process of CNTs. The coverage of Au NPs on CNTs is tunable by varying the experimental parameters, such as the initial molar ratio of PEI to HAuCl4, the relative concentration of PEI and HAuCl4 to MWNTs, and the temperature and duration of the heat treatment. More importantly, even the heterogeneous CNT/Au composite nanowires are obtainable through this method. TEM, XPS, and XRD are all used to characterize the CNT/Au composite materials. In addition, the optical and electrocatalytic properties are investigated.     

Phase transition sensitive to interlayer defects in layered semiconductor TlGaSe2

The structural and thermal properties of ferroelectric–semiconductor TlGaSe₂ with layered crystalline structure have been investigated in 77–300 K temperature range. It is found that all outlined physical properties of TlGaSe₂ are significantly modified near 180 K due to the phase transition (PT). The nature of this PT has been analyzed and it is established that the main peculiarity of such PT is the extreme sensitivity to interlayer bonding. As a result, any manifestations of this PT depend strongly on defects and impurities located between the layers and can be changed by illumination, temperature annealing and applying electric field.     

Resonance Raman contribution to the D band of carbon materials: modeling defects with quantum chemistry

Polycyclic aromatic hydrocarbons (PAHs) are employed to model the Raman features that are generally associated with sp(2) nanostructures in carbon materials or with disorder and defects in graphitic materials. To this end molecular parameters (geometry changes upon electronic excitation, vibrational normal modes, and displacement parameters) are computed with semiempirical quantum-chemical methods for a series of PAHs ranging from 6 to 384 carbon atoms, and Raman intensities are evaluated according to Albrecht's formalism restricted to the A term. The computed preresonance and resonance Raman intensities are compared with available experimental data for hexa-peri-hexabenzocoronene and for pyrene. For the latter compound, simulations carried out at semiempirical and ab initio levels of theory are shown to be of comparable quality. Finally, the collection of displacement parameters computed for the sample of conjugated molecules is used to model the effect of disorder and defects in the Raman response of a carbon material containing sp(2) islands. It is shown that the computed D-band frequency dispersion, with respect to excitation wavelength, reproduces closely the experimental data measured for sp(2) hybridized carbon materials.     

Thermoanalytical procedures to identify the morphology of polycrystalline materials

Polypropylenes of differing morphologies were examined by differential thermal analysis. The problems of reordering during the programmed heating are discussed. Four potential procedures by which the reordering could be inhibited are considered, and three of these are experimentally assessed. Their advantages and deficiencies are identified. Two pretreatments, an etching and a crosslinking, are recognised as able to qualitatively characterise the initial morphology, with a preference for the former. A test to identify the occurrence of reordering is identified. In celebration of the 60^th birthday of Dr. Andrew K. Galwey     

Nitrogen-rich carbon nitride network materials via the thermal decomposition of 2,5,8-triazido-s-heptazine

This report describes the rapid and slow thermal decomposition of an energetically unstable polycyclic and heterocyclic azide, triazido-s-heptazine (C6N16), to produce nitrogen-rich CNx materials (x > 1.2). An analysis of gaseous byproducts shows that this large heterocyclic precursor releases primarily N2 gas during its decomposition. The product composition and its morphology are dependent on the rapidity of the TAH decomposition. The CNx products are thermally stable to 500 degrees C and exhibit variations in H and O content dependent on precursor preparation and atmospheric exposure. The rapid decomposition of TAH leads to visibly porous powders, while slow decomposition yields smooth monoliths that are reminiscent of the morphology of the starting polycrystalline powder. IR and NMR spectral similarities between the amorphous CNx products and several previously reported heptazine molecules and extended heptazine networks supports significant retention of heptazine motif in these amorphous carbon nitride extended materials.     

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4

Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.

     

Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways

Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.     

水热碳材料在分离分析中的应用研究进展

水热碳材料是一种由糖类或含碳有机物经过水热反应制备的新型功能材料,具有来源丰富、绿色环保、亲水性、易修饰等优点,已广泛应用于催化剂载体、能源电极材料、环境吸附剂等方面。其中,水热碳在吸附领域的应用显示出其与特定分子的相互作用,近年来,水热碳材料作为分离富集固相基质,在色谱固定相以及生物样品处理领域逐渐得到应用。该文主要综述了水热碳材料在离子化合物、极性化合物、磷酸化肽段和糖基化肽段分离分析等方面的最新应用,讨论了水热碳材料在实际分离分析应用中的优点和局限性,并就水热碳材料在该领域的应用前景进行了展望。     

小波分析和人工神经网络方法用于重叠色谱峰的解析

将神经网络与小波分析相结合,提出一种利用小波变换提取重叠色谱峰信息,再用人工神经网络解析的方法,为色谱峰重叠难于分开的物质的测定提供一种方法,提高了色谱分析的准确度,并将此方法应用于高效液相色谱中,建立了混合体系中3.4-二甲酚和2.5-二甲酚同时测定的新化学计量学方法.     

Comparative analysis of layered materials using laser-induced plasma spectrometry and laser-ionization time-of-flight mass spectrometry

Laser-induced plasma spectrometry (LIPS) and laser ionization time-of-flight mass spectrometry (LI-TOFMS) have been evaluated for the in-depth analysis of layered materials. LI-TOFMS shares with LIPS important advantages in terms of speed of analysis and negligible sample handling. However, additional features such as real multielemental capabilities and the absence of background contribution must be added to the former. In order to have a useful estimation of the potential of each technique, an in-depth characterized Zn-coated steel has been analyzed. Without complete optimization of the system, the averaged ablation rate has been measured to be in the range 20–30 nm/pulse without beam conditioning or optical modifications.     

L-色氨酸在金纳米粒子修饰电极上的电化学行为及其分析研究

制备了金纳米粒子修饰玻碳电极(Au/GCE),用循环伏安法研究L-色氨酸(L-Trp)在修饰电极上的电化学行为,以及支持电解质、溶液p H、扫描速率等对L-Trp伏安响应的影响。实验表明:在p H=3.5的HAcNa Ac支持电解质中,L-Trp在Au/GCE上有一灵敏的氧化峰(Epa=0.93)。氧化峰电流与L-Trp浓度在5.0×10-7~1.0×10-4mol·L-1范围内呈良好的线性关系,相关系数为0.9990,检出限1.6×10-7mol·L-1。测得L-Trp样品平均回收率为98%。     

Application of quantitative artificial neural network analysis to 2D NMR spectra of hydrocarbon mixtures

Understanding relationships between the structure and composition of molecular mixtures and their chemical properties is a main industrial aim. One central field of research is oil chemistry where the key question is how the molecular characteristics of composite hydrocarbon mixtures can be associated with the macroscopic properties of the oil products. Apparently these relationships are complex and often nonlinear and therefore call for advanced spectroscopic techniques. An informative and an increasingly used approach is two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy. In the case of composite hydrocarbons the application of 2D NMR methodologies in a quantitative manner pose many technical difficulties, and, in any case, the resulting spectra contain many overlapping resonances that challenge the analytical work. Here, we present a general methodology, based on quantitative artificial neural network (ANN) analysis, to resolve overlapping information in 2D NMR spectra and to simultaneously assess the relative importance of multiple spectral variables on the sample properties. The results in a set of 2D NMR spectra of oil samples illustrate, first, that use of ANN analysis for quantitative purposes is feasible also in 2D and, second, that this methodology offers an intrinsic opportunity to assess the complex and nonlinear relationships between the molecular composition and sample properties. The presented ANN methodology is not limited to the analysis of NMR spectra but can also be applied in a manner similar to other (multidimensional) spectroscopic data.     

First experimental evidence of potassium ordering in layered k4co7o14

The layered P2-K4Co7O14 oxide has been prepared and characterized by means of X-ray diffraction, electrical conductivity, thermopower, and magnetic measurements. The crystal structure of K4Co7O14 (P6(3)/m space group, Z=2, a=7.5171(1) A, and c=12.371(1) A) consists of a stacking of slabs of edge-shared CoO6 octahedra with K+ ions occupying ordered positions in the interslab space, leading to a a0 radical7xa0 radical7 supercell. Potential energy calculations at 0 K are in good agreement with the ordered distribution of potassium ions in the (ab) plane. This oxide is metallic, and the magnetic susceptibility is of Pauli-type, which contrasts with the Curie-Weiss behavior of the homologous NaxCoO2 (x approximately 0.6) oxide with close alkali content. The thermopower at room temperature is about one-third that of polycrystalline Na0.6CoO2.     

Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis

Instrumentation spectra used for chemometrics analysis are often too unwieldy to model, as many of the inputs do not contain important information. Several mathematical methods are used for reducing the number of inputs to the significant ones only. Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs. However, using a non-random initial connection weight algorithm and local minima avoidance and escape techniques can overcome these difficulties. Once the ANN model is trained, the analysis of its connection weights can easily identify the more relevant inputs. Repeating the process of training the ANN model with the reduced input set and the selection of the more relevant inputs can proceed until a quasi-optimal, small, set of inputs is identified. Two examples are presented—finding the minimal set of wavelengths in benchmark diesel fuel NIR spectra, and in spectra generated in a recent work, modeling of “artificial nose” sensor array. In the last example, 1260 inputs were reduced to optimal sets of <10 inputs. Causal index calculation can analyze the influence of each of selected wavelengths on the predicted property. Some of the resulting minimal sets are not unique, depending on the ANN architecture used in the training. The accuracy of the resulting ANN models is usually better, and more robust, than the original large ANN model.