In-flame soot particle structure on the up- and down-swirl side of a wall-interacting jet in a small-bore diesel engine

This study shows how the structure of soot particles within the flame changes due to the relative direction of the swirl flow in a small-bore diesel engine in which significant flame–wall interactions cause about half of the flame travelling against the swirl flow while the other half penetrating in the same direction. The thermophoresis-based particle sampling method was used to collect soot from three different in-flame locations including the flame–wall impingement point near the jet axis and the two 60° off-axis locations on the up-swirl and down-swirl side of the wall-interacting jet. The sampled soot particle images were obtained using transmission electron microscopes and the image post-processing was conducted for statistical analysis of size distribution of soot primary particles and aggregates, fractal dimension, and sub-nanoscale parameters such as the carbon layer fringe length, tortuosity, and spacing. The results show that the jet-wall impingement region is dominated by many small immature particles with amorphous internal structure, which is very different to large, fractal-like soot aggregates sampled from 60° downstream location on the down-swirl side. This structure variation suggests that the small immature particles underwent surface growth, coagulation and aggregation as they travelled along the piston-bowl wall. During this soot growth, the particle internal structure exhibits the transformation from amorphous carbon segments to a typical core–shell structure. Compared to those on the down-swirl side, the soot particles sampled on the up-swirl side show much lower number counts and more compact aggregates composed of highly concentrated primary particles. This soot aggregate structure, together with much narrower carbon layer gap, indicates higher level of soot oxidation on the up-swirl side of the jet.     

Replica sub-permutation method for molecular dynamics and monte carlo simulations

We propose an improvement of the replica-exchange and replica-permutation methods, which we call the replica sub-permutation method (RSPM). Instead of considering all permutations, this method uses a new algorithm referred to as sub-permutation to perform parameter transition. The RSPM succeeds in reducing the number of combinations between replicas and parameters without the loss of sampling efficiency. For comparison, we applied the replica sub-permutation, replica-permutation, and replica-exchange methods to a β-hairpin mini protein, chignolin, in explicit water. We calculated the transition ratio and number of tunneling events in the parameter space, the number of folding–unfolding events, the autocorrelation function, and the autocorrelation time as measures of sampling efficiency. The results indicate that among the three methods, the proposed RSPM is the most efficient in both parameter and conformational spaces. © 2019 Wiley Periodicals, Inc.     

Delineating Protein–Protein Curvilinear Dissociation Pathways and Energetics with Naïve Multiple-Walker Umbrella Sampling Simulations

The protein–protein interaction energetics can be obtained by calculating the potential of mean force (PMF) from umbrella sampling (US) simulations, in which samplings are often enhanced along a predefined vector as the reaction coordinate. However, any slight change in the vector may significantly vary the calculated PMF, and therefore the energetics using a random choice of vector may mislead. A non-predefined curve path-based sampling enhancement approach is a natural alternative, but was relatively less explored for protein–protein systems. In this work, dissociation of the barnase–barstar complex is simulated by implementing non-predefined curvilinear pathways in US simulations. A simple variational principle is applied to determine the lower bound PMF, which could be used to derive the standard free energy of binding. Two major dissociation pathways, which include interactions with the RNA-binding loop and the Val 36 to Gly 40 loop, are observed. Further, the proposed approach was used to discriminate the decoys from protein–protein docking studies. © 2019 Wiley Periodicals, Inc.     

The Convergence of Markov Chain Monte Carlo Methods: From the Metropolis Method to Hamiltonian Monte Carlo

From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not only increasingly difficult problems but also novel techniques adopted from physics. Here, the history of Markov chain Monte Carlo is reviewed from its inception with the Metropolis method to the contemporary state‐of‐the‐art in Hamiltonian Monte Carlo, focusing on the evolving interplay between the statistical and physical perspectives of the method.     

低维螺旋磁体纳米条带中磁场驱动的磁结构演变规律

利用蒙特卡洛方法研究了低维螺旋磁体纳米条带中磁场驱动的磁结构演变规律,以及偶极相互作用对螺旋磁体纳米条带中磁结构的影响。研究表明,螺旋磁体纳米条带中的自旋结构由自旋的面内取向平行或垂直于边界的边缘态和中间聚集态两部分构成。在一定范围内增加外磁场,条带边缘平行于边界的自旋排列将增多,最终将在条带外围形成大的磁涡旋环,该涡旋环十分稳定,即使在很强的外磁场下仍然存在。处于条带中间的聚集态随着外磁场的增大,从磁螺旋态逐步过渡到Skyrmions态,最终形成铁磁态。Skyrmions的排列状况与条带尺寸密切相关。此外,偶极相互作用对磁矩平行于边界且首尾顺次连接的排列起积极作用,在无外磁场作用下,随着偶极相互作用的增大,体系终将形成涡旋态。     

可迁移的土壤重金属污染高光谱定性分类方法研究

现有基于高光谱遥感的土壤重金属污染定性分类模型,大多采用同一地区室内光谱测定训练样本数据进行模型构建与测试。但室内光谱测定需要复杂的处理过程,成本高,效率低,且无法快速获得目标区域空间上连续的光谱信息。考虑到实际应用需求,模型在相同实验区和不同试验区野外光谱数据是否具有较好的迁移推广能力是目前迫切需要回答的问题。为回答这一问题,选取湖南省郴州市和衡阳市两铅锌矿区作为实验研究区,选用支持向量机（SVM）作为分类器,将郴州实验区室内采样的83个样本数据和衡阳实验区室内采样的46个样本数据分别用于分类器训练,将衡阳地区野外采样的46个样本数据用于分类测试。并首先通过基于联合分布适配（JDA）的迁移学习方法进行光谱变换以缩小两地室内外测定光谱分布差异,然后进行不同区域室内外土壤重金属污染定性分类模型迁移。实验结果表明：（1）由于野外测得的光谱数据会受到太阳辐射、提取的土壤成分差异等因素的干扰导致室内外光谱数据存在显著的分布差异,难以直接将基于室内采样数据训练得到的土壤重金属污染定性分类模型迁移到同一地区测定的野外高光谱数据上。但通过JDA变换缩小室内外分布差异后,模型迁移能力得到显著提升,砷（As）、铅（Pb）和锌（Zn）三种重金属含量是否超标的分类精度都达到了84%以上,Zn元素含量是否超标的分类精度甚至达到了89%以上。（2）由于季节性影响、地区成分的干扰和光谱噪声的增加,不同地区光谱数据存在着更为显著的分布差异,加大了不同地区土壤重金属污染监测的难度,难以将基于室内采样光谱数据所建立的土壤重金属定性分类模型直接迁移到其他地区野外采样数据上（平均分类精度仅在50%左右）。经过JDA迁移学习方法进行室内外光谱变换处理后,模型迁移能力得到保证,因此,室外光谱采样可直接用于研究不同试验区域重金属（As,Pb和Zn）的污染情况。     

Salinity and pH as factors affecting the passive sampling and extraction of pharmaceuticals from water

Passive sampling is an attractive technique for the long‐term monitoring of pharmaceuticals in the water environment. The reliability of the received results depends on the properly performed calibration, namely the determination of analyte sampling rates. This step can be the source of a systematic error, as the sampling rate values are dependent on the water donor phase parameters. This is especially important for pharmaceuticals, since their chemical characteristics and ionic form change with pH. In this study, the cross‐effect of pH (3, 7, and 9) and salinity (0, 7, and 35 practical salinity unit, using artificial sea water) on the passive sampling of 21 pharmaceuticals (antiparasitics, beta‐blockers, non‐steroidal anti‐inflammatory drugs, sulfonamides) was tested. The primarily determined parameter was the sampling rate. In addition, the extraction efficiency, partitioning coefficient, and the concentration of the analytes on the sorbent were calculated. Generally, for the non‐steroidal anti‐inflammatory drugs, beta‐blockers, and antiparasitics, the change both in pH and salinity had a negligible impact on the mentioned experimental parameters. In contrast, the extraction of sulfonamides was impacted by both pH and salinity, while lipophilicity was not a decisive parameter.     

飞行时间质谱仪固体样品自动进样系统研制

以常规型飞行时间质谱仪为基础,通过配置移动平台、样品杆、工业相机、LED灯源、工业显示屏等部件,研制了一套飞行时间质谱仪用固体样品自动进样系统,实现了固体有机样品的直接进样分析。该装置具有一定的普适性,可为相关实验室设备升级改造提供借鉴和参考。该装置可进行难溶未知有机化合物的结构剖析,实现目标物的快速筛查,且操作人员可实时观测锥孔处样品状态并可软件操控进样位置,实现人机分离的自动化测试。     

Idealization of correlations between optimal simplified molecular input-line entry system-based descriptors and skin sensitization

The Index of Ideality of Correlation (IIC) is a new criterion of the predictive potential for quantitative structure–property/activity relationships. The value of the IIC is a mathematical function sensitive to the value of the correlation coefficient and dispersion (expressed via mean absolute error). The IIC has been applied to develop QSAR models for skin sensitization achieving good predictive potential. The ‘ideal correlation’ is based on elementary fragments of simplified molecular input-line entry system (SMILES) and on the taking into account of the total numbers of nitrogen, oxygen, sulphur and phosphorus in the molecule.