Developing the Low-Temperature Oxidation Mechanism of Cyclopentane: An Experimental and Theoretical Study

At present, the reactivity of cyclic alkanes is estimated by comparison with acyclic hydrocarbons. Due to the difference in the structure of cycloalkanes and acycloalkanes, the thermodynamic data obtained by analogy are not applicable. In this study, a molecular beam sampling vacuum ultraviolet photoionization time-of-flight mass spectrometer (MB-VUV-PI-TOFMS) was applied to study the low-temperature oxidation of cyclopentane (CPT) at a total pressure range from 1–3 atm and low-temperature range between 500 and 800 K. Low-temperature reaction products including cyclic olefins, cyclic ethers, and highly oxygenated intermediates (e. g., ketohydroperoxide KHP, keto-dihydroperoxide KDHP, olefinic hydroperoxides OHP and ketone structure products) were observed. Further investigation of the oxidation of CPT – electronic structure calculations – were carried out at the UCCSD(T)-F12a/aug-cc-pVDZ//B3LYP/6-31+ G(d,p) level to explore the reactivity of O₂ molecules adding sequentially to cyclopentyl radicals. Experimental and theoretical observations showed that the dominant product channel in the reaction of CPT radicals with O₂ is HO₂ elimination yielding cyclopentene. The pathways of second and third O₂ addition – the dissociation of hydroperoxide – were further confirmed. The results of this study will develop the low-temperature oxidation mechanism of CPT, which can be used for future research on accurately simulating the combustion process of CPT.     

Some Results on the Optimal Matching Problem for the Jacobi Model

Potential Analysis - We establish some exact asymptotic results for a matching problem with respect to a family of beta distributions. Let X1,…,Xn be independent random variables with common...     

A Stochastic Subgradient Method for Distributionally Robust Non-convex and Non-smooth Learning

Journal of Optimization Theory and Applications - We consider a distributionally robust formulation of stochastic optimization problems arising in statistical learning, where robustness is with...     

Excellent UV-absorbing cotton fabric with high durability and hydrophobicity based on surface-initiated ATRP of polymerizable organic UV-absorber

Cellulose - Ultraviolet (UV) protective cotton fabric is a convenient and reliable way to protect human body from sunlight. Herein, a polymerizable UV-absorber,...     

坚硬顶板爆破切顶在沿空留巷中的应用研究

当煤层上方为坚硬顶板时,在工作面回采期间容易出现大面积悬顶,导致巷旁充填体出现大变形破坏,极大威胁采煤安全.为解决这一问题,以某矿1205工作面沿空留巷为工程背景,分析了爆炸围岩裂隙范围和坚硬顶板破断特征,建立了切顶卸压围岩结构力学模型,研究表明,相邻炮孔爆炸后,炮孔间裂隙互相贯穿,降低了顶板强度,同时当切顶角为15°时,巷道稳定性较好.工程实践结果表明:采用爆破切顶技术后,顶板下沉量减小了62.3%,较好地保证了围岩稳定性.     

Interfacial instability of ferrofluid flow under the influence of a vacuum magnetic field

This study is to numerically test the interfacial instability of ferrofluid flow under the presence of a vacuum magnetic field. The ferrofluid parabolized stability equations(PSEs) are derived from the ferrofluid stability equations and the Rosensweig equations, and the characteristic values of the ferrofluid PSEs are given to describe the ellipticity of ferrofluid flow. Three numerical models representing specific cases considering with/without a vacuum magnetic field or viscosity are created to mathematically examine the interfacial instability by the computation of characteristic values. Numerical investigation shows strong dependence of the basic characteristic of ferrofluid Rayleigh-Taylor instability(RTI) on viscosity of ferrofluid and independence of the vacuum magnetic field.For the shock wave striking helium bubble, the magnetic field is not able to trigger the symmetry breaking of bubble but change the speed of the bubble movement. In the process of droplet formation from a submerged orifice, the collision between the droplet and the liquid surface causes symmetry breaking. Both the viscosity and the magnetic field exacerbate symmetry breaking. The computational results agree with the published experimental results.     

基于GA_XGBoost模型的大学生科研能力预测问题研究

科学评价大学生科研创新能力对我国科研水平的提高具有重要意义.采用机器学习模型来预测大学生科研能力可以起到良好的效果,提出一种GAXGBoost模型来实现对大学生的科研能力预测.此模型是以Xgboost算法为基础,然后充分利用遗传算法的全局搜索能力自动搜索Xgboost最优超参数,避免了人为经验调参不准确的缺陷,最后采用精英选择策略以此确保每一轮都是最佳的进化结果.通过分析表明,所采用的GAXGBoost模型在大学生科研能力预测的结果中具有很高的精度,将此模型与Logistic Regression、Random Forest、SVM等模型进行对比,GAXGBoost模型的预测精度最高.