Detecting pore size distribution of activated carbon by low-field nuclear magnetic resonance

In this study, the transverse relaxation time (T₂) of activated carbon (AC) in different relative environment humidity was detected firstly by low-field nuclear magnetic resonance (LFNMR). The pore size (diameter) of AC distributions was calculated by the relationship between T₂ and surface relaxation rate (ρ), where ρ was obtained by the detection of nine porous materials with known pore size. The results showed that the pore size distributions of AC calculated by ρ < 0.19 nm/ms were in good agreement with that obtained by nitrogen adsorption method and proved that LFNMR as a new detection method was feasible for characterizing AC pore size distribution.     

Effect of temperature on the interactions between cellulose and lignin via molecular dynamics simulations

Cellulose - Heating is essential in various biomass pre-treatments and thermal conversion processes. It is of practical significance to study the characteristics of cellulose-lignin and...     

Development and validation of LC–MS/MS method for amlexanox in rat plasma and its application in preclinical pharmacokinetics

Amlexanox, an anti-inflammatory and anti-allergic agent, has been widely used clinically for the treatment of canker sores, asthma, and allergic rhinitis. Recently, amlexanox has received considerable attention in curing nonalcoholic fatty liver diseases and hepatitis virus infection. Herein, we first established a sensitive high-performance liquid chromatography-tandem mass spectrum (LC–MS/MS) method for the determination of amlexanox in rat plasma. Propranolol was used as the internal standard (IS). Using a simple protein precipitation method, the amlexanox and IS were separated with Capcell Pak C18 column (2.0 × 50 mm, 5 μm) and eluted with water and acetonitrile each containing 0.1% formic acid using gradient elution condition at a flow rate of 0.4 mL·min⁻¹. Amlexanox and IS were detected by a triple quadrupole mass in multiple reactive monitoring (MRM) under the transitions of m/z 299.2 → 281.2 and m/z 259.9 → 116.1 with positive electrospray ionization, respectively. The calibration curves of amlexanox were established with the range of 50 to 2000 ng·mL⁻¹ (r² > 0.99). The validation method consisted of selectivity, accuracy, precision, carryover effect, matrix effect, recovery, dilution effect, and stability. The fully validated method was successfully applied to the pharmacokinetic study of amlexanox in Wistar rats.     

Improved production of levoglucosan and levoglucosenone from acid-impregnated cellulose via fast pyrolysis

Cellulose - In this research, the production of levoglucosan (LG) and levoglucosenone (LGO) was improved from acid-impregnated cellulose via fast pyrolysis. Thermogravimetric and kinetic analysis...     

High-fidelity Recognition of Organotrifluoroborate Anions (R−BF3−) as Designer Guest Molecules

The recognition of boron compounds is well developed as boronic acids but untapped as organotrifluoroborate anions (R−BF₃⁻). We are exploring the development of these and other designer anions as anion-recognition motifs by considering them as substituted versions of the parent inorganic ion. To this end, we demonstrate strong and reliable binding of organic trifluoroborates, R−BF₃⁻, by cyanostar macrocycles that are size-complementary to the inorganic BF₄⁻ progenitors. We find that recognition is modulated by the substituent's sterics and that the affinities are retained using the common K⁺ salts of R−BF₃⁻ anions.     

Study on the static properties of spiral springs under static loading

Applied Mathematics and Mechanics - Spiral springs have a wide range of applications in various fields. As a result of the complexity of friction, few theoretical analyses of spring belts under...     

Gap theorems for Lagrangian submanifolds in complex space forms

Geometriae Dedicata - Inspired by a recent work of Grove and Petersen (Alexandrov spaces with maximal radius, 2018), where the authors studied positively curved Alexandrov spaces with largest...     

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.