中学化学教学中的科学思维与逻辑辨析

化学是一门具有创造性的基础科学,是环境、生命、医药、材料等科学的基础,在思维逻辑上彰显和蕴含了丰富的科学思维方法。系统总结化学科学思维方法,可以有效破解中学化学学习中的“繁,难,乱”。在多年的教学实践中,提出了化学科学多向思维方法体系,强调多层次思维过程中对多因素的立体逻辑思维辨析。重点介绍了与中学化学教学相关的零阶、初阶、中阶和高阶逻辑思维辨析的内涵,期望化学教学中重视化学思维逻辑过程,实现化学教与学在思维模式上的深层次发展与提升。     

Evaluation of Physiological Characteristics,Soluble Sugars,Organic Acids and Volatile Compounds in ‘Orin’ Apples (Malus domestica) at Different Ripening Stages

‘Orin’ is a popular apple cultivar, which has a yellow-green appearance, pleasant taste, and unique aroma. However, few studies on the fruit quality characteristics of ‘Orin’ apples have been reported before. In this study, changes of the physiological characteristics were measured at different ripening stages, and the soluble sugars and organic acids were determined by high-performance liquid chromatography (HPLC). Volatile compounds were identified using the headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS). During the fruit ripening, the ‘Orin’ apple fruit weight, size, and total soluble solid were gradually increased by contrast with the titratable acidity, and the firmness decreased. The content of four soluble sugars reached the maximum at the 180 days after full bloom (DAFB) stage. Malic acid was measured as the most abundant organic acid in ‘Orin’ apples. Ethyl butyrate, hexyl propanoate, hexyl acetate and butyl acetate belonging to esters with high odor activity values (OAVs) could be responsible for the typical aroma of ‘Orin’ apples. The aim of this work was to provide information on the flavor characteristics of ‘Orin’ apples and promote this apple cultivar for marketing and processing in the future.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Year Discrimination of Mild Aroma Chinese Liquors Using Three-Dimensional Fluorescence Spectroscopy Combined with Parallel Factor and Neural NetworkSCIEI北大核心CSCD

Three-dimensional fluorescence spectroscopy coupled with parallel factor analysis and neural network was applied to the year discrimination of mild aroma Chinese liquors. The excitation-emission fluorescence matrices (EEMs) of 120 samples with various years were measured by FLS920 fluorescence spectrometer. The trilinear decomposition of the data array was performed and the loading scores of and the excitation-emission profiles of four components were also obtained. The scores were employed as the inputs of the BP neural networks and the PARAFAC-BP identification model was constructed. 10 samples were collected from 10, 20 and 30 years of liquors respectively, and 30 samples were selected as the test sets. The remaining 90 samples were used as the training sets to build the training model. The year prediction of unknown samples was also carried out, and the prediction accuracy was 90%, 100% and 100%, respectively. Meanwhile, the discrimination analysis method and the multi way partial least squares discriminant analysis were compared, namely PARAFAC-BP and NPLS-DA. The results indicated that parallel factor combined with the neural network (PARAFAC-BP) has higher prediction accuracy. The proposed method can effectively extract the spectral characteristics, and also reduce the dimension of the input variables of neural network. A good year discrimination result was finally achieved.     

Determination of phthalate esters in liquor samples by vortex‐assisted surfactant‐enhanced‐emulsification liquid–liquid microextraction followed by GC–MS

A novel method using vortex‐assisted surfactant‐enhanced‐emulsification liquid–liquid microextraction has been developed for the extraction of phthalate esters (PAEs) in Chinese liquor samples prior to analysis by GC–MS. In the proposed method, a high‐density extraction solvent (carbon tetrachloride) was dispersed into samples with the aid of a surfactant (Triton X‐100) and vortex agitation, resulting in a short extraction equilibrium (30 s). After centrifugation, a single microdrop of solvent was easily collected for GC–MS analysis. Key factors that affected the extraction efficiency were optimized. Under the optimum conditions, linearity was found in the range from 0.05 to 50 μg/L. Coefficients of determination varied from 0.9938 to 0.9971. LODs, based on an S/N of 3, ranged from 4.9 to 13 ng/L. Enrichment factors varied from 140 to 184. Reproducibility and recoveries were assessed by testing a series of three liquor samples that were spiked with different concentration levels. Finally, the proposed method was successfully applied to the determination of PAEs in 16 Chinese liquor samples. In this work, high‐density‐solvent vortex‐assisted surfactant‐enhanced‐emulsification liquid–liquid microextraction was applied for the first time for the extraction of PAEs in Chinese liquor samples and was proved to be simple, rapid, and sensitive.     

费谢尔判别法鉴别原油/燃料油

以多环芳烃作为变量,建立了原油、燃料油属性鉴别的费谢尔判别法。分别测定了来自不同国家和地区的26个原油样品和25个燃料油样品中8种多环芳烃的含量,并将它们作为判别变量。借助SPSS 16.0进行费谢尔判别分析,建立费谢尔判别函数。将未知样品的判别变量值代入后,可以快速地得知样品的类别。结果表明,以多环芳烃作为判别变量进行原油、燃料油费谢尔判别快速而准确。     

长江下游下蜀黄土的稀土元素物源判别

通过对长江下游镇江、福家湾、新港、芜湖、福路镇等地区下蜀黄土中稀土元素的研究,结合长江古漫滩堆积物及北方黄土沉积物,对下蜀黄土进行了物源判别,得出：下蜀黄土与长江河漫滩之间的各稀土元素的 DF 值绝大部分小于0．2,表现出在各个稀土元素上与河漫滩的接近程度较大,而与北方黄土接近程度较小;由物源指数 PI 计算得出,新港、福路镇、镇江下蜀黄土 PI 值均在0．2附近,表现出下蜀黄土与河漫滩在稀土元素总体上的相似程度极高,物源判别为长江河漫滩;福家湾下蜀黄土物源判别为北方黄土,北方黄土对其有一定影响。总体上,长江下游下蜀黄土受到北方黄土与河漫滩的影响,其中以长江河漫滩影响为主。     

Intracellular Entropy‐Driven Multi‐Bit DNA Computing for Tumor Progression Discrimination

Tumor progressions such as metastasis are complicated events that involve abnormal expression of different miRNAs and enzymes. Monitoring these biomolecules in live cells with computational DNA nanotechnology may enable discrimination of tumor progression via digital outputs. Herein, we report intracellular entropy‐driven multivalent DNA circuits to implement multi‐bit computing for simultaneous analysis of intracellular telomerase and microRNAs including miR‐21 and miR‐31. These three biomolecules can trigger respective DNA strand displacement recycling reactions for signal amplification. They are visualized by fluorescence imaging, and their signal outputs are encoded as multi‐bit binary codes for different cell types. The results can discriminate non‐tumorigenic, malignant and metastatic breast cells as well as respective tumors. This DNA computing circuit is further performed in a microfluidic chip to differentiate rare co‐cultured cells, which holds a potential for the analysis of clinical samples.     

Enantiomeric Discrimination by Surface‐Enhanced Raman Scattering–Chiral Anisotropy of Chiral Nanostructured Gold Films

A surface‐enhanced Raman scattering‐chiral anisotropy (SERS‐ChA) effect is reported that combines chiral discrimination and surface Raman scattering enhancement on chiral nanostructured Au films (CNAFs) equipped in the normal Raman scattering Spectrometer. The CNAFs provided remarkably higher enhancement factors of Raman scattering (EFs) for particular enantiomers, and the SERS intensity was proportional to the enantiomeric excesses (ee) values. Except for molecules with mesomeric species, all of the tested enantiomers exhibited high SERS‐ChA asymmetry factors (g), ranging between 1.34 and 1.99 regardless of polarities, sizes, chromophores, concentrations and ee. The effect might be attributed to selective resonance coupling between the induced electric and magnetic dipoles associated with enantiomers and chiral plasmonic modes of CNAFs.