基于MOOC“一站式”有机化学在线翻转课堂实践

新型冠状病毒(COVID-19)疫情下,为了响应教育部"停课不停学,停课不停教",实现在线教学和线下课堂同质等效,本文依托中国大学MOOC平台,以在线开放课程有机化学为线上学习资源,建立校内SPOC课程,借助慕课堂,实现在线翻转课堂。课前学生学习视频,自学自测;直播赋能解惑,互动参与;课后举一反三,巩固提高。"一站式"解决了在线翻转课堂三阶段的学习需求,初步探讨了在线翻转课堂教学实践的关键因素。     

高职有机化学抛锚式翻转课堂教学模式的构建和实践

以高职有机化学课程为例，探讨了抛锚式翻转课堂教学模式的构建和实践。主要包括：课前微课视频的制作、课堂组织、课后拓展、辅导和教学评价等环节。抛锚式教学模式和翻转课堂的结合，可以调动学生学习的主动性和积极性，使学生成为课程学习的主体，有利于有机化学课程的教学，有利于培养更多更好的新时代药学专业技术技能人才。     

激励机制辅助的翻转课堂教学模式的应用——以有机化学实验为例

针对我校有机化学实验课程传统教学模式中存在的弊端,探索了激励机制辅助的"翻转课堂"教学模式,并以正溴丁烷的制备实验为例,介绍了课前、课中以及课后的教师和学生工作内容以及教学评价。实践表明:激励机制辅助的"翻转课堂"教学模式,极大地调动了学生自主学习的积极性和主动性,且激励机制可以更有效地保证"翻转课堂"的顺利实施。     

大学无机化学“原电池”翻转课堂教学研究

通过翻转课堂与传统课堂在学习目标与教学流程上的对比,详细阐述了"原电池"翻转课堂的教学设计特点与实践研究过程,即课前利用微视频和学习单自主学习形成基本概念,课堂上通过解决问题实现重要概念内化,课后通过开放性问题体验原电池的价值。实践结果显示,"原电池"翻转课堂教学设计与实践能够更好地促进学生的发展,并在一定程度上优于传统课堂教学。     

“互联网+教育”助力分析化学课程群建设

在"互联网+教育"和传统课堂教育交融时期,将微课融入传统课堂,逐步向慕课、翻转课程过渡,将专业基础课程和方向课程组建成分析化学课程群新体系,优化教学内容,传授学科思想,提升学生的专业学习兴趣和教学质量。     

翻转课堂教学模式在普通化学教学中的应用与评价

首次探讨了在普通化学民族班教学中应用"翻转课堂"的教学模式,并就教学效果进行了评估。翻转课堂教学模式充分调动了民族班学生的学习积极性和主动性,同时提高了教师的教学水平,取得了较好的教学效果。为在对母语为非汉语的学生开设的普通化学课程教学中逐步采用"翻转课堂"教学模式替代传统教学模式提供了成功的经验。     

高分子材料类课程“翻转课堂”的实践经验交流

介绍了作者在高分子材料类课程(材料科学研究方法)教学改革中的一些经验体会。作者将"翻转课堂"理念融入教学过程,让学生课后分组收集、整理和理解高分子材料领域内不同的研究前沿知识,然后在课堂上让学生进行演讲,并进行学生之间以及学生与教师之间的互动,激发了学生的学习兴趣,获得了良好的教学效果!     

以任务或项目为驱动的翻转课堂——3种模式在国际高中化学教学中的实践

基于3种类型的翻转课堂——任务导向型翻转课堂、展示型翻转课堂和项目驱动式翻转课堂在国际高中化学课程中的实践过程,探讨了如何在把握以学生为中心的翻转课堂本质的基础上,通过融入任务导向或项目驱动进一步发挥教师的专业引导作用,强化课前和课内连接而成的有机整体,将学生个性化自主学习的效率最大化,并最终引导学生成长为有后续学习能...     

基于翻转课堂的《高聚物成型加工》教学模式初探

翻转课堂的教学模式可以为学生提供更多主动参与学习的机会,缓解了传统课堂中忽视学生自主学习和缺乏教学针对性的问题,从而受到了教研工作者的普遍关注。本文在分析《高聚物成型加工》的课程特点的基础上,提出根据课程性质和课程结构的差异性进行不同形式课程翻转的思想,将《高聚物成型加工》课程划分为理论基础和成型工艺两个部分,分别采用不同的翻转课堂教学模式,并从自主学习的有效性和学习时间的有限性对翻转教学的难点进行分析,并提出了解决方法。     

翻转课堂模式在有机化学实验教学中的探索与实践

针对当前有机化学实验课程存在重讲授轻实验、重验证轻探究、重结果轻过程等弊端,学生积极性和主动性受抑制等问题,我们将翻转课堂模式应用于有机化学实验的教学中进行探索和实践。改变课前和课中学习方式,课前通过信息技术手段完成知识传授过程,课中师生研讨完成知识内化过程,将教学模式从"以教为主"转变为"以学为主",提高了学生的学习积极性、课堂参与度,培养了学生的自主学习、团队协作、表达交流等能力。我们发现翻转课堂模式下,学生在实验操作失败率、实验产物收率、实验报告质量、理论考试成绩等实验课程综合能力上获得提高,有机化学实验课程教学质量得到进一步提升。     

人工神经网络在溶解度预测方面的应用

溶解度作为一项重要的物化指标,一直是化学学科的研究重点。然而,通过实验测量获得数据耗时费力,因此,科研人员建立了多种理论方法来进行估算,其中,人工神经网络因其能够关联复杂的多变量情况而受到广泛关注。本文综述了人工神经网络在物质溶解度预测方面的应用,介绍了应用最广泛的3种神经网络(BP神经网络、小波神经网络、径向基神经网络)的模型结构、预测方法和预测优势,探讨了神经网络的不足以及改进方法。文章最后对神经网络在物质溶解度预测方面的发展前景进行了展望。与其他方法相比,人工神经网络技术在物质溶解度预测方面具有预测结果精确度高、操作简单等特点,具有广阔的应用前景,但输入变量选择、隐含层节点数确定、避免局部最优等问题还需逐步建立系统的理论指导。     

阴离子嵌段共聚的交联聚苯乙烯的网络链长多分散性与网络结构非均一性

用链长分布不同的活性聚苯乙烯子聚物与二乙烯基苯进行阴离子嵌段共聚,合成了一系列两相模型交联网络。以作者等提出的方法测定了所合成网络的结构非均一因子Z。实验测定的网络结构非均一因子Z与交联前聚苯乙烯活性链的分子量分布宽度指数d之间有平行的相应变化规律,表明所给予的非均一因子Z的物理意义是合理的。同时说明,子聚物链长分布较宽时,在网络的高交联区中除了二乙烯基苯外,还有一些对溶胀无贡献的子聚物以悬挂链的形式存在。     

Estimating correlation energy of diatomic molecules and atoms with neural networks

The electronic correlation energy of diatomic molecules and heavy atoms is estimated using a back propagation neural network approach. The supervised learning is accomplished using known exact results of the electronic correlation energy. The recall rate, that is, the performance of the net in recognizing the training set, is about 96%. The correctness of values given to the test set and prediction rate is at the 90% level. We generate tables for the electronic correlation energy of several diatomic molecules and all the neutral atoms up to radon (Rn). © 1997 by John Wiley & Sons, Inc. J Comput Chem 18 : 1407–1414, 1997     

Cationic polymerization of diglycidyl ether of Bisphenol A. II. theory

Network formation in cationic polymerization of polyepoxides was treated using a combination of kinetic theory and the statistical theory of branching processes (TBP). The complex reaction mechanism involved a degradative termination reaction affecting the molecular weight distribution of the primary chains. Moreover, formation of low-molecular-weight products, initiation, two mechanisms of propagation and transfer were taken into account. © 1997 John Wiley & Sons, Inc. J Polym Sci A: Polym Chem 35: 651–663, 1997     

Applicability of Statistical Theories of Network Formation

Summary: The basis of the statistical method of generation of branched and crosslinked structures as a Markov process is analyzed with special emphasis on the effect of differences in reactivities of functional groups. For irreversible reactions, it is important that the transition probabilities are calculated kinetically and that bonds once formed are not allowed to be reformed. The statistical methods are often a good approximation of real situation even if not all approximations used in the model are fulfilled. Cyclization is still a serious problem in modeling; For practical application it is recommended to rescale calculated ring-free dependences against the experimental gel point.     

毛细管电泳-小波神经网络法辅佐诊断乳腺癌的研究

采用毛细管电泳法测定了46个健康人和26个乳腺癌病人尿样中的13种正常核苷和修饰核苷,以小波神经网络作为模式识别工具对健康人和乳腺癌病人的分类作了研究,随机选取的训练集的识别率达到100％,相应的预测集判别率正确性在96％以上,与经典的前向多层神经网络相比,小波神经网络具有更强的信息提取和逼近能力.研究结果还表明,小波神经网络的预测能力强于主成分分析和线性判别分析,毛细管电泳法与小波神经网络的结合有望成为乳腺癌的辅助诊断手段.     

ChemNetworks: A complex network analysis tool for chemical systems

Many intermolecular chemical interactions persist across length and timescales and can be considered to form a “network” or “graph.” Obvious examples include the hydrogen bond networks formed by polar solvents such as water or alcohols. In fact, there are many similarities between intermolecular chemical networks like those formed by hydrogen bonding and the complex and distributed networks found in computer science. Contemporary network analyses are able to dissect the complex local and global changes that occur within the network over multiple time and length scales. This work discusses the ChemNetworks software, whose purpose is to process Cartesian coordinates of chemical systems into a network/graph formalism and apply topological network analyses that include network neighborhood, the determination of geodesic paths, the degree census, direct structural searches, and the distribution of defect states of network. These properties can help to understand the network patterns and organization that may influence physical properties and chemical reactivity. The focus of ChemNetworks is to quantitatively describe intermolecular chemical networks of entire systems at both the local and global levels and as a function of time. The code is highly general, capable of converting a wide variety of systems into a chemical network formalism, including complex solutions, liquid interfaces, or even self‐assemblies. © 2013 Wiley Periodicals, Inc.     

超分子三维氢键网络：{[Cu(phen)3](SO4)(H3PCA)2(8H2O)}的晶体结构

A new complex， {[Cu(phen)₃](SO₄)(H₃PCA)₂(8H₂O)}， was synthesized and structurally characterized by single-crystal X-ray analysis. The complex is composed of copper cations， sulfate anions， 1，10-phenanthroline， protocatechuic acid and lattice water molecules. The structure of H₃PCA， SO₄^2- and waters comprises packing of anionic three-dimensional network by hydrogen bonds with cavities. The complex can be considered as a model of host/guest supramolecule. The three-dimensional hydrogen-bonding network is the host species. The Cu(phen)₃²⁺ cations， guest species， occupied the cavities of the host. And the results demonstrate that the form of protocatechuic acid at pH< 1 should be free ligand. CCDC： 191733.     

Characterisation and effect of polymer network deformation in reverse-mode polymer-stabilised cholesteric texture

In reverse-mode polymer-stabilised cholesteric texture (PSCT), the dynamic response is derived from local director reorientation governed by dielectric coupling effect/self-assembly and polymer network deformation. A double exponential rise/decay model is proposed to investigate the underlying physical mechanisms. Through simulation of the transient rise and decay processes, the polymer network deformation in PSCT can be quantitatively evaluated. Less deformation and faster restoration speed of the polymer network can suppress hysteresis. These results provide useful guidelines for future PSCT fabrication and performance optimisation.