基于深度学习的“实验方案设计”主题式复习教学——以“阿司匹林的制备和提纯”为例

针对实验方案设计题的得分率分析和教学现状,基于课程标准、教材、真题和文献研究,构建“阿司匹林的制备和提纯”深度学习主题模型。通过设计制备和提纯阿司匹林的实验方案,构建实验方案设计的思维模型,并以制备七水合硫酸锌为例内化思维模型,在解决真实、复杂的问题过程中实现核心知识结构化,生成指向深度学习的主题式复习教学流程。     

基于奥苏贝尔学习理论对人教版和鲁科版高中化学教材“化学键”的比较及建议

以奥苏贝尔学习理论为基础,比较人教版与鲁科版化学教材“化学键”内容,对教材的引言、正文编排顺序、正文内容组织、正文栏目、插图、习题等进行分析,并从奥苏贝尔学习理论角度阐述教材编写及教学建议。     

初中化学“主题式项目化学习”的理念与实践*

初中化学“主题式项目化学习”是学生在主题情境中,从先前项目经验中汲取灵感,对真实且具有内在联系的驱动型任务进行深入探究的学习方式。“主题式项目化学习”的各个项目不是独立的,而是具有层层递进的内在联系,并被整合进一个主题情境中。不同项目之间的“相似性”为学生提供了迁移和应用知识的机会,而“相异性”又迫使学生审慎思考知识的适用范围。新旧项目的不断交互串联起了学生的学习历程,使经验连续生长、认知不断发展。     

高中有机化学教学中提升核心素养并体味科学价值*——以“乙炔的化学性质”为例

介绍了“乙炔的化学性质”的教学设计思路,展现了如何利用创新实验、丰富素材和问题讨论,帮助学生理解有机物性质和结构之间的关系。主要涉及以下几个环节:(1)通过课前自主预习,从乙炔与乙烯的相似性入手,预测乙炔的性质,了解乙炔的实验室制法;(2)结合2个创新分组实验“基于井穴板的微型化气体连续制备和性质检验装置”“利用真空输液瓶、微型样品瓶和注射器的密闭气体微型反应装置”,教师和学生一起巩固了乙炔的实验室制法,验证了预测的化学性质,丰富了对乙炔性质的感性认知,发现了“乙炔不能使酸性K₂Cr₂O₇溶液褪色”的异常现象;(3)从尝试解释异常现象入手,教师和学生一起从键能、加成比例、聚合后主链结构等多个角度探讨了乙炔与乙烯性质和结构的差异,建立起有机物官能团与性质关系的认知模型;(4)通过尝试利用模型解释2000年诺贝尔化学奖“导电高分子”和2017年度国家自然科学成果一等奖“AIE聚集诱导发光材料” 的结构、性质与应用,教师和学生一起提升了对认知模型的理解建构,感悟了科学研究的乐趣,体会化学科学在促进人类文明发展和社会进步中的重要作用。     

基于实践创新素养发展的高中教学设计——以“合成高分子”为例

实践创新素养是中学生核心素养的重要组成部分。以选择性必修模块3中“合成高分子”内容为例,以“棉粮争地”问题为背景,以项目式研究的形式在化学课堂上进行实践创新素养的教学尝试,并取得较好的结果。同时还引入了班内分层教学的尝试,为因材施教进行了有效的尝试。     

人工智能在锂离子电池研发中的应用

锂离子电池已成为解决现代社会储能问题的最佳解决方案之一。然而,电池材料和器件开发都是复杂的多变量问题,传统的依赖研究人员进行实验的试错法在电池性能提升方面遇到了瓶颈。人工智能（AI）具有强大的高速、海量数据处理能力,是上述突破研究瓶颈的最具潜力的技术。其中,机器学习 (ML) 算法在评估多维数据变量和集合之间的组合关联方面的独特优势有望帮助研究人员发现不同因素之间的相互作用规律并阐明材料合成和设备制造的机制。本综述总结了锂离子电池传统研究方法遇到的各种挑战,并详细介绍了人工智能在电池材料研究、电池器件设计与制造、材料与器件表征、电池循环寿命与安全性评估等方面的应用。最重要的是,我们介绍了AI和ML在电池研究中面临的挑战,并讨论了它们应用的缺点和前景。我们相信,未来实验科学家、数学建模专家和AI专家之间更紧密的合作将极大地促进AI和ML方法用以解决传统方法难以克服的电池和材料问题。     

Predictive modeling and computational machine learning simulation of adsorption separation using advanced nanocomposite materials

Adsorption process was simulated in this study for removal of Hg and Ni from water using nanocomposite materials. The used nanostructured material for the adsorption study was a combined MOF and layered double hydroxide, which is considered as MOF-LDH in this work. The data were obtained from resources and different machine learning models were trained. We selected three different regression models, including elastic net, decision tree, and Gradient boosting, to make regression on the small data set with two inputs and two outputs. Inputs are Ion type (Hg or Ni) and initial ion concentration in the feed solution (C₀), and outputs are equilibrium concentration (Ce) and equilibrium capacity of the adsorbent (Qe) in this dataset. After tuning their hyper-parameters, final models were implemented and assessed using different metrics. In terms of the R2-score metric, all models have more than 0.97 for Ce and more than 0.88 for Qe. The Gradient Boosting has an R2-score of 0.994 for Qe. Also, considering RMSE and MAE, Gradient Boosting shows acceptable errors and best models. Finally, the optimal values with the GB model are identical to dataset optimal: (Ion = Ni, C₀ = 250, Ce = 206.0). However, for Qe, it is different and is equal to (Ion = Hg, C0 = 121.12, Ce = 606.15). The results revealed that the developed methods of simulation are of high capacity in prediction of adsorption for removal of heavy metals using nanostructure materials.     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.