Using data-driven activities with ChemEd X Data to practice structure-property relationships in General Chemistry

AbstractGeneral Chemistry covers a wide variety of structure-property relationships that rely upon electronic, atomic, crystal or molecular factors. Giving students experimental data will allow them to identify the structure-property patterns as well as identify the limit of predictability of such patterns. “ChemEd X Data” is a web interface designed by the author that facilitates the navigation, filtering and graphical representation of chemical and physical data. It can assist students at identifying trends in structure-property relationships, they can create controlled experiments to test a relationship as well as investigating how different molecular factors may affect a single macroscopic property. In particular, since the site offers unstructured but dynamically searchable data, it is designed to have students learn control of variable strategies (CVS). This paper describes the implementation of a five-step sequence of activities related to structure-property relationships in a General Chemistry semester. ChemEd X Data is used for the open-ended or data-driven steps of this sequence. Student performance is analyzed with the objective of understanding which activities require a higher cognitive skill, as well as identify student previous performances that correlate with success in the activities and in the course in general.

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Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks

Molecular Systems Design & Engineering ◽

10.1039/d0me00020e ◽

2020 ◽

Vol 5 (5) ◽

pp. 962-975

Author(s):

Yixing Wang ◽

Min Zhang ◽

Anqi Lin ◽

Akshay Iyer ◽

Aditya Shanker Prasad ◽

...

Keyword(s):

Neural Networks ◽

Polymer Nanocomposites ◽

Data Driven ◽

Learning Approach ◽

Structure Property ◽

Element Analysis ◽

Structure Property Relationships ◽

Structure Property Relationship ◽

Using Data ◽

Relationship Of

In this paper, a data driven and deep learning approach for modeling structure–property relationship of polymer nanocomposites is demonstrated. This method is applicable to understand other material mechanisms and guide the design of material with targeted performance.

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Valency-Based Topological Properties of Linear Hexagonal Chain and Hammer-Like Benzenoid

Complexity ◽

10.1155/2021/9939469 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yi-Xia Li ◽

Abdul Rauf ◽

Muhammad Naeem ◽

Muhammad Ahsan Binyamin ◽

Adnan Aslam

Keyword(s):

Graphical Representation ◽

Structural Features ◽

Topological Indices ◽

Quantitative Structure ◽

Structure Property ◽

Hexagonal Chain ◽

Structure Property Relationships ◽

Quantitative Measurements ◽

Qsar Modelling ◽

Quantitative Structure Property Relationships

Topological indices are quantitative measurements that describe a molecule’s topology and are quantified from the molecule’s graphical representation. The significance of topological indices is linked to their use in QSPR/QSAR modelling as descriptors. Mathematical associations between a particular molecular or biological activity and one or several biochemical and/or molecular structural features are QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships). In this paper, we give explicit expressions of two recently defined novel ev-degree- and ve-degree-based topological indices of two classes of benzenoid, namely, linear hexagonal chain and hammer-like benzenoid.

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Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing

Computational Mechanics ◽

10.1007/s00466-018-1539-z ◽

2018 ◽

Vol 61 (5) ◽

pp. 521-541 ◽

Cited By ~ 48

Author(s):

Wentao Yan ◽

Stephen Lin ◽

Orion L. Kafka ◽

Yanping Lian ◽

Cheng Yu ◽

...

Keyword(s):

Additive Manufacturing ◽

Data Driven ◽

Structure Property ◽

Multi Scale ◽

Structure Property Relationships ◽

Process Structure

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Multifractal investigation of Ag/DLC nanocomposite thin films

Scientific Reports ◽

10.1038/s41598-020-79455-z ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Ştefan Ţălu ◽

Bandar Astinchap ◽

Senour Abdolghaderi ◽

Azizollah Shafiekhani ◽

Ilya A. Morozov

Keyword(s):

Graphical Representation ◽

Three Dimensional ◽

Rf Sputtering ◽

Chemical Vapor ◽

Structure Property ◽

Force Microscopy ◽

Additional Information ◽

Structure Property Relationships ◽

Surface Micromorphology ◽

Novel Algorithms

AbstractThe objective of this study is the experimental investigation of the silver in diamond-like carbon (Ag/DLC) nanocomposite prepared by the co-deposition of radio frequency plasma-enhanced chemical vapor deposition (RF-PECVD) and RF-sputtering. Atomic force microscopy (AFM), X-ray diffraction analyses, ultraviolet–visible (UV–visible) spectroscopy measurements were applied to describe the three-dimensional surface texture data in connection with the statistical, and multifractal analyses. Additional information about structure–property relationships in prepared Ag/DLC nanocomposite was studied in detail to allow a better understanding of the surface micromorphology. The performed analysis revealed the studied samples have multifractal properties and can be included in novel algorithms for graphical representation of complex geometrical shapes and implemented in computer simulation algorithms.

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Statistics, damned statistics and nanoscience – using data science to meet the challenge of nanomaterial complexity

Nanoscale Horizons ◽

10.1039/c5nh00126a ◽

2016 ◽

Vol 1 (2) ◽

pp. 89-95 ◽

Cited By ~ 20

Author(s):

Baichuan Sun ◽

Michael Fernandez ◽

Amanda S. Barnard

Keyword(s):

Digital Technology ◽

Data Science ◽

Structure Property ◽

Nano Structure ◽

Detailed Understanding ◽

Structure Property Relationships ◽

Modern Methods ◽

Using Data ◽

The Way

Combining advances in digital technology and modern methods in statistics with a detailed understanding of nano-structure/property relationships can pave the way for more realistic predictions of nanomaterials performance.

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Investigating the impact of three-dimensional learning interventions on student understanding of structure–property relationships

Chemistry Education Research and Practice ◽

10.1039/d0rp00216j ◽

2021 ◽

Author(s):

Sonia M. Underwood ◽

Alex T. Kararo ◽

Gabriela Gadia

Keyword(s):

General Chemistry ◽

Active Learning ◽

Boiling Point ◽

Three Dimensional ◽

Intermolecular Forces ◽

Control Group ◽

Chemistry Curriculum ◽

Structure Property ◽

Structure Property Relationships ◽

The Impact

The ability to predict macroscopic properties using a compound's chemical structure is an essential idea for chemistry as well as other disciplines such as biology. In this study we investigate how different levels of interventions impact the components of students’ explanations (claims, evidence, and reasoning) of structure–property relationships, particularly related to boiling point trends. These interventions, aligned with Three-Dimensional Learning (3DL), were investigated with four different cohorts of students: Cohort 1 – a control group of students enrolled in an active learning general chemistry course; Cohort 2 – students enrolled in the same active learning general chemistry course but given Intervention 1 (a 3DL worksheet administered during class time); Cohort 3 – students enrolled in the same active learning general chemistry course but given Intervention 1 and Intervention 2 (a 3DL course exam question administered after instruction); and Cohort 4 – a reference group of students enrolled in a transformed active learning general chemistry curriculum in which 3DL is an essential feature and includes Intervention 1 and Intervention 2 as part of the curriculum. We found that Cohort 2 students (with the 3DL worksheet intervention) were more likely than the control group (Cohort 1) to correctly predict the compound with a higher boiling point as well as incorporate ideas of strength of intermolecular forces into their explanations of boiling point differences. When a 3DL exam question was given as a follow up to the 3DL worksheet, students in Cohort 3 were more likely than Cohorts 1 and 2 to correctly identify the claim. Further comparison showed that Cohort 4 (transformed general chemistry curriculum) were more likely than Cohorts 1–3 to also include the ideas of energy needed to overcome stronger forces for a more sophisticated explanation (50% of Cohort 4 students compared to 17–33% for Cohorts 1–3). In addition, 80% of Cohort 4 students were able to construct a correct representation of hydrogen bonding as a non-covalent interaction compared to 13–57% for the other three cohorts.

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Modeling process-structure-property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches

Journal of Physics Materials ◽

10.1088/2515-7639/abca7b ◽

2020 ◽

Author(s):

Nadia Kouraytem ◽

Xuxiao Li ◽

Wenda Tan ◽

Branden Kappes ◽

Ashley Spear

Keyword(s):

Additive Manufacturing ◽

Data Driven ◽

Structure Property ◽

Metal Additive Manufacturing ◽

Versus Data ◽

Structure Property Relationships ◽

Modeling Process ◽

Process Structure ◽

Metal Additive

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Structure-property relationships of PE/PP sandwiched films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100126998 ◽

1987 ◽

Vol 45 ◽

pp. 454-455

Author(s):

J. Petermann ◽

G. Broza ◽

U. Rieck ◽

A. Jaballah ◽

A. Kawaguchi

Keyword(s):

Mechanical Tests ◽

Polymer Materials ◽

Ionic Crystals ◽

Structure Property ◽

Polymer Systems ◽

Structure Property Relationships ◽

Oriented Films ◽

Materials Used ◽

Polymer Laminates ◽

Heated Glass

Oriented overgrowth of polymer materials onto ionic crystals is well known and recently it was demonstrated that this epitaxial crystallisation can also occur in polymer/polymer systems, under certain conditions. The morphologies and the resulting physical properties of such systems will be presented, especially the influence of epitaxial interfaces on the adhesion of polymer laminates and the mechanical properties of epitaxially crystallized sandwiched layers.Materials used were polyethylene, PE, Lupolen 6021 DX (HDPE) and 1810 D (LDPE) from BASF AG; polypropylene, PP, (PPN) provided by Höchst AG and polybutene-1, PB-1, Vestolen BT from Chemische Werke Hüls. Thin oriented films were prepared according to the method of Petermann and Gohil, by winding up two different polymer films from two separately heated glass-plates simultaneously with the help of a motor driven cylinder. One double layer was used for TEM investigations, while about 1000 sandwiched layers were taken for mechanical tests.

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Approaches for TEM imaging of cavitation in toughened nylon

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153737 ◽

1989 ◽

Vol 47 ◽

pp. 352-353

Author(s):

Barbara A. Wood

Keyword(s):

Morphological Characteristics ◽

Structure Property ◽

Polymer Systems ◽

Selective Staining ◽

Structure Property Relationships ◽

Rubber Toughened ◽

Shear Yield ◽

Controversial Topic ◽

Selection Of ◽

Diamond Knife

A controversial topic in the study of structure-property relationships of toughened polymer systems is the internal cavitation of toughener particles resulting from damage on impact or tensile deformation.Detailed observations of the influence of morphological characteristics such as particle size distribution on deformation mechanisms such as shear yield and cavitation could provide valuable guidance for selection of processing conditions, but TEM observation of damaged zones presents some experimental difficulties.Previously published TEM images of impact fractured toughened nylon show holes but contrast between matrix and toughener is lacking; other systems investigated have clearly shown cavitated impact modifier particles. In rubber toughened nylon, the physical characteristics of cavitated material differ from undamaged material to the extent that sectioning of heavily damaged regions by cryoultramicrotomy with a diamond knife results in sections of greater than optimum thickness (Figure 1). The detailed morphology is obscured despite selective staining of the rubber phase using the ruthenium trichloride route to ruthenium tetroxide.

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