Bridging the gap between structures and properties: An investigation and evaluation of students\u27 representational competence

The heart of learning chemistry is the ability to connect a compound\u27s structure to its function; Lewis structures provide an essential link in this process. In many cases, their construction is taught using an algorithmic approach, containing a set of step-by-step rules. We believe that this approach is in direct conflict with the precepts of meaningful learning. From a sequential, mixed methods study, we found that students have much difficulty constructing these structures and that the step-by-step rules do not make use of students\u27 relevant prior knowledge. This causes students to develop \u27home grown\u27 rules when unsure of how to progress with the construction process. It also became clear that most students are uncertain of the importance of Lewis structures since they perceive them as being useful only for obtaining structural information but not property information. Using responses from student interviews and open ended questions, the Information from Lewis Structures Survey (ILSS) was developed, validated, and found reliable to assess students\u27 representational competence by determining their understanding of the purpose of Lewis structures. Since students had many problems with the relationship of structures and properties, an alternative curriculum was evaluated to determine if it could help students develop a more meaningful understanding of this process. This instruction was part of a larger NSF-funded general chemistry curriculum redesign called Chemistry, Life, the Universe and Everything (CLUE). Using a control and treatment group, the effectiveness of this new curriculum was evaluated for two main aspects: 1. the students\u27 ability to construct Lewis structures using OrganicPad and 2. the students\u27 representational competence using the ILSS. Through four main studies (a pilot study, instructor effect study, main study, and retention study), we found that the CLUE curriculum helps students develop more expert-like strategies for constructing Lewis structures and a better understanding of why these structures are important by encouraging more meaningfully learning

Large contraction conducting polymer molecular actuators

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, February 2005.Vita. Leaf 239 blank.Includes bibliographical references.The development of powerful and efficient artificial muscles that mimic Nature will profoundly affect engineering sciences including robotics and prosthetics, propulsion systems, and microelectromechanical systems (MEMS). Biological systems driven by muscle out-perform human-engineered systems in many key aspects. For example, muscle endows animals with a level of dexterity and speed that has yet to be emulated by even the most complex robotic system to date. Conducting polymers were chosen for research as actuators, based on a review of the relevant properties of all known actuators and active materials. Key features of conducting polymer actuators include low drive voltages (1 - 2 V) and high active strength (10 - 40 MPa) but moderate active strains (2 %). Active strains of 20 %, which human skeletal muscle is capable of, are desirable for applications in life-like robotics, artificial prostheses or medical devices. This thesis focuses on two approaches to create large contraction in conducting polymer actuators. The first strategy utilizes polypyrrole (PPy), a conducting polymer actuator material that contracts and expands based on a bulk ion swelling mechanism. Optimization of the polymer activation environment via room temperature ionic liquids enables PPy actuators to generate large contractions (16.3 % recoverable strain at 2.5 MPa, 21 % max) at slow speeds (0.4 %/s). In addition, cycle life can reach 10⁵ cycles without significant polymer degradation. This thesis presents an in-depth characterization of the behavior of polypyrrole actuators in room temperature 1-butyl-3-methyl imidazolium tetrafluoroborate liquid salt electrolyte.(cont.) The characterization includes the assessment of passive and electroactive mechanical properties as well as electrical and morphological properties. Using Nature's actin-myosin molecular engine as a source of inspiration, the second approach uses molecular mechanisms to create motion. In this bottom-up approach molecules are rationally designed from the molecular level for specific actuation properties. Such active molecular building blocks include shape changing, load bearing, passively deformable or hinge-like molecular elements. Several novel materials based on contractile molecular design were synthesized and their active properties characterized.by Patrick A.T. Anquetil.Ph.D

Winthrop University Undergraduate Scholarship & Creative Activity 2018

University College and Winthrop University proudly present Undergraduate Scholarship and Creative Activity 2018. This seventh annual University-wide compilation of undergraduate work chronicles the accomplishments of students and faculty mentors from at least 32 academic departments and programs, spanning all five colleges of the university: College of Arts and Sciences (CAS), College of Business Administration (CBA), College of Education (COE), College of Visual and Performing Arts (CVPA) and University College (UC).https://digitalcommons.winthrop.edu/undergradresearch_abstractbooks/1016/thumbnail.jp

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Learning the Language of Chemical Reactions – Atom by Atom. Linguistics-Inspired Machine Learning Methods for Chemical Reaction Tasks

Over the last hundred years, not much has changed how organic chemistry is conducted. In most laboratories, the current state is still trial-and-error experiments guided by human expertise acquired over decades. What if, given all the knowledge published, we could develop an artificial intelligence-based assistant to accelerate the discovery of novel molecules? Although many approaches were recently developed to generate novel molecules in silico, only a few studies complete the full design-make-test cycle, including the synthesis and the experimental assessment. One reason is that the synthesis part can be tedious, time-consuming, and requires years of experience to perform successfully. Hence, the synthesis is one of the critical limiting factors in molecular discovery. In this thesis, I take advantage of similarities between human language and organic chemistry to apply linguistic methods to chemical reactions, and develop artificial intelligence-based tools for accelerating chemical synthesis. First, I investigate reaction prediction models focusing on small data sets of challenging stereo- and regioselective carbohydrate reactions. Second, I develop a multi-step synthesis planning tool predicting reactants and suitable reagents (e.g. catalysts and solvents). Both forward prediction and retrosynthesis approaches use black-box models. Hence, I then study methods to provide more information about the models’ predictions. I develop a reaction classification model that labels chemical reaction and facilitates the communication of reaction concepts. As a side product of the classification models, I obtain reaction fingerprints that enable efficient similarity searches in chemical reaction space. Moreover, I study approaches for predicting reaction yields. Lastly, after I approached all chemical reaction tasks with atom-mapping independent models, I demonstrate the generation of accurate atom-mapping from the patterns my models have learned while being trained self-supervised on chemical reactions. My PhD thesis’s leitmotif is the use of the attention-based Transformer architecture to molecules and reactions represented with a text notation. It is like atoms are my letters, molecules my words, and reactions my sentences. With this analogy, I teach my neural network models the language of chemical reactions - atom by atom. While exploring the link between organic chemistry and language, I make an essential step towards the automation of chemical synthesis, which could significantly reduce the costs and time required to discover and create new molecules and materials

Excellentia Eminentia Effectio

"In these pages you will learn about the fascinating research endeavors that each of our faculty members is undertaking. We have divided their research into the broad categories of health, sustainability, information, and systems. While we recognize the imperfect nature of categorizing research that, by its very nature may be interdisciplinary or transdisciplinary, we nonetheless believe it will be helpful as a way to see the depth and breadth of our research endeavors within each grouping. As you read the profiles on these pages, I know you will begin to appreciate that, taken as a whole, the research spectrum at Columbia Engineering is exceptional and that, as our professors go about their work, they are at the cusp of making breakthroughs that will have a major impact on the way we live our lives today and tomorrow.

Science for Primary Teachers

The general aim of this textbook was to provide some basic knowledge of how science works. After all, we live in a very science-oriented and techno world. We also wanted to make it easier for you to get your pupils interested in science

Expanding Eco-Visualization: Sculpting Corn Production

This dissertation expands upon the definition of eco-visualization artwork. EV was originally defined in 2006 by Tiffany Holmes as a way to display the real time consumption statistics of key environmental resources for the goal of promoting ecological literacy. I assert that the final forms of EV artworks are not necessarily dependent on technology, and can differ in terms of media used, in that they can be sculptural, video-based, or static two-dimensional forms that communicate interpreted environmental information. There are two main categories of EV: one that is predominantly screen-based and another that employs a variety of modes of representation to visualize environmental information. EVs are political acts, situated in a charged climate of rising awareness, operating within the context of environmentalism and sustainability. I discuss a variety of EV works within the frame of ecopsychology, including EcoArtTech’s Eclipse and Keith Deverell’s Building Run; Andrea Polli’s Cloud Car and Particle Falls; Nathalie Miebach’s series, The Sandy Rides; and Natalie Jeremijenko’s Mussel Choir. The range of EV works provided models for my creative project, Sculpting Corn Production, and a foundation from which I developed a creative methodology. Working to defeat my experience of solastalgia, Sculpting Corn Production is a series of discrete paper sculptures focusing on American industrial corn farming. This EV also functions as a way for me to understand our devastated monoculture landscapes and the politics, economics, and related areas of ecology of our food production

2022 - The Third Annual Fall Symposium of Student Scholars

The full program book from the Fall 2022 Symposium of Student Scholars, held on November 17, 2022. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1026/thumbnail.jp

Neural function approximation on graphs: shape modelling, graph discrimination & compression

Graphs serve as a versatile mathematical abstraction of real-world phenomena in numerous scientific disciplines. This thesis is part of the Geometric Deep Learning subject area, a family of learning paradigms, that capitalise on the increasing volume of non-Euclidean data so as to solve real-world tasks in a data-driven manner. In particular, we focus on the topic of graph function approximation using neural networks, which lies at the heart of many relevant methods. In the first part of the thesis, we contribute to the understanding and design of Graph Neural Networks (GNNs). Initially, we investigate the problem of learning on signals supported on a fixed graph. We show that treating graph signals as general graph spaces is restrictive and conventional GNNs have limited expressivity. Instead, we expose a more enlightening perspective by drawing parallels between graph signals and signals on Euclidean grids, such as images and audio. Accordingly, we propose a permutation-sensitive GNN based on an operator analogous to shifts in grids and instantiate it on 3D meshes for shape modelling (Spiral Convolutions). Following, we focus on learning on general graph spaces and in particular on functions that are invariant to graph isomorphism. We identify a fundamental trade-off between invariance, expressivity and computational complexity, which we address with a symmetry-breaking mechanism based on substructure encodings (Graph Substructure Networks). Substructures are shown to be a powerful tool that provably improves expressivity while controlling computational complexity, and a useful inductive bias in network science and chemistry. In the second part of the thesis, we discuss the problem of graph compression, where we analyse the information-theoretic principles and the connections with graph generative models. We show that another inevitable trade-off surfaces, now between computational complexity and compression quality, due to graph isomorphism. We propose a substructure-based dictionary coder - Partition and Code (PnC) - with theoretical guarantees that can be adapted to different graph distributions by estimating its parameters from observations. Additionally, contrary to the majority of neural compressors, PnC is parameter and sample efficient and is therefore of wide practical relevance. Finally, within this framework, substructures are further illustrated as a decisive archetype for learning problems on graph spaces.Open Acces