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Related Concept Videos

Thermal Sigmatropic Reactions: Overview01:16

Thermal Sigmatropic Reactions: Overview

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
Sigmatropic shifts are classified based on an order term [i, j ], where i and j indicate the number of atoms across which each end of the σ bond migrates. Below are examples of a [3,3] sigmatropic shift in 1,5-hexadiene, referred...
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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Thermodynamic Systems01:06

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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
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Thermodynamics: Chemical Potential and Activity01:10

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The effective concentration of a species in a solution can be expressed precisely in terms of its activity. Activity considers the effect of electrolytes present in the vicinity of the species of interest and depends on the ionic strength of the solution. The activity of a species is expressed as the product of molar concentration and the activity coefficient of the species.
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Multiscale Cross-Domain Thermochemical Knowledge-Graph.

Sebastian Mosbach1,2, Angiras Menon1, Feroz Farazi1

  • 1Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom.

Journal of Chemical Information and Modeling
|November 26, 2020
PubMed
Summary
This summary is machine-generated.

Software agents enhance thermodynamic data in a knowledge graph using quantum calculations. This improves data accuracy and enables cross-domain applications, like predicting pollutant dispersion.

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Area of Science:

  • Computational chemistry
  • Data science
  • Semantic Web technologies

Background:

  • Chemical species thermodynamic data is crucial for many scientific and industrial applications.
  • Existing knowledge graphs often lack dynamic updating capabilities and interoperability.
  • Integrating diverse data sources, including quantum chemical calculations, remains a challenge.

Purpose of the Study:

  • To develop autonomous software agents for improving a thermodynamic data knowledge graph.
  • To enhance data accuracy through quantum chemical calculations and error-canceling reactions.
  • To demonstrate the integration of chemical data with broader applications, such as environmental impact assessment.

Main Methods:

  • Utilizing linked data principles and ontologies to represent species-associated information.
  • Implementing autonomous software agents to perform quantum chemical calculations.
  • Deriving standard enthalpies of formation and updating the knowledge graph with new results.
  • Extending the J-Park Simulator knowledge graph and its agent ecosystem.

Main Results:

  • Successfully improved the thermodynamic data within the knowledge graph.
  • Demonstrated the agents' ability to add new data and refine existing values.
  • Showcased seamless cross-domain application by linking quantum calculations to industrial emission scenarios.
  • Validated the approach through a use-case on atmospheric pollutant dispersion.

Conclusions:

  • The developed software agents effectively enhance thermodynamic data knowledge graphs.
  • The approach facilitates interoperability and enables advanced data-driven applications.
  • Integrating computational chemistry with Semantic Web technologies offers significant potential for scientific discovery and practical problem-solving.