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Classification of Titrimetric Analysis Based on Reaction Types01:01

Classification of Titrimetric Analysis Based on Reaction Types

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Titrimetric analysis in solution chemistry involves measuring the volume of solutions and is often called volumetric analysis. The standard solution of known concentration in the burette is called the titrant, whereas the solution of unknown concentration in the flask is called the analyte, or titrand. Titrimetric analyses can be classified into four types based on the reactions between the titrant and analyte.
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Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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The periodic table arranges atoms based on increasing atomic number so that elements with the same chemical properties recur periodically. When their electron configurations are added to the table, a periodic recurrence of similar electron configurations in the outer shells of these elements is observed. Because they are in the outer shells of an atom, valence electrons play the most important role in chemical reactions. The outer electrons have the highest energy of the electrons in an atom...
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Classification of Elements and Compounds02:54

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Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Related Experiment Video

Updated: May 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A machine learning based classifier for topological quantum materials.

Ashiqur Rasul1, Md Shafayat Hossain2, Ankan Ghosh Dastider3

  • 1Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh. ashiqur.rasul@seu.edu.bd.

Scientific Reports
|December 31, 2024
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Summary
This summary is machine-generated.

This study introduces a deep learning model combining persistent homology and graph neural networks to efficiently classify topological materials. This approach accelerates the discovery of novel quantum materials by overcoming computational bottlenecks.

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Area of Science:

  • Quantum science and technology
  • Materials science
  • Computational chemistry

Background:

  • Discovering new materials with specific properties is crucial for quantum science and technology.
  • Ab initio calculations for materials discovery are computationally intensive, posing a significant bottleneck.
  • Classifying materials, especially topological versus non-topological, requires efficient and accurate methods.

Purpose of the Study:

  • To develop an effective and robust deep learning model for classifying topological materials.
  • To overcome the computational limitations of traditional methods in materials discovery.
  • To enable high-throughput searching of novel topological materials.

Main Methods:

  • A deep learning model integrating persistent homology with graph neural networks (GNNs) was developed.
  • The GNN component encodes atomic relationships based on crystalline structures, processing non-Euclidean data.
  • Persistent homology integrates topological analysis of crystal structures into the deep learning framework.

Main Results:

  • The proposed model achieved high accuracy and an F1 score in classifying topological materials, outperforming existing state-of-the-art classifiers.
  • The method demonstrates high confidence in classifying out-of-distribution and newly discovered topological materials.
  • The integration of GNNs and persistent homology enhances model robustness and performance.

Conclusions:

  • The developed deep learning classifier is an efficacious tool for predicting topological material classes.
  • This approach significantly accelerates the search for new topological materials.
  • The model offers a robust and efficient solution to a major bottleneck in quantum materials discovery.