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Titration Calculations: Strong Acid - Strong Base02:28

Titration Calculations: Strong Acid - Strong Base

34.0K
Calculating pH for Titration Solutions: Strong Acid/Strong Base
A titration is carried out for 25.00 mL of 0.100 M HCl (strong acid) with 0.100 M of a strong base NaOH. The pH at different volumes of added base solution can be calculated as follows:
(a) Titrant volume = 0 mL. The solution pH is due to the acid ionization of HCl. Because this is a strong acid, the ionization is complete and the hydronium ion molarity is 0.100 M. The pH of the solution is then:
34.0K
Strong Acid and Base Solutions03:22

Strong Acid and Base Solutions

35.8K
A strong acid is a compound that dissociates completely in an aqueous solution and produces a concentration of hydronium ions equal to the initial concentration of acid. For example, 0.20 M hydrobromic acid will dissociate completely in water and produces 0.20 M of hydronium ions and 0.20 M of bromide ions.
35.8K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Titration of a Strong Acid with a Strong Base01:23

Titration of a Strong Acid with a Strong Base

10.5K
During the titration of a strong acid with a strong base, pH calculations are primarily based on the concentration of residual hydronium or hydroxide ions. Initially, a strong acid like hydrochloric acid fully dissociates, creating hydronium and chloride ions, resulting in a low pH. The addition of a strong base like sodium hydroxide alters the concentration of hydronium ions by neutralizing them. As more base is added, the pH gradually increases. At the equivalence point, all hydronium ions...
10.5K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Titration Calculations: Weak Acid - Strong Base03:55

Titration Calculations: Weak Acid - Strong Base

49.3K
Calculating pH for Titration Solutions: Weak Acid/Strong Base
For the titration of 25.00 mL of 0.100 M CH3CO2H with 0.100 M NaOH, the reaction can be represented as:
49.3K

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Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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NARX neural network model for strong resolution improvement in a distributed temperature sensor.

Luís Cicero Bezerra da Silva, Jorge Leonid Aching Samatelo, Marcelo Eduardo Vieira Segatto

    Applied Optics
    |August 18, 2018
    PubMed
    Summary

    This study introduces a new method for processing distributed temperature sensor data using a neural network. The approach enhances temperature and spatial resolution while reducing processing time.

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    Area of Science:

    • Sensor Technology
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Distributed temperature sensing (DTS) systems are crucial for monitoring.
    • Processing DTS data can be computationally intensive and complex.
    • Existing methods may struggle with signal noise and resolution limitations.

    Purpose of the Study:

    • To develop an efficient and robust method for processing DTS data.
    • To improve both the temperature and spatial resolution of DTS measurements.
    • To reduce the computational load and processing time for DTS signals.

    Main Methods:

    • A nonlinear autoregressive with external input (NARX) neural network was employed.
    • The model involves three stages: characteristic extraction, regression, and signal reconstruction.
    • The approach does not require prior knowledge of signal characteristics.

    Main Results:

    • Achieved a significant reduction in data processing requirements.
    • Demonstrated a low processing time for the sensor data.
    • Successfully improved spatial resolution to 5 cm.
    • Obtained full correction of temperature resolution.

    Conclusions:

    • The proposed NARX neural network approach offers a robust solution for DTS data processing.
    • This method enhances sensor performance by improving resolution and reducing processing time.
    • The technique is adaptable and does not rely on specific signal characteristic assumptions.