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Related Concept Videos

Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
Gas Chromatography–Mass Spectrometry (GC–MS)01:14

Gas Chromatography–Mass Spectrometry (GC–MS)

Gas chromatography–mass spectrometry (GC–MS) is the combination of analytical techniques of gas chromatography and mass spectrometry in a single instrument for analyzing a mixture of compounds. The gas chromatograph separates the compounds in the mixture, and the mass spectrometer analyzes each compound separately to determine the molecular masses and molecular structures.
A gas chromatograph consists of a long, narrow capillary column with a polysiloxane coating on the inner wall. The coating...

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Encapsulating Cytochrome c in Silica Aerogel Nanoarchitectures without Metal Nanoparticles while Retaining Gas-phase Bioactivity
11:06

Encapsulating Cytochrome c in Silica Aerogel Nanoarchitectures without Metal Nanoparticles while Retaining Gas-phase Bioactivity

Published on: March 1, 2016

Cytochrome c biosensor--a model for gas sensing.

Michael Hulko1, Ingeborg Hospach, Nadejda Krasteva

  • 1Sony Deutschland GmbH, Materials Science Laboratory, Stuttgart, Germany. hulko@sony.de

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

A new mathematical model predicts biosensor responses for gas sensing applications. This cytochrome c biosensor model aids in early-stage development by analyzing complex sensing steps.

Keywords:
biosensorcytochrome cmodelpredictionsensing processthiol

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An Anaerobic Biosensor Assay for the Detection of Mercury and Cadmium
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Area of Science:

  • Biosensors
  • Chemical Sensing
  • Mathematical Modeling

Background:

  • Gas biosensing utilizes cytochrome c for detecting specific gases.
  • Understanding the multi-step sensing process is crucial for biosensor development.
  • Accurate prediction of biosensor response aids in early-stage design and optimization.

Purpose of the Study:

  • To develop and validate a mathematical model for predicting cytochrome c biosensor responses to gases.
  • To quantitatively describe the individual steps involved in the gas biosensing process.
  • To demonstrate the model's applicability in methanethiol gas detection.

Main Methods:

  • Analysis of individual sensing steps: phase partition equilibrium, intermediate reactions, mass transport, and reaction kinetics.
  • Development of a quantitative mathematical model integrating these steps.
  • Experimental verification using a planar three-electrode electro-optical cytochrome c biosensor and methanethiol gas.

Main Results:

  • A comprehensive mathematical model was established by combining quantitative descriptions of sensing steps.
  • The model successfully predicted the optical readout response of the cytochrome c biosensor for methanethiol.
  • Experimental validation confirmed the model's predictive accuracy in a spectroelectrochemical cell.

Conclusions:

  • The developed mathematical model provides reliable predictions for gas biosensor responses.
  • This predictive capability is valuable for accelerating biosensor development and optimization.
  • The study demonstrates a robust approach for analyzing and modeling complex gas biosensing mechanisms.