Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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: 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,...
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...
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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ternary Micronano Electrodes Based on rGO/NiO/ZnO are Used for Efficient Electrolytic Hydrogen Production from Methanol.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

RFID-Based ZnO/TiO<sub>2</sub>/SnS<sub>2</sub> Soil Oxygen Content Sensor Coupled with 1D-CNN-GRU Model: Classification for Predicting Soil Oxygen Content System.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

Nickel Foam-Loaded CoFe<sub>2</sub>O<sub>4</sub>/CuS/RGO Electrode for a Bifunctional Device for Glucose Detection and Hydrogen Release.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

The impact of digitization on environmental sustainability: New insights from G20 nations.

Journal of environmental management·2025
Same author

Retraction notice to "Poverty improvement policies and household income: Evidence from China" [Heliyon 9 (2023) e21442].

Heliyon·2025
Same author

Design of agricultural product traceability system based on blockchain and RFID.

Scientific reports·2024
Same journal

Synergistic Visible-Light-Driven CO<sub>2</sub> Reduction and H<sub>2</sub>O Oxidation over Ti<sub>3</sub>C<sub>2</sub> Quantum Dot-Modified Cu/g-C<sub>3</sub>N<sub>4</sub> Photocatalysts.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Spontaneous Phase Separation Enables Rapid, Polymerization-Free Fabrication of Gels.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Lamellar-Confinement-Induced ZIF-67 Nanosheet Mixed Matrix Membranes for Enhanced CH<sub>4</sub>/N<sub>2</sub> Separation.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Structure Control of Oblate Nanoparticles Self-Assembled by ABC Cyclic Terpolymers under Soft Confinement.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Tuning Brønsted/Lewis Acid Site Ratios via Ammonia Modulation for Selective Conversion of Glycerol to 1,3-Propanediol or Solketal.

Langmuir : the ACS journal of surfaces and colloids·2026
Same journal

Catalytic and Nitriding Competition of Nitrogen Atom on Graphene and Its Finite Rate Surface Chemistry Model.

Langmuir : the ACS journal of surfaces and colloids·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Intelligent Multigas Monitoring: A Reconfigurable RFID Sensor with Machine Learning-Assisted Decoding for O2 and CO2.

Fengjuan Miao1, Jiapeng Dai1, Bairui Tao1

  • 1College of Communications and Electronics Engineering, Qiqihar University, Qiqihar, Heilongjiang 161006, China.

Langmuir : the ACS Journal of Surfaces and Colloids
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel reconfigurable RFID sensor for precise wireless monitoring of oxygen and carbon dioxide in fermentation. The system utilizes a random forest algorithm for high-accuracy gas concentration prediction, enhancing industrial processes.

More Related Videos

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

Related Experiment Videos

Last Updated: Jun 17, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing
10:42

Infrared Degenerate Four-wave Mixing with Upconversion Detection for Quantitative Gas Sensing

Published on: March 22, 2019

Area of Science:

  • Sensor Technology
  • Machine Learning
  • Chemical Engineering

Background:

  • Traditional fermentation monitoring faces limitations in precision and real-time data acquisition.
  • Existing wireless sensors often struggle with dual-parameter detection and complex environmental conditions.
  • Need for advanced sensing solutions to support digital transformation in the brewing industry.

Purpose of the Study:

  • To develop a reconfigurable antenna-based RFID sensor for simultaneous wireless monitoring of O2 and CO2.
  • To integrate a random forest algorithm for accurate, high-precision gas concentration inversion.
  • To demonstrate the application of this technology in fermentation environments, specifically wine production.

Main Methods:

  • Design of a reconfigurable RFID tag antenna using complementary split-ring resonators (CSRR) and photodiodes for enhanced coding capacity.
  • Utilization of SnS2/ZnO/NiO and SnO2/CuO/TiO2 nanocomposites as gas-sensitive layers for improved adsorption and conductivity.
  • Development of a multidimensional RF feature-decoding framework based on random forest regression for nonlinear signal inversion.

Main Results:

  • The sensor achieved high-precision detection of O2 (1000-250,000 ppm) and CO2 (500-50,000 ppm) with rapid response times.
  • Demonstrated excellent repeatability, long-term stability, and significant amplitude changes (14.79 dB for O2, 17.13 dB for CO2).
  • Random forest model exhibited high accuracy and generalization ability, outperforming traditional linear fitting methods in gas concentration prediction.

Conclusions:

  • The developed RFID sensor system enables accurate, wireless, dual-parameter gas monitoring in fermentation.
  • The integration of reconfigurable antennas and random forest algorithms overcomes previous accuracy limitations.
  • This technology supports the digital transformation of the brewing industry, improving product quality and reducing costs.