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Related Concept Videos

Photoluminescence: Applications01:14

Photoluminescence: Applications

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Photoluminescence offers a wide range of applications due to its inherent sensitivity and selectivity. This technique allows for both direct and indirect analyses of the analyte. Direct quantitative analysis is possible when the analyte exhibits a favorable quantum yield for fluorescence or phosphorescence. However, an indirect analysis may be feasible if the analyte is not fluorescent or phosphorescent, or if the quantum yield is unfavorable. Indirect methods include reacting the analyte with...
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Fluorometers and spectrofluorometers are two types of instruments used for measuring molecular fluorescence. These instruments differ in how they select excitation and emission wavelengths and the type of light sources they utilize. Fluorometers use absorption interference filters to choose excitation and emission wavelengths. The excitation source in a fluorometer is typically a low-pressure mercury vapor lamp that emits intense lines distributed throughout the ultraviolet and visible regions.
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In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
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Related Experiment Video

Updated: Dec 10, 2025

Luminescence Lifetime Imaging of O2 with a Frequency-Domain-Based Camera System
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Dual Oxygen and Temperature Luminescence Learning Sensor with Parallel Inference.

Francesca Venturini1,2, Umberto Michelucci2,3, Michael Baumgartner1

  • 1Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland.

Sensors (Basel, Switzerland)
|September 3, 2020
PubMed
Summary

This study introduces a novel learning sensor for accurate oxygen and temperature measurement. It uses a neural network to autonomously generate data and process optical signals, overcoming traditional modeling challenges.

Keywords:
artificial intelligenceluminescenceluminescence quenchingmachine learningneural networkoptical sensoroxygen sensorphase fluorimetry

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Area of Science:

  • * Optical Sensing
  • * Luminescence Quenching
  • * Artificial Intelligence in Sensor Technology

Background:

  • * Optical oxygen sensing relies on luminescence quenching, but accurate mathematical modeling is challenging due to cross-interfering parameters like oxygen concentration and temperature.
  • * Current methods often require separate sensors for each parameter and rely on approximate, ad hoc models.
  • * Developing sensors with unknown or complex underlying mathematical models presents a significant hurdle.

Purpose of the Study:

  • * To develop a novel learning sensor with parallel inference capabilities for accurate optical measurements.
  • * To overcome the limitations of traditional mathematical modeling in sensor response.
  • * To introduce a new metric, Error Limited Accuracy, for evaluating neural network-based sensors.

Main Methods:

  • * Development of a learning sensor employing parallel inference.
  • * Autonomous and automatic generation of a large dataset for training.
  • * Utilization of neural network-based signal processing for sensor data analysis.
  • * Exploitation of cross-sensitivity between parameters for simultaneous extraction from optical measurements.

Main Results:

  • * Demonstrated a new approach for developing learning sensors that autonomously generate and utilize large datasets.
  • * Achieved unprecedented accuracy in extracting multiple parameters (e.g., oxygen, temperature) from single optical measurements without a priori models.
  • * Proposed a novel performance metric, Error Limited Accuracy, for neural network-based sensors.

Conclusions:

  • * The proposed learning sensor with parallel inference effectively overcomes challenges in optical sensing, particularly for parameters with cross-interfering effects.
  • * The method enables accurate sensing even when the underlying mathematical model is unknown or complex, applicable beyond oxygen and temperature.
  • * The autonomous data generation and neural network training approach offers a robust and versatile solution for advanced sensor development.