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Interference leads to systematic error in atomic absorption (AA) measurements by enhancing or diminishing the analytical signal or the background. These interferences can be grouped into three main categories: spectral interference, chemical interference, and physical interference.
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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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A New Ammonium Smart Sensor with Interference Rejection.

Juan V Capella1, Alberto Bonastre1, José C Campelo1

  • 1Instituto de las Tecnologías de la Información y Comunicaciones ITACA, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

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Summary

A new smart ammonium sensor overcomes limitations of traditional ion-selective electrodes (ISEs). It uses an expert system to prevent failures and reject interfering ions, improving water quality monitoring.

Keywords:
expert systemin-line water monitoringinterference tolerancesmart ammonium sensortriple modular redundancywireless sensor networks

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Sensor Technology

Background:

  • Ammonium concentration is crucial for assessing water pollution and eutrophication.
  • Traditional ion-selective electrodes (ISEs) for ammonium (NH4+) measurement suffer from interference by potassium and sodium ions.
  • Random sensor malfunctions can compromise measurement accuracy.

Purpose of the Study:

  • To develop and test a smart ammonium sensor with improved reliability and accuracy.
  • To address the limitations of conventional ISEs in water quality analysis.
  • To enhance the detection of ammonium in environmental water samples.

Main Methods:

  • Implementation of an expert system to supervise a set of ISEs.
  • Utilizing the expert system to detect and mitigate random sensor failures.
  • Employing the expert system to reject interfering ions like potassium and sodium.
  • Experimental validation of the smart sensor in real water samples.

Main Results:

  • The developed smart ammonium sensor successfully avoided random failures.
  • The sensor effectively rejected interfering ions, leading to higher measurement quality.
  • Experimental measurements in water samples demonstrated the sensor's enhanced features.
  • The system proved capable of accurate ammonium determination despite potential interferences.

Conclusions:

  • The smart ammonium sensor offers a robust solution for accurate ammonium determination in water.
  • The expert system approach enhances the reliability and reduces interferences in ISE measurements.
  • This technology holds potential for in-line water quality monitoring using wireless sensor networks.