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Climate·G Space·Evidence-backed gain·Published 2026-09-02

Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

Abstract: The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ . During Fe 3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe 3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100…

TRV-2026-0960Peer-reviewedPermanent record — cite & verify
Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments

In situ Raman Spectroscopy Study of the Formation of Graphene from Urea and Graphite Oxide by Mowry, Michael N.. Public domain

The quick read

The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ .

To enhance prediction accuracy across a broad concentration range, a machine learning model was introduced to develop a high-precision quantitative analysis method for Fe 3+ . The spiked recovery rates in actual water samples ranged from 99.26% to 101.14%, with relative standard deviations below 3%.

Main points
  • The concentration of iron ions is a crucial indicator for assessing water quality.
  • In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe 3+ .
  • During Fe 3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe 3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100 µM, with a detection limit of 0.55 µM.
Gain

To enhance prediction accuracy across a broad concentration range, a machine learning model was introduced to develop a high-precision quantitative analysis method for Fe 3+ .

The rundown

During Fe 3+ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe 3+ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100 µM, with a detection limit of 0.55 µM. By integrating smartphone-based image analysis, visual semi-quantitative detection of alkaline pH and Fe 3+ was achieved.

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