aceMachine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments
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…
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+ .
Evidence
- Peer-reviewedAnalytical Methods2026-09-01
How should this claim be treated?
Truvace Impact Record TRV-2026-0960, v1: “Machine learning-assisted nitrogen-doped carbon dots for Fe<sup>3+</sup> detection in aqueous environments.” Truvace, 2026-09-02. /record/TRV-2026-0960 (accessed at citation time). sha256 352049b215fbd55d…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0960 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.