A Miniature Purge-and-Trap Using a Gold-plated Wire for Field Detection of Ultra-Trace Mercury in Water by Microplasma Optical Emission Spectrometry
Bibliographic record
Abstract
Herein, a miniature purge-and-trap (μP&T) device using a single electrochemically gold-plated nichrome (Au@Ni-Cr) wire was developed for the ultra-sensitive field detection of mercury in environmental water samples by coupling with miniature point discharge optical emission spectrometry (μPD-OES).The cold mercury vapor (Hg 0 ) reduced from inorganic mercury (Hg 2+ ) by BH4 -reduction, was separated from liquid phase and further trapped on the Au@Ni-Cr wire.The trapped Hg 0 could be efficiently and rapidly released (15 s) via direct heating wire with low power consumption (28 W) and further carried into the point discharge optical emission spectrometer for detection.Under optimal conditions, a limit of detection (LOD) of 0.24 ng L -1 with a relative standard deviation (RSD) of 3.6% was obtained for mercury when a 100 mL of sample was analyzed.Compared to the conventional P&T device, the developed μP&T device significantly reduces power and gas consumption.Moreover, the high temperature desorption chamber used in the conventional device was eliminated since the Au@Ni-Cr wire was directly heated with a battery, thus substantially minimizing its size and making it more suitable for field analytical chemistry by coupling with μPD-OES.The accuracy and practicality of the portable P&T-μPD-OES were validated through the successful analyses of several environmental water samples with satisfactory recoveries (92-112%) and surface water samples confirming that the system retains great potential for the field detection of ultra-trace mercury in water with advantages of higher sensitivity, lower power consumption and analysis time.Spectrosc. 2025, 46(3), 241-250.assessmentframeworks for sudden environmental disasters by integrating geochemical monitoring, ecological modeling, and disaster response strategies. Atom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".