Microscale quantitation of heavy metals by back-scattering interferometry in conjunction with photothermal effect using a single laser beam
Bibliographic record
Abstract
A microanalytical technique based on the photothermal effect in conjunction with back-scattering interferometry (BSI) using a single laser beam was developed for quantitative detection of heavy metals. After the chromogenic reaction of an analyte in a capillary tube, the photothermal effect induced by irradiation with the same laser beam leads to a change of the refractive index of the solution, which can be "quantified" using the BSI technique. For prove-of-concept, Cu(II) was chosen as the trial analyte, for which the solution changes to purplish through reacting with the chromogenic reagent; a single laser beam of 532 nm was adapted for both inducing the photothermal effect and realizing BSI detection. With as little as 1.0 μL solution, a limit of detection (LOD) of 0.10 mg/L for Cu(II) was achieved. In addition, the versatility of the technique was demonstrated by detecting other two heavy metal ions, Fe(II) and Cr(VI), with limits of detection of 0.06 mg/L and 0.04 mg/L, respectively. The demonstrated detection sensitivity, application versatility, and instrumentation simplicity of this new technique promises it as a practical tool for environmental monitoring and beyond.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".