Efficient Heavy Metals Detection in Plants: A Microfluidic Approach Combined with Laser-Induced Breakdown Spectroscopy
Bibliographic record
Abstract
The detection of heavy metals (HMs) in plants is essential for ensuring the health of both plants and humans. Nevertheless, existing detection techniques are limited by high costs and their potential to damage plant samples. In this paper, a wearable paper-based microfluidic sap enrichment (PMSE) device combined with laser-induced breakdown spectroscopy (LIBS) is presented. Colorimetric analysis was employed to optimize critical parameters of the HM-enrichment filter paper, including its diameter and pore size, ensuring efficient and uniform HM accumulation. The CuNPs-modified filter paper effectively enriched HMs and enhanced LIBS signals, enabling the detection of Cd(II) and Pb(II) in cucumber plants across a concentration range of 0-150 ppb. The method achieved detection limits of 3 ppb for Cd(II) and 5 ppb for Pb(II). This study provides a theoretical basis and technical support for the rapid detection of heavy metals in plants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".