β-Galactosidase-Based Colorimetric Paper Sensor for Determination of Heavy Metals
Bibliographic record
Abstract
We demonstrate a novel approach for rapid, selective, and sensitive detection of heavy metals using a solid-phase bioactive lab-on-paper sensor that is inkjet printed with sol-gel entrapped reagents to allow colorimetric visualization of the enzymatic activity of β-galactosidase (B-GAL). The bioactive paper assay is able to detect a range of heavy metals, either alone or as mixtures, in as little as 10 min, with detection limits as follows: Hg(II) = 0.001 ppm; Ag(I) = 0.002 ppm, Cu(II) = 0.020 ppm; Cd(II) = 0.020 ppm; Pb(II) = 0.140 ppm; Cr(VI) = 0.150 ppm; Ni(II) = 0.230 ppm. The paper-based assay was immune to interferences from nontoxic metal ions such as Na(+) or K(+), could be used to detect heavy metals that were spiked into tap water or lake water, and provided quantitative data that was in agreement with values obtained by atomic absorption. With the incorporation of standard chromogenic metal sensing reagents into a multiplexed bioactive paper sensor, it was possible to identify specific metals in mixtures, albeit with much lower detection limits than were obtained with the enzymatic assay. The paper-based sensor should be valuable for rapid, on-site screening of trace levels of heavy metals in resource limited areas and developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".