Aqueous lead speciation determined using DNAzyme GR5
Bibliographic record
Abstract
Environmental context The on-site and real-time detection of metal ions is important for environmental monitoring and risk assessment. For appropriate management decisions, it is necessary to specifically sense the labile fraction of metal rather than total metal. This study provides a proof-of-principle that the DNAzyme GR5 can be used to sense labile lead in natural waters containing dissolved organic matter. Rationale DNAzyme-based sensors are a promising technology for possible labile metal monitoring that have not yet been fully tested in real waters. In clean, buffered, laboratory waters specific DNAzymes interact with specific metal ions and produce a signal (e.g. fluorescence). In more complex natural solutions the free ion concentration is reduced by complexation (e.g. to dissolved organic matter, DOM) and the signal would not be proportional to total metal, but hypothetically proportional to the labile fraction of total metal; i.e. the fraction of metal available to interact with the DNA. Methodology Here, an existing metal specific RNA-cleaving DNAzyme for Pb2+ (GR5) is used to test waters representative of natural solutions. Samples were prepared with ionic strengths from 25 to 100 mM using sodium acetate, sodium chloride and sodium bicarbonate. In addition, pH values of 6.5, 7.5 and 8.5 were tested for the different electrolytes, with and without added dissolved organic carbon, at 2, 5 and 8 mg C L–1. Lead additions were performed at toxicologically relevant levels (less than or equal to 1 µM of added lead). Results and discussion The GR5 response was found to be dependent on ionic strength, including identity of the background electrolyte, where high ionic strength slowed the reaction and chloride media increased reaction speed. Reproducible responses of GR5 are possible at conditions similar to natural waters, except responses were too fast for high pH (8.5 or higher), low DOC (less than 2 mg C L–1) and low ionic strength (25 mM). It is found that GR5 responds to three lead species, PbOH+, PbCl+ and Pb2+, with relative sensitivities in the same order. GR5 does not respond to lead complexed with acetate, carbonate or DOM. It is possible to use the measured first-order rate constant for lead induced fluorescence of GR5 to calculate ionic lead that agrees within a factor of two with respect to Windermere Humic Aqueous Model predictions. Thus, GR5 may represent a labile lead probe, although further work is necessary to test this.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".