Room Temperature Cation Exchange Reaction in Nanocrystals for Ultrasensitive Speciation Analysis of Silver Ions and Silver Nanoparticles
Bibliographic record
Abstract
To evaluate the toxicity of silver nanoparticles (AgNPs) and Ag(+) and gain deep insight into the transformation of AgNPs in the environment or organisms, ultrasensitive analytical methods are needed for their speciation analysis. About 40-fold of Cd(2+) in CdTe ionic nanocrystals can be "bombarded-and-exploded" (exchanged) in less than 1 min simply by mixing the nanocrystals with Ag(+) solution at room temperature, while this cation exchange reaction did not occur when only silver nanoparticles were present. On the basis of this striking difference, an ultrasensitive method was developed for speciation analysis of Ag(+) and AgNPs in complex matrices. The released Cd(2+) was reduced to its volatile species by sodium tetrahydroborate, which was separated and swept to an inductively coupled plasma mass spectrometer (ICPMS) or an atomic fluorescence spectrometer (AFS) for the indirect but ultrasensitive detection of Ag(+). Owing to the remarkable signal amplification via the cation exchange reaction and the advantages of chemical vapor generation for sampling, the limit of detection was 0.0003 μg L(-1) for Ag(+) by ICPMS, which was improved by 100-fold compared to the conventional method. Relative standard deviations are better than 2.5% at a concentration of 0.5 μg L(-1) Ag(+) or AgNPs regardless of the detector. The proposed method retains several unique advantages, including ultrahigh sensitivity, speciation analysis, simplicity and being organic reagent-free, and has been successfully utilized for speciation analysis of Ag(+) and AgNPs in environmental water samples and paramecium cells.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".