Bibliographic record
Abstract
As a result of expanding use, nanomaterials such as nanosilver (AgNP), are being continuously released into the environment through production, usage, and disposal. Research is needed to develop cost‐effective and sensitive methods to quantify AgNP in various environmental compartments in order to fully evaluate the exposure and risks associated with the presence of these nanomaterials in the environment. This work is hampered by the lack of sensitive methods to detect AgNPs and other nanomaterials in environmental matrices. The present study focused on the development, calibration and application of a passive sampling technique for detecting AgNP and estimating low concentrations of these nanomaterials in aquatic matrices. This thesis work included the design, calibration and application of the designed sampler to the detection of silver in nano-form in surface waters and wastewater. The Carbon Nanotube Integrative Sampler (CNIS) developed in this study was deployed in an experimental lake dosed with AgNP in order to demonstrate the applicability of these samplers for monitoring in natural surface waters. The time weighted average (TWA) concentrations of "CNIS-labile" Ag estimated from deployments of the sampler in the dosed lake varied between 1-10 ppb, which were similar to the concentrations of total Ag determined from grab samples collected in the lake. The samplers were then applied to monitoring AgNP in a wastewater treatment plant (WWTP), as well as the quantification of AgNPs in surface waters. Results of monitoring in the WWTP for the city of Peterborough, ON showed an estimated TWA concentration of CNIS-labile Ag of 0.2 ppb in the treated effluent, but there was no evidence of an increase in CNIS-labile Ag in receiving waters of the Otonabee River, ON, downstream of the wastewater discharge. Deployments of CNIS in two nearshore areas of Lake Ontario situated near discharges of municipal wastewater also showed estimated TWA concentrations of CNIS-labile Ag that were lower than 1 ppb. These monitoring results are consistent with recent literature showing that much of the Ag in municipal wastewater will settle out of the suspension as Ag2S before final discharge into receiving waters. This thesis provided an assessment of the presence of AgNP in environmental waters and demonstrated the potential of the CNIS samplers as a tool to monitor concentrations of nanomaterials in surface waters and wastewater.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".