Examining nanoparticle characteristics and removal through direct filtration treatment
Bibliographic record
Abstract
Water utilities in Nova Scotia face numerous challenges treating low turbidity water and complying with stringent guidelines and treatment standards. Problems associated with the treatment of low-turbidity water are not confined to Nova Scotia; several other provinces, British Columbia, Manitoba and Ontario share similar water characteristics of drinking water sources. The treatment of low turbidity water is a challenge for these utilities as it requires maintaining the appropriate coagulant dosage that will ensure adequate particle and natural organic matter removal, while at the same time not enhancing the formation of disinfection by-products. Another concern associated with the treatment of such water is that when the particle content of the water is very low, charge neutralization will not be effective due to the weak contact between destabilized particles. Currently, nanoparticles are not regulated as water contaminants, and thus it is unclear whether the existing filtration treatment practices are capable of removing them from drinking water. Obtaining in-depth information on nanoparticle characteristics in drinking water sources will provide a valuable resource that can assist in the development of future treatment strategies.\nIn this research, characteristics of four synthetic nanoparticles cerium dioxide (CeO2), ferric oxide (Fe2O3), silicon dioxide (SiO2) and titanium dioxide (TiO2) were investigated in Milli-Q water for particle size, surface area, and surface potential using different characterization techniques. Water samples from Pockwock Lake were also characterized for naturally occurring nanoparticles. After initial testing, titanium dioxide (TiO2) nanoparticles were selected to examine particle removal at bench-scale filtration experiments, under operating conditions similar to those practiced at the J.D. Kline Water Supply Plant, Halifax, NS, Canada. Filter performance for the deposition of TiO2 nanoparticles was evaluated through the calculation of its attachment efficiency and coefficient under various water chemistry conditions. The calculated filter efficiency was then applied to simulate natural nanoparticles removal from water. \nThe results of the research indicate that the investigated nanoparticles behaved similar to natural particles and formed aggregates with larger particle sizes in Milli-Q water. Among the tested nanoparticles, only titanium dioxide could be coagulated with alum, as its negative surface charge and zero point of charge were closer to that of alum. Filtration experiments revealed that TiO2 nanoparticles, when present in water, could successfully be removed by an alum dose of 8 mg/L. Indeed, removal in excess of 99.5% was achieved under the study conditions. Under the investigated water chemistry conditions, very low attachment efficiencies (?) of 0.001, 0.002 and 0.01, and filter coefficients (?) of -0.003, -0.001 and -0.02 were determined for the filters. Based on the calculated attachment efficiencies, and under the studied conditions, natural nanoparticles remain dispersed in the water and would not likely to be removed by direct filtration. The overall research findings represent a major step forward in nanoparticle removal by direct filtration.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".