Real-time monitoring of nanoscale TiO <sub>2</sub> concentration by spectrophotometry: implications of agglomeration due to natural organic matter and multivalent ions
Bibliographic record
Abstract
The study of the environmental fate of nanoscale TiO2 (n-TiO2) is a major recent research focus which requires a rapid and accurate on-site concentration determination method. Inductively coupled plasma mass spectroscopy (ICP-MS) has been the most widely used method for determining the concentration of n-TiO2 in environmental samples; however, poses many challenges, such as hazardous hydrofluoric acid pre-treatment and clear limitations in mobile on-site measurement and monitoring. This study demonstrates that industrial wastewater containing natural organic matter (NOM) can present a major challenge to the analysis of n-TiO2 by ICP-MS, and introduces a spectrophotometry technique that can be used as an alternative. The results suggest that spectrophotometry methods can be more accurate than slurry nebulization ICP-MS for measuring the concentrations of n-TiO2 in wastewater containing NOM under low salt conditions. Furthermore, this study demonstrates the use of a portable flow-through spectrophotometer for use in applications of wastewater treatment and environmental monitoring with real-time feedback of n-TiO2 concentrations. The ability to detect and monitor n-TiO2 will greatly assist in improving the understanding of hazards and risks that emerging nanomaterials pose to the environment and the public health.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".