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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".