Nano-techniques: a tool to prevent disinfection by-product formation in drinking water
Bibliographic record
Abstract
Water, a vital component required by all living things, becomes more and more polluted as a result of globalisation and industrialisation. Currently, pathogenic microbes, emerging pollutants, industrial chemicals and waste are polluting all-natural water resources and transportation systems. Therefore, before usage, drinking water has to be treated. When water disinfectants react with the organic natural components found in source waters, they may unintentionally produce disinfection by-products (DBPs), which have a negative impact on the biological system. There is a need for the development of innovative water-treatment solutions since traditional drinking water treatment (DWT) does not reduce emerging contaminants. The main objective of this study is to give an overview of disinfection techniques, DBP generation, health consequences and environmental repercussions. Additionally, there are advancements in nanotechnology that have been made in drinking water system treatment for the prevention of DBP and contaminant removal. Further review describes the analysis of advanced DWT processes that can be employed for the removal and control of precursors and DBPs.
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 | 0.001 | 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 | 0.001 |
| 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".