Nanomaterials-based Electrochemical Sensors for the Detection of Emerging Contaminants
Bibliographic record
Abstract
Recently, acetaminophen, estrogen and bisphenol-A have become emerging contaminants in water systems and environment due to their increased presence in water that causes adverse effects in health and aquatic ecosystems The first two contaminants are the result of the increased human excretion and improper disposal Although the concentrations of these contaminants are very low, typically in the nanomolar range, acetaminophen's long-term exposure to individuals can cause increased mortality, as well as heart, gastrointestinal, and kidney diseases, and estrogen is toxic and can cause breast and prostate cancers. Estrogen is a plasticizers' derivative chemical, resulting from leaching from packaging materials such as feeding bottles, water bottles, and beverage cans into food and water. Exposure to bisphenol-A can also adversely affect on brain, thyroid, and reproductive organs, leading to neurodegenerative, cardiovascular, and carcinogenic diseases due to its toxic behaviour Therefore, frequent monitoring of these contaminants is critical to predict their exposure and adverse effect to humans. Conventional analytical techniques such as liquid chromatography and enzymelinked immunosorbent assay are commonly used for detecting these contaminants [3]. However, recently, electrochemical sensing techniques have shown much promise for simple, rapid, and precise detection of these contaminants. These electrochemical sensors are simple to fabricate and have small footprint, high sensitivity, and require minimal sample preparation. In electrochemical sensing, the sensing electrode transduces by binding or reacting with analytes (contaminants) into a measurable signal Nanomaterials are now widely used to design the sensing electrodes due to their high surface to volume ratio, excellent catalytic activity and tunable electronic properties, providing more binding sites and stronger signals that is very important for the fabrication of high-performance sensors to detect trace level detection of these contaminants In this presentation, we will report on our recent advances of nanomaterials-based electrochemical sensors for the detection of acetaminophen, estrogen and bisphenol-A. We will explain our results using graphene oxides, multiwall carbon nanotubes, and beta-cyclodextrins and focus on the research challenges, and future perspectives of the detection of the emerging contaminants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".