Green Nanomaterial-based Electrochemical Sensors for Health and Environmental Monitoring
Bibliographic record
Abstract
Health and environmental monitoring are essential for protecting ecosystems, ensuring public health, and promoting sustainable development. Nanomaterial-based electrochemical sensors have emerged as powerful tools for on-site monitoring of a wide range of analytes, including biomarkers, pharmaceuticals, heavy metals, toxic substances, and microplastics. These sensors offer rapid, reliable, and cost-effective measurements by leveraging the unique chemical and physical properties of nanomaterials, such as high effective surface area and catalytic activity, which enhance sensitivity and selectivity—critical features for effective health and environmental protection. However, traditional chemical synthesis methods for nanomaterials often involve high temperatures and hazardous chemicals, which contradict the principles of sustainability. To address these issues, green synthesis techniques have been developed, utilizing eco-friendly substances such as plant extracts, microorganisms, and other biological systems. Green synthesis not only promotes environmental sustainability and cost-efficiency but also produces nanomaterials with unique properties that can further enhance sensing performance. This chapter will review the methodologies employed in green synthesis, highlighting their distinct characteristics and the application of green nanomaterials in electrochemical sensors. It also addresses the challenges in this field and explores potential avenues for future advancements, including the integration of smart sensing technologies for real-time and remote monitoring.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".