Nanomaterials-based electrochemical sensors for health and environmental monitoring
Bibliographic record
Abstract
Bisphenol A (BPA), an endocrine disruptor, requires monitoring in water for health safety. Glutamate, H2O2, and glucose are vital biomarkers for various diseases. However, lab-based methods are expensive, time-consuming, and require skilled personnel, making them unsuitable for point-of-care (POC) devices. The electrochemical sensor enables POC device development. However, it suffers from low sensitivity and selectivity. This thesis focuses on the use of nanomaterials to enhance the sensitivity and selectivity of electrochemical sensors to monitor BPA in water, along with glutamate, H2O2, and glucose in bio-fluids. A BPA sensor was developed using chemically modified MWCNTs with βCD on a screen-printed carbon electrode (SPCE). The MWCNTs-βCD/SPCE exhibited high sensitivity, attributed to the catalytic activity of MWCNTs and the host-guest interaction ability of βCD. It provided a linear range (LR) of 125 nM −30 µM, with a limit of detection (LOD) of 13.76 nM (SNR = 3). We improved the performance by curing the MWCNTs-βCD/SPCE with CTAB. The sensor demonstrated a dynamic range of 500 fM to 10 μM, with a LOD of 96.5 fM, surpassing the Canada-assigned PNEC of BPA in water (0.77 nM). We fabricated a nonenzymatic glutamate sensor using CuO nanostructures and MWCNTs on SPCE. The sensor showed irreversible oxidation of glutamate involving one electron and one proton, and an LR of 20 μM−200 μM with LOD of 17.5 μM and sensitivity of 8500 μAmM−1cm−2. The sensor is promising to detect glutamate in blood. We developed a nonenzymatic glucose sensor using green synthesized gold nanoparticles and CuO-modified SPCE. The LR offered by the sensor (2 µM to 397 µM) is suitable for quantifying saliva glucose. We also created nonenzymatic H2O2 sensor by green synthesized silver nanoparticles modified SPCE which offers LR of 0.5- 161.8 µM with LOD 0.3 µM which is capable of H2O2 monitoring in urine.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".