Nanomaterial-Based Electrochemical Sensors for Environmental and Biomedical Applications
Bibliographic record
Abstract
The rapid growth of pharmaceutical industries has led to new biomedical and environmental concerns. There is an urgent need for sensitive, portable and cost-effective sensors for the detection of pharmaceuticals to either track patient overdosing or to monitor the pollutants in the environment. Nanomaterial-based electrochemical sensing technologies can readily tackle the aforementioned problems, which spurred significant research interests recently [1-3]. In this presentation, the synthesis of nanoporous gold and graphene oxide-based nanomaterials for the electrochemical sensing of acetaminophen, naproxen and isoniazid is discussed. The design rationale and the performance of the proposed electrochemical sensors are highlighted. Specifically, the hierarchical nanoporous gold exhibited high sensitivity of 58.16 µAµM−1cm−2 and a low detection limit of 1.01 nM. In addition, it was found that the oxygen content of the graphene-based nanomaterials played a critical role in both sensing of naproxen and isoniazid. The proposed electrochemical sensors were further tested using real samples, which showed their promising applicability in biomedical and environmental applications. [1] J. van der Zalm, S. Chen, W. Huang, and A, Chen, J. Electrochem. Soc., 167 ,037532 (2020). [2] L. Qian, S. Durairaj, S. Prins, and A. Chen, Biosens. Bioelectron., 112836 (2020). [3] L. Qian, A.R. Thiruppathi, R. Elmahdy, J. van der Zalm, and A. Chen, Sensors., 20,1252 (2020).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".