Carbon Nanomaterials Based Electrochemical Sensors/Biosensors for the Sensitive Detection of Pharmaceutical and Biological Compounds
Bibliographic record
Abstract
Electrochemical sensors and biosensors have attracted considerable attention for the sensitive detection of a variety of biological and pharmaceutical compounds. Since the discovery of carbon-based nanomaterials, including carbon nanotubes, C60 and graphene, they have garnered tremendous interest for their potential in the design of high-performance electrochemical sensor platforms due to their exceptional thermal, mechanical, electronic, and catalytic properties. Carbon nanomaterial-based electrochemical sensors have been employed for the detection of various analytes with rapid electron transfer kinetics. This feature article focuses on the recent design and use of carbon nanomaterials, primarily single-walled carbon nanotubes (SWCNTs), reduced graphene oxide (rGO), SWCNTs-rGO, Au nanoparticle-rGO nanocomposites, and buckypaper as sensing materials for the electrochemical detection of some representative biological and pharmaceutical compounds such as methylglyoxal, acetaminophen, valacyclovir, β-nicotinamide adenine dinucleotide hydrate (NADH), and glucose. Furthermore, the electrochemical performance of SWCNTs, rGO, and SWCNT-rGO for the detection of acetaminophen and valacyclovir was comparatively studied, revealing that SWCNT-rGO nanocomposites possess excellent electrocatalytic activity in comparison to individual SWCNT and rGO platforms. The sensitive, reliable and rapid analysis of critical disease biomarkers and globally emerging pharmaceutical compounds at carbon nanomaterials based electrochemical sensor platforms may enable an extensive range of applications in preemptive medical diagnostics.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".