Carbon Nanomaterials Based Electrochemical Sensors/Biosensors for the Sensitive Detection of Pharmaceutical and Biological Compounds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it