The role of nanodimensions in enhancing electrochemical sensing: A comprehensive review
Bibliographic record
Abstract
Catalytic sensing of molecular biomarkers has become increasingly important in health monitoring and point-of-care diagnostics due to their promising properties. To develop technology for catalytic sensing of molecular biomarkers, financial and environmental sustainability must be considered. Small-molecule biomarkers play a crucial role in numerous physiological processes and are commonly utilized for disease detection by monitoring cell signaling, bioprocesses, cell viability, metabolomics, and pharmacokinetics. Nanostructured materials with varied dimensions have been extensively utilized in molecular sensing, capitalizing on their enhanced electrocatalytic activities. As a result of significant advancements in materials and fabrication technologies, novel nanostructured platforms have been developed in various shapes to accommodate various analytes of interest. The nanostructured platforms can be divided into four main categories based on their dimensionality, including nanoparticles or zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) nanomaterials. Each category of nanomaterials has demonstrated numerous advantages for electrocatalytic sensing due to their extraordinary surface-to-volume ratio and fast electron-hole transfer routes. Still, the existing disadvantages, such as their volatility to chemicals and wetness, hinder their widespread applications in sensing. Despite their advantages, challenges such as susceptibility to chemicals and wetness hinder their widespread use in sensing applications. Recently, the integration of multiscale nanocompositions, particularly 2D functional materials, has drawn attention for enhancing electrocatalytic activity. Our innovative outlook in this review involves integrating multiscale nano compositions, particularly 2D functional materials and fractal nanostructures, to enhance catalytic and electrocatalytic activities. These advancements will drive significant progress in the field of biosensing, offering new solutions for complex diagnostic challenges.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".