Review on Gold Nanoparticles-Based Biosensors in Clinical and Non-Clinical Applications
Bibliographic record
Abstract
Gold nanoparticles (GNP) acquire unique properties that have made significant contributions to clinical and non-clinical fields, specifically in the application of GNP’s for designing biosensor devices in which exhibit novel functional properties. Many properties of GNP’s are reviewed in this literature including optical properties, biocompatibility, conductivity, catalytic properties, high surface-to-volume ratio, and high density of the GNPs, that make them excellent in the application of constructing GNP-based biosensors. This literature review covers a specific comparison between the optical, electrochemical, and piezoelectric biosensors, as these are the three most common GNP-based biosensors. Optical biosensors are optimal due to their ability to cater to surface modification, which then leads to the ability for selective bonding. Furthermore, with the use of GNP and the sensor's non-invasive and non-toxic method of use, high-resolution images and signals can be formed. The sensitivity and specificity of electrochemical biosensors with the conductivity of GNPs, the electrodes of this stable biosensor can detect tumour markers in the human body. Piezoelectric biosensors are mass sensitive sensors and with the use of GNP, it amplifies the changes in mass. Through this, these sensors progress to be immunosensors which determine microorganisms and macromolecular compounds. As well, this review will conclude with an outline of present and future research recommendations for real-world application of the three GNP-based biosensors discussed.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".