Review—Recent Advances in the Development of Nanoporous Au for Sensing Applications
Bibliographic record
Abstract
In the fields of medicine, environmental protection, and food safety, sensors are imperative for the detection of biomarkers, contaminants, and preservatives. The use of nanoporous gold (NPG) as a sensing platform may greatly enhance performance due to its stability, high surface area, and catalytic abilities. There are many methods reported in the literature for fabricating NPG, including chemical strategies and various electrochemical techniques. The primarily use of NPG in sensing applications may be classified into three categories: electrochemical, bioelectrochemical, and optical. Although both electrochemical and bioelectrochemical sensors are based on the electrical signal produced by a specific analyte, a biological recognition element is involved in the bioelectrochemical sensing process. On the other hand, optical sensors exploit NPG through unique surface plasmon resonance properties that can be monitored by UV-Vis, Raman, or fluorescence spectroscopy. For this review, the primary strategies for fabricating NPG, including dealloying, electrochemical, and dynamic hydrogen bubble template (DHBT), are discussed. In addition, advances made over the last decade towards the detection of biomarkers, pollutants, contaminants, and food additives are highlighted. The future development of NPG based sensors for medical, environmental, and food safety applications is discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.002 |
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".