Enhancement of Enzymatic IDE Biosensor Response Using Gold Nanoparticles. Example of the Detection of Urea
Bibliographic record
Abstract
Abstract A new conductometric biosensor based on interdigitated electrodes (IDEs) has been developed for the detection of enzymatic substrates using gold nanoparticles (GNPs), synthesized bellowing the citrate process, with an average diameter of 23 nm and functionalized with urease using layer‐by‐layer technique. A detection limit of 100 µM of urea is obtained when cross‐linked urease is directly immobilized on top of the IDEs (interdigitated distance: 20 µm) whereas a detection limit of 2 µM is obtained when urease functionalized gold nanoparticles are deposited on the top of the IDEs. The use of gold nanoparticles allows the increase of the sensitivity of detection (from 10 µS/mM to 107 µS/mM) due to the decrease of the thickness of probed zone.
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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) | 0.000 | 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 | 0.001 | 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 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".