Manipulation of Fe/Au Peroxidase-Like Activity for Development of a Nanocatalytic-Based Assay
Bibliographic record
Abstract
Nanoparticles have been discovered to have intrinsic peroxidase-like catalytic activity that shows beneficial applications in a biosensor.The aim of this study is to investigate the synthesised Fe/Au nanoparticles' peroxidase-like activity and further evaluate them for development of a nanocatalytic-based assay specifically designed to detect 17β-estradiol in water.The peroxidase-like activity of the synthesised Fe/Au nanoparticles was optimised using the H 2 O 2 -ABTS system and was characterised using Michaelis-Menten kinetics.Then, the nanoparticles surface was functionalised with aptamers for specific conjugation with the target analyte, 17β-estradiol.The feasibility of this assay was tested at different concentration of aptamer-tagged Fe/Au nanoparticles and 17β-estradiol.Also, assessment of this assay was conducted with potentially interfering materials and spiked real tap water samples.Results obtained from absorbance data reveal that the Fe/Au-17β-estradiol complex significantly hampered the peroxidase-like catalytic activity of the nanoparticles.The absorbance intensity declined drastically after aptamer-tagged nanoparticles (Fe/Au-fl-apt) "captured" the targets and formed nanoparticles-analytes complexes.This assay showed good accuracy and reproducibility for detection of 17β-estradiol concentration ranging from 3 to 272 ng/L.Furthermore, the aptamers used in this study were very selective towards the target analyte and related compounds showed little to no interference.Thus, a simple, rapid and sensitive detection assay, specific for 17β-estradiol was developed using a new detection strategy by manipulation of nanoparticles' peroxidase-like activity.
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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 | 0.000 |
| 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 | 0.001 |
| 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".