Manipulation of Fe/Au Peroxidase-Like Activity for Development of a Nanocatalytic-Based Assay
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 17-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, 17-estradiol. The feasibility of this assay was tested at different concentration of aptamer-tagged Fe/Au nanoparticles and 17-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-17-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 17-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 17-estradiol was developed using a new detection strategy by manipulation of nanoparticles' peroxidase-like activity.
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.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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 it