Gold Nano/Micro-Islands Overcome the Molecularly Imprinted Polymer Limitations to Achieve Ultrasensitive Protein Detection
Bibliographic record
Abstract
Here, we report on an electrochemical biosensor based on core–shell structure of gold nano/micro-islands (NMIs) and electropolymerized imprinted ortho -phenylenediamine (o-PD) for detection of heart-fatty acid binding protein (H-FABP). The shape and distribution of NMIs (the core) were tuned by controlled electrodeposition of gold on a thin layer of electrochemically reduced graphene oxide (ERGO). NMIs feature a large active surface area to achieve a low detection limit (2.29 fg mL –1, a sensitivity of 1.34 × 10 13 μA mM –1 ) and a wide linear range of detection (1 fg mL –1 to 100 ng mL –1 ) in PBS. Facile template H-FABP removal from the layer (the shell) in less than 1 min, high specificity against interference from myoglobin and troponin T, great stability at ambient temperature, and rapidity in detection of H-FABP (approximately 30 s) are other advantages of this biomimetic biosensor. The electrochemical measurements in human serum, human plasma, and bovine serum showed acceptable recovery (between 91.1 ± 1.7 and 112.9 ± 2.1%) in comparison with the ELISA method. Moreover, the performance of the biosensor in clinical serum showed lower detection time and limit of detection against lateral flow assay (LFA) rapid test kits, as a reference method. Ultimately, the proposed biosensor based on the core–shell structure of gold NMIs and MIP opens interesting avenues in the detection of proteins with low cost, high sensitivity and significantstability for clinical applications.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".