Polyaniline-based bovine serum albumin imprinted electrochemical sensor for ultra-trace-level detection in clinical and food safety applications
Bibliographic record
Abstract
Monitoring bovine serum albumin (BSA) at ultra-low levels is crucial for clinical and food safety applications, as it plays a significant role in identifying various health conditions and potential risks, necessitating fast, trace-level detection of BSA. This study proposes an approach to address these challenges by employing molecularly imprinted polymer (MIP) to develop an ultra-trace-level and cost-effective BSA sensing platform. The MIP electrochemical sensor was developed using polyaniline (PANI) combined with the protein crosslinker glutaraldehyde (GA) to optimize BSA surface imprinting in the MIP. As a result, the sensor achieves a sensitivity of 1.24 μA/log(pg/mL), with a picomolar detectable limit of 2.3 pg/mL (0.035 pM) and a wide detection range from 20 pg/mL to 200,000 pg/mL (0.303 pM to 3030 pM), making it suitable for clinical and food safety applications. Additionally, the study explores the interaction between an acidic surfactant protein eluent (acetic acid with sodium dodecyl sulfate, AcOH-SDS) and BSA vacant sites, enhancing recognition and re-binding. The PANI-based MIP sensor demonstrates initial feasibility and practicality in commercial milk and real human serum, opening avenues for early disease detection and ensuring food safety in BSA-related immune responses.
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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".