Molecularly Imprinted Polymer Biosensor Based on Nitrogen-Doped Electrochemically Exfoliated Graphene/Ti<sub>3</sub> CNT<i><sub>X</sub></i> MXene Nanocomposite for Metabolites Detection
Bibliographic record
Abstract
Rapid and accurate quantification of metabolites in different bodily fluids is crucial for a precise health evaluation. However, conventional metabolite sensing methods, confined to centralized laboratory settings, suffer from time-consuming processes, complex procedures, and costly instrumentation. Introducing the MXene/nitrogen-doped electrochemically exfoliated graphene (MXene@N-EEG) nanocomposite as a novel biosensing platform in this work addresses the challenges associated with conventional methods, leveraging the concept of molecularly imprinted polymers (MIP) enables the highly sensitive, specific, and reliable detection of metabolites. To validate our biosensing technology, we utilize agmatine as a significant biologically active metabolite. The MIP biosensor incorporates electrodeposited Prussian blue nanoparticles as a redox probe, facilitating the direct electrical signaling of agmatine binding in the polymeric matrix. The MXene@N-EEG nanocomposite, with excellent metal conductivity and a large electroactive specific surface area, effectively stabilizes the electrodeposited Prussian blue nanoparticles. Furthermore, increasing the content of agmatine-imprinted cavities on the electrode enhances the sensitivity of the MIP biosensor. Evaluation of the designed MIP biosensor in buffer solution and plasma samples reveals a wide linear concentration range of 1.0 nM–100.0 μM ( R 2 = 0.9934) and a detection limit of 0.1 nM. Notably, the developed microfluidic biosensor offers low cost, rapid response time to the target molecule (10 min of sample incubation), good recovery results for detecting agmatine in plasma samples, and acceptable autonomous performance for on-chip detection. Moreover, its high reliability and sensitivity position this MIP-based biosensor as a promising candidate for miniaturized microfluidic devices with the potential for scalable production for point-of-care applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".