Copper Nanoparticle-Dispersed Laser-Induced Graphene Film-Based Electrochemical Sensor for Thrombin in Biomimic Samples of Hemophilia
Bibliographic record
Abstract
Bleeding disorders account for a significant health burden in the global population. An early and accurate detection of related biomarkers can surely help in its reduction. However, diagnostic platforms generally lack the ability to detect relatively lower levels of biomarkers with high accuracy in complex biofluids, such as blood serum. Therefore, there is an urgent requirement of cost-effective platforms like electrochemical biosensors for the detection of biomarkers such as thrombin, which is present at low levels (0.01–0.5 U/mL) in the serum of hemophilic patients. In this study, a Cu nanoparticles (NPs)-dispersed laser-induced graphene (Cu-LIG)-based electrochemical biosensor is fabricated for the measurement of thrombin in biomimetic samples of hemophilia. The sensing surface is created via the suspension polymerization of phenol and formaldehyde with the in situ impregnation of Cu salt in the reaction mixture. Laser ablation is used to convert the polymeric content to the electrically conductive sp 2 hybridized graphitic carbon and the Cu salt to Cu NPs. Thrombin detection is achieved by depositing ferrocene-modified fibrinogen on the sensor, which acts as the substrate for thrombin. The quantification is based on the enzymatic activity of thrombin. The sensor shows a linear response over 0.01–25 U/mL concentration range covering the low concentration levels found in serum of hemophilic patients. The response is unaffected by common interfering biomolecules present in the blood. The accuracy of the sensor is verified using serum samples spiked with thrombin enzyme. Furthermore, the performance of the sensor remains unaltered over a storage of 30 days. Thus, the developed Cu-LIG based electrochemical biosensor shows the ability of detecting low levels of thrombin, generally expected in serum samples of hemophilic patients, which can help in designing effective treatment regimens and follow-ups by the clinicians.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 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".