Molecularly Imprinted Electroimpedance Sensor for Detection of 8-isoprostane in Exhaled Breath Condensate
Bibliographic record
Abstract
Development of low-cost, rapid response time capacitive sensors have a valuable role in the creation of point-of-care systems.A novel approach in the materials and application of Molecularly Imprinted Polymers was investigated for the detection of 8isoprostane in exhaled breath condensate.The detection method is based on the quantifiable capacitance change that occurs between two electrodes as the target molecule binds on the MIPs surface, which is detected through electrochemical impedance spectroscopy.This work focuses on the use of a generic polymer material for the sensing layer, as opposed to a traditional synthesized polymer material.PVA-SbQ was spun onto a custom IDE and then imprinted to detect 8-isoprostance.With aerosolized samples, the sensor was proven to detect a physiologically relevant concentration of 1 to 100 pg/mL.A fully integrated multiplexed system was then developed for point-of-care health monitoring.Dr. Siziwe Bebe.The test setup, data acquisition, modeling, data analysis, and PCB design were performed solely by the author.The FGO chemiresistive gas sensor project, referenced as C, was performed in collaboration with the same group and the main author, Mr. Ivan Amor.For this report, the author assisted in the development of the readout circuitry, measurements, and data analysis of initial tests.This conference paper describes the sensors' ability to detect ammonia and acetone at ultra-low concentrations in gaseous forms.The paper provides an analysis of the functionalization process of the graphene-oxide, the readout system, and the usability of the sensor for point-of-care health monitoring.
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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) | 0.000 | 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) | 0.002 | 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".