Comsol Multiphysics modeling of an electrochemical biosensor using carbon nanotubes for detecting urinary estrogen receptor
Bibliographic record
Abstract
A class of steroid hormones known as estrogens is essential for the health of the heart, bones, and reproductive system. Changes in estrogen levels have been connected to several health problems, such as endocrine disorders, metabolic syndromes, and cancer. In pharmaceutical applications, environmental monitoring, and medical diagnostics, biosensors that measure estrogen levels are essential. This study models estrogen detection biosensors based on urine liquid, horseradish peroxidase biorecognition, and carbon nanotubes (CNT) using Comsol Multiphysics. This study demonstrates that most interactions happen at the upper boundary of the concave pillars put inside the box. Besides, it shows that the velocity has the highest value between the concave pillars inside the box. The results demonstrate that the number of interactions (absorption and adsorption) rises with increasing the concave pillars' area, affecting the biosensor output.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".