Modeling Biodegradable Free Chlorine Sensor Performance Using Artificial Neural Networks
Bibliographic record
Abstract
Abstract Electrochemical sensors are used to measure target analytes in water, meat, fruits, or vegetables, to ensure their safety, security, and integrity for human use. In this paper, a solution‐based fabrication process is presented for a biodegradable electrochemical free chlorine sensor using asparagine that is functionalized onto graphene oxide (GO). An ink solution of the GO functionalized with asparagine is synthesized, and then deposited onto a screen‐printed carbon electrode (SPCE) using a spin coater. The sensor shows high a sensitivity of 0.30 µA ppm −1 over a linear range of 0–8 ppm with a hysteresis‐limited resolution of 0.2 ppm after achieving a steady state at 50 s. From the development and testing of the free chlorine sensor, over 9000 datapoints are collected and used for training an artificial neural network (ANN) model to quantify and characterize the factors affecting the free chlorine sensor's performance, and model its degradation characteristics. The model reports a low mean absolute error (MAE) of 0.1603 and a high model accuracy with a Pearson correlation coefficient (PCC) of 0.9950, demonstrating that these degradation characteristics can be modeled and be used to compensate the degraded performance characteristics of the free chlorine sensors.
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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 | 0.001 |
| 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".