Surface Air Plasma Treatment of Laser-Induced Graphene-Based Electrodes for Enhanced Electrochemical Sensing Performance
Bibliographic record
Abstract
Laser-induced graphene (LIG) is a cost-effective and sustainable single-step electrode fabrication technique for electrochemical applications that eliminates the need for additional materials or complex processing. Plasma treatment has been applied to enhance the electrochemical performance of LIG electrodes. However, the effect of varying treatment durations on LIG performance remains unexplored, leaving a gap in understanding whether electrochemical properties improve gradually with increased exposure time. We investigate the effect of air plasma treatment time (0–120 s) on the electrochemical and material properties of LIG electrodes. Treatment intervals are set at 10 s from 0–60 s for detailed trend analysis, with an extended 120 s point. Cyclic voltammetry (CV) and material characterization techniques determine the optimal treatment time of the 50 s that enhances sensor performance by balancing increased surface reactivity with overexposure. Compared to untreated electrodes, the optimized electrodes exhibited a 243% increase in CV current, a 25-fold reduction in charge transfer resistance ( R ct ) measured via electrochemical impedance spectroscopy (EIS), a 10-fold lower limit of detection (LOD) for potassium ferrocyanide/ferricyanide. Additionally, the treated sensors maintained stable performance over 14 days, demonstrating their long-term reliability. These findings highlight the importance of plasma treatment optimization in enhancing the performance of LIG-based electrochemical 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) | 0.001 | 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".