Historical Evolution of Electrodes and Their Impact on Electrochemical Sensing and Biosensing
Bibliographic record
Abstract
This review outlines the historical development of electrodes and their importance in electrochemical sensing and biosensing. Electrode design and material choice directly influence sensitivity, selectivity, and applicability. Early systems such as mercury-based dropping mercury electrodes (DMEs) provided reproducible surfaces and broad potential windows, although their toxicity and environmental concerns restricted widespread use. The shift to solid electrodes including glassy carbon, carbon paste, and noble metals brought higher stability, conductivity, and simpler modification, which expanded sensing applications. Subsequent advances such as screen-printed and pencil graphite electrodes introduced low-cost, disposable formats that made electrochemical sensing more portable and accessible. More recently, flexible substrates, 3D-printed devices, and nanostructured materials have created opportunities for wearable technologies, real-time monitoring, and ultra-sensitive detection. Alongside these material innovations, this review examines current gaps related to scalability, commercialization, and sustainability, where translation from laboratory research to practical devices remains limited. The growing role of artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in optimizing electrode design, enabling large-scale data analysis, and supporting remote monitoring is also discussed. By combining historical insights with present challenges, this review outlines future directions toward reliable, safe, and widely accessible electrochemical sensing technologies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".