Engineering Self‐Powered Electrochemical Sensors Using Analyzed Liquid Sample as the Sole Energy Source
Bibliographic record
Abstract
Many healthcare and environmental monitoring devices use electrochemical techniques to detect and quantify analytes. With sensors progressively becoming smaller-particularly in point-of-care (POC) devices and wearable platforms-it creates the opportunity to operate them using less energy than their predecessors. In fact, they may require so little power that can be extracted from the analyzed fluids themselves, for example, blood or sweat in case of physiological sensors and sources like river water in the case of environmental monitoring. Self-powered electrochemical sensors (SPES) can generate a response by utilizing the available chemical species in the analyzed liquid sample. Though SPESs generate relatively low power, capable devices can be engineered by combining suitable reactions, miniaturized cell designs, and effective sensing approaches for deciphering analyte information. This review details various such sensing and engineering approaches adopted in different categories of SPES systems that solely use the power available in liquid sample for their operation. Specifically, the categories discussed in this review cover enzyme-based systems, battery-based systems, and ion-selective electrode-based systems. The review details the benefits and drawbacks with these approaches, as well as prospects of and challenges to accomplishing them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".