All-Solid-State Potentiometric Sensors for Potassium Ion Detection with Enhanced Stability By Interlayer Incorporation
Bibliographic record
Abstract
Solid state potassium ion selective electrode (K+ ISE) has been the epitome of the most studied chemical sensors in recent years due to its practical importance in biomedical applications. In fact, potassium monitoring in food and serum, urine, and potentially in brain has been carried out in the clinical and medical fields to reveal physical conditions of the patients such as renal diseases, hypopotassemia, alkalosis, cirrhosis of liver, etc. In this report, we developed an electrochemical sensing unit which working, counter and reference electrodes are integrated in a single plane as all-solid-state form. Here, a polyaniline intermediate layer and salt-saturated polyvinylebutyral top coating are introduced in the working and reference electrodes, respectively, in order to mitigate the output potential drift problem that prevented widespread use of solid-state K+ ISE. Morphological and electrochemical properties of K+ ISE are studied by using a scanning electron microscope (SEM) and a potentiostat, respectively. Various measurement modes of electrochemical measurements, including cyclic voltammetry (CV), chronopotentiometry (CP), and electrochemical impedance spectroscopy (EIS), are implemented. The K+ ISE show desirable properties including high sensitivity (60.5 mV/decade), low concentration for the limit of detection (10-5.8 M), and large range of linear detection (10-5 – 1 M). Selectivity of our of K+ ISE against NH4 + , Na+ , Mg2+ , Ca2+ , and Fe3+ was studied. With its high potential to be miniaturized, we foresee that our solid-state K+ ISE will find future applications in microdevices for clinical analysis, agricultural and, environmental applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".