Polymer Electrolytes for Printed 2-D Microcapacitors with Silver Electrodes
Bibliographic record
Abstract
The next generations of wearable electronics require high-performance, low-cost, thin, and flexible 2-D electronic components, e.g. capacitors and transistors. The commercial feasibility of these technologies relies on printable, high throughput, highly electronically-conductive, and inexpensive silver electrodes/current collectors to replace gold counterparts. However, silver-based devices are prone to failure due to corrosion forming resistive oxides or dendrites in aqueous environments (as shown in Figure 1a). Polymer electrolytes, possessing high ionic conductivity and enabled double layer capacitance at electrode interface, are highly tunable to minimize the dendritic growth and can be a promising technology for these 2-D devices. In this study, some high-performance aqueous polymer electrolytes that have previously been demonstrated for supercapacitors were compared and modified for better integration into printed interdigitated devices with silver electrodes. The polymer electrolytes were evaluated for their apparent ionic conductivities, capacitance, and corrosion properties using cyclic voltammetry and electrochemical impedance spectroscopy. Polymer electrolytes can effectively inhibit dendritic formation as shown via optical microscope in Fig. 1b. The optimized electrolytes possessed (i) high ionic conductivity (>1 mS cm -1 ) at ambient, (ii) good ability to maintain well-dissociated ions enabling double layer capacitance formation (Fig. 1c), with capacitance >100 μF cm -2 and (iii) good compatibility with silver electrodes. Overall, the optimized polymer electrolytes can be a low-cost, viable alternative in printed electronics requiring high dielectric materials, such as microcapacitors and low-powered field-effect transistors. Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".