Scalable High-Performance Ultraminiature Graphene Micro-Supercapacitors by a Hybrid Technique Combining Direct Writing and Controllable Microdroplet Transfer
Bibliographic record
Abstract
Miniaturization of energy storage devices can significantly decrease the overall size of electronic systems. However, this miniaturization is limited by the reduction of electrode dimensions and the reproducible transfer of small electrolyte drops. This paper reports first a simple scalable direct writing method for the production of ultraminiature microsupercapacitor (MSC) electrodes, based on femtosecond laser reduced graphene oxide (fsrGO) interlaced pads. These pads, separated by 2 μm spacing, are 100 μm long and 8 μm wide. A second stage involves the accurate transfer of an electrolyte microdroplet on top of each individual electrode, which can avoid any interference of the electrolyte with other electronic components. Abundant in-plane mesopores in fsrGO induced by a fs laser together with ultrashort interelectrode spacing enables MSCs to exhibit a high specific capacitance (6.3 mF cm –2 and 105 F cm –3 ) and ∼100% retention after 1000 cycles. An all graphene resistor–capacitor (RC) filter is also constructed by combining the MSC and a fsrGO resistor, which is confirmed to exhibit highly enhanced performance characteristics. This new hybrid technique combining fs laser direct writing and precise microdroplet transfer easily enables scalable production of ultraminiature MSCs, which is believed to be significant for practical application of micro-supercapacitor microelectronic systems.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".