One-Step Fabrication of 2.5D CuMoOx Interdigital Microelectrodes Using Numerically Controlled Electric Discharge Machining for Coplanar Micro-Supercapacitors
Bibliographic record
Abstract
With the increasing market demands for wearable and portable electronic devices, binary metal oxides (BMOs) with a remarkable capacity and good structure stability have been considered as a promising candidate for fabricating coplanar micro-supercapacitors (CMSCs), serving as the power source. However, the current fabrication methods for BMO microelectrodes are complex, which greatly hinder their further development and application in BMO CMSCs. Herein, the one-step fabrication of 2.5D CuMoOx-based CMSCs (CuMoCMSCs) has been realized by numerically controlled electric discharge machining (NCEDM) for the first time. In addition, the controllable capacity of CuMoCMSCs has been achieved by adjusting the NCEDM-machining voltage. The CuMoCMSCs machined by a machining voltage of 60 V (CuMoCMSCs60) showed the best performance. The fabricated CuMoCMSCs60 with binary metal oxides could operate at an ultra-high scanning rate of 10 V s−1, and gained a capacity of 40.3 mF cm−2 (1.1 mA cm−2), which is more than 4 times higher than that of MoOx-based CMSCs (MoCMSCs60) with a single metal oxide. This is because CuMoOx BMOs materials overcome the poor electroconductivity problem of the MoOx single metal oxide. This one-step and numerically controlled fabrication technique developed in this research opens a new vision for preparing BMO materials, BMO microelectrodes, and BMO microdevices in an environmental, automatic, and intelligent way.
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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 | 0.000 | 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".