3D Binder-Free Mo@CoO Electrodes Directly Manufactured in One Step via Electric Discharge Machining for In-Plane Microsupercapacitor Application
Bibliographic record
Abstract
Cobalt oxide-based in-plane microsupercapacitors (IPMSCs) stand out as a favorable choice for various applications in energy sources for the Internet of Things (IoT) and other microelectronic devices due to their abundant natural resources and high theoretical specific capacitance. However, the low electronic conductivity of cobalt oxide greatly hinders its further application in energy storage devices. Herein, a new manufacturing method of electric discharging machining (EDM), which is simple, safe, efficient, and environment-friendly, has been developed for synthesizing Mo-doped and oxygen-vacancy-enriched Co-CoO (Mo@Co-CoO) integrated microelectrodes for efficiently constructing Mo@Co-CoO IPMSCs with customized structures in a single step for the first time. The Mo@Co-CoO IPMSCs with three loops (IPMSCs3) exhibited a maximum areal capacitance of 30.4 mF cm−2 at 2 mV s−1. Moreover, the Mo@Co-CoO IPMSCs3 showed good capacitive behavior at a super-high scanning rate of 100 V s−1, which is around 500–1000 times higher than most reported CoO-based electrodes. It is important to note that the IPMSCs were fabricated using a one-step EDM process without any assistance of other material processing techniques, toxic chemicals, low conductivity binders, exceptional current collectors, and conductive fillers. This novel fabrication method developed in this research opens a new avenue to simplify material synthesis, providing a novel way for realizing intelligent, digital, and green manufacturing of various metal oxide materials, microelectrodes, and microdevices.
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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 | 0.001 | 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".