Unraveling the Effect of Electric Discharge Machining Current on the Fabrication of 3D Mo‐Doped VO<sub>0.2</sub> Integrated Microsupercapacitors
Bibliographic record
Abstract
Vanadium oxides are widely used for microsupercapacitors (MSCs) due to their multiple‐valence and high theoretical capacitance. A conceptually new approach of electric discharge machining (EDM) with computer‐aided control is developed to one‐step fabricate Mo‐doped VO0.2‐based electrodes and devices with designable geometry. The results demonstrate that the Mo@VO0.2 integrated interdigital MSCs (IIMSCs) with the narrowest electrode distance of 300 μm show the best capacitive performance, which is furtherly manifested by the electric field simulation. Moreover, this work concentrates on expounding the relationships between the EDM machining current, surface morphology of Mo@VO0.2, and the capacitive behavior of Mo@VO0.2 IIMSCs. Compared to the machining current of 2 and 3 A, the machining current of 1 A facilitates synthesizing smaller Mo@VO0.2 particles with more porosity and higher surface area and thus achieving a larger capacitance value for Mo@VO0.2 IIMSCs device, which is achieving 32 mF cm−2 at 1 mV s−1, working well up to an ultrahigh scan rate of 30 V s−1, and obtaining a good cyclic stability of 88.61% after 5000 cycles. Moreover, this innovative EDM approach opens a new avenue for one‐step synthesis of various ceramic metal oxides for various microdevices such as microbatteries and microsensors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".