Green Supercapacitor Patterned by Synthesizing MnO/Laser-induced-graphene Hetero-nanostructures on Wood via Femtosecond Laser Pulses
Bibliographic record
Abstract
Eco-friendly next-generation energy storage devices with high energy density are required to meet the increasing demand for sustainable and green electronics. However, their manufacturing requires a lot of chemical precursors and is usually accompanied by chemical waste; it also involves laborious and time-consuming processes such as mixing, heat treating, casting, and drying. Here, we propose that mass production of microsupercapacitors (MSCs) for green electronics can be achieved by embedding manganese monoxide (MnO) on wood-derived laser-induced-graphene (LIG) via femtosecond laser direct writing (FsLDW) technique. The direct synthesis of MnO/LIG hetero-nanostructures on wood was realized by drop-casting a small amount of precursor between the first and second FsLDW. The preceding FsLDW thermochemically converts wood into LIG while the following FsLDW converts the precursor into MnO, resulting in MnO/LIG hetero-nanostructures. As-fabricated MnO/LIG MSC exhibits enhanced areal capacitance (35.54 mF cm at 10 mV s) and capacitance retention (approximately 82.31% after 10,000 cycles) with only a small inclusion of Mn sources (0.66 mg cm) and short production time (10 min cm), which attributes to operate light-emitting diodes, digital clocks, and electronic paper as well. This approach enables the green, facile, fast, and cost-effective fabrication of future sustainable energy storage devices for next-generation green electronics.
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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".