Salt-Induced Doping and Templating of Laser-Induced Graphene Supercapacitors
Bibliographic record
Abstract
The use of inexpensive and widely available CO 2 lasers to selectively irradiate polymer films and form a graphene foam, termed laser-induced graphene (LIG), has incited significant research attention. The simple and rapid nature of the approach and the high conductivity and porosity of LIG have motivated its widespread application in electrochemical energy storage devices such as batteries and supercapacitors. However, nearly all high-performance LIG-based supercapacitors reported to date are prepared from costly, petroleum-based polyimide (Kapton, PI). Herein, we demonstrate that incorporating microparticles of inexpensive, non-toxic, and widely abundant sodium salts such as NaCl and Na 2 SO 4 into poly(furfuryl alcohol) (PFA) resins enables the formation of high-performance LIG. The embedded particles aid in carbonization and act as a template for pore formation. While increasing both the carbon yield and surface area of the electrodes, the salt also dopes the LIG formed with S or Cl. The combination of these effects results in a two- to four-order-of-magnitude increase in device areal capacitance, from 8 μF/cm 2 for PFA/no salt at 5 mV/s to up to 80 mF/cm 2 for some PFA/20% Na 2 SO 4 samples at 0.05 mA/cm 2, significantly higher than that of PI-based devices and most other LIG precursors.
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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".