Photo-Reduced Graphene As Electrode Active Materials for Supercapacitor Applications
Bibliographic record
Abstract
Supercapacitors are among the most promising energy storage devices due to their high power density, short recharging time, long cycle life, as well as environmental friendliness. Supercapacitors with increased energy densities are needed for practical applications such as plug-in hybrid electric vehicles, wind turbine energy storage, regenerative breaking, cold starting of trucks and space exploration applications. Supercapacitors store energy using either ion adsorption (electrochemical double layer capacitors) or fast surface redox reactions (pseudo-capacitors). Electrochemical double layer capacitors use the high surface area of carbon, such as activated carbon, carbon fiber and graphene, as the electrode active materials, while metal oxides and electronically conducting polymers are used as the electrode active materials for pseudo-capacitors. Graphene is a form of carbon that has substantially higher surface area than the more common electrode active material, activated carbon, and thus potentially allows for more storing of electrostatic charge. The high electrical conductivity and planar structure of graphene also gives rise to fast charging and discharging. In this presentation, recent development on the fabrication of graphene by means of photo reduction of graphene oxide will be given. The photo reduction creates a disordered corrugated graphene structure in which adjacent sheets are no longer oriented parallel to each other. As an example, the performance of excimer laser reduced graphene and N-doped graphene when it is used as electrode active materials in supercapacitors was evaluated in both aqueous and organic electrolytes at room temperature. By varying the laser irradiation processing conditions such as laser energy and irradiation time, graphene and N-doped graphene with different supercapacitive behaviors were successfully grown. The specific capacitance of laser reduced graphene reaches 120 F/g at 5mV/sec in the aqueous electrolyte. Highly conductive N-doped graphene can be prepared by adding NH3 into graphene oxide solution during laser photo-reduction process and the doped graphene has slightly reduced specific capacitance of 110 F/g at 5 mV/sec in 0.5 M K2SO4 aqueous electrolyte. This work demonstrated that excimer laser irradiation process is a very promising technique for preparation of graphene and N-doped graphene for its use as the electrode active material for supercapacitors due to its excellent flexibility and capability of controlling microstructures, electric conduction and specific capacitance. Figure 1
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".