Nano-TiO <sub>2</sub> /Graphene Using Pulsed Laser Ablation (ULPING) for Aqueous Sodium-Ion Pseudocapacitors
Bibliographic record
Abstract
The growing demand for sustainable energy storage solutions has accelerated research into sodium-ion systems, such as sodium-ion batteries (SIBs) and sodium-ion capacitors (NICs), as promising alternatives to lithium-ion energy storage. However, SIBs face challenges such as low energy density, sluggish kinetics, and limited cycle life, necessitating the development of advanced pseudocapacitor electrode materials. This study reports the fabrication of nanostructured TiO 2 and TiO 2 -graphene multilayered electrodes using ultrashort laser pulses for in situ nanostructure generation (ULPING) for aqueous sodium-ion pseudocapacitors. The TiO 2 layer was directly grown on titanium substrates via pulsed laser ablation using a ytterbium picosecond fiber laser, achieving a highly porous floret-like morphology conducive to ion diffusion. An additional graphene layer, applied with a carboxymethyl cellulose (CMC) binder, improved conductivity and surface area. Structural characterization revealed a mixed anatase/rutile TiO 2 phase with predominant rutile, which is advantageous for high-power performance. Electrochemical characterization in a 1 M Na 2 SO 4 electrolyte using a three-electrode testing system demonstrated that the TiO 2 -graphene electrodes exhibited a significantly higher areal capacitance of 46.9 mF/cm 2 and an energy density of 22.3 μWh/cm 2, approximately 3.5× higher than pristine TiO 2 . Cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) confirmed a pseudocapacitive behavior with a stable capacitance retention of over 85% after 200 cycles. Moreover, the TiO 2 -graphene electrode showed a reduced charge transfer resistance and a higher sodium-ion diffusion coefficient (2.124 × 10 – 14 cm 2 /s) compared to pristine TiO 2, indicating enhanced electrochemical kinetics. This study highlights the potential of ULPING as a scalable and cost-effective technique for engineering high-performance electrode materials, positioning TiO 2 -graphene hybrids as viable candidates for next-generation aqueous sodium-ion pseudocapacitors.
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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".