ULPING-Based Titanium Oxide as a New Cathode Material for Zn-Ion Batteries
Bibliographic record
Abstract
The need for alternative energy storage options beyond lithium-ion batteries is critical due to their high costs, resource scarcity, and environmental concerns. Zinc-ion batteries offer a promising solution, given zinc’s abundance, cost effectiveness, and safety, particularly its compatibility with non-flammable aqueous electrolytes. In this study, the potential of laser-ablation-based titanium oxide as a novel cathode material for zinc-ion batteries was investigated. The ultra-short laser pulses for in situ nanostructure generation (ULPING) technique was employed to generate nanostructured titanium oxide. This laser ablation process produced highly porous nanostructures, enhancing the electrochemical performance of the electrodes. Zinc and titanium oxide samples were evaluated using two-electrode and three-electrode setups, with cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge–discharge (GCD) techniques. Optimal cathode materials were identified in the Ti-5W (laser ablated twice) and Ti-10W (laser ablated ten times) samples, which demonstrated excellent charge capacity and energy density. The Ti-10W sample exhibited superior long-term performance due to its highly porous nanostructures, improving ion diffusion and electron transport. The potential of laser-ablated titanium oxide as a high-performance cathode material for zinc-ion batteries was highlighted, emphasizing the importance of further research to optimize laser parameters and enhance the stability and scalability of these electrodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".