Electrochemical Performance of Ti- and Zr-Doped LiCoO<sub>2</sub> Film Cathodes Prepared By Rf-Magnetron Sputtering for Lithium Microbatteries
Bibliographic record
Abstract
In an attempt to enhance the microstructural and electrochemical (EC) properties, LiCoO2 thin films were doped with titanium or zirconium. RF magnetron sputtering technique has been employed for the deposition of films on Au/Ti/SiO2/Si substrates from lithium-rich LiCoO2 target with mosaic configuration. The as-deposited and Ti- and Zr-doped LiCoO2 thin films at lower doping concentration exhibited the α-NaFeO2 structure with R-3 m symmetry as confirm from X-ray diffraction and Raman studies. The cyclic voltammogram of micro-electrodes in aqueous electrolyte exhibited perfect redox peaks with good reversibility. The chronopotentiometry studies revealed that the discharge capacity of pure LiCoO2 was 64 µAh cm-2 µm-1, while 2% Ti- and Zr-doped films showed enhanced capacities 69 and 68 µAh cm-2 µm-1 (248 mC cm-2 µm-1, 245 mC cm-2 µm-1) respectively. The Zr-doped films exhibited good structural stability even after 25 cycles with the capacity retention of 95%. The kinetics of lithium ions in pure and Ti/Zr-doped LiCoO2 thin film cathodes and their cycleability are studied using both aqueous Pt//LiCoO2and and non-aqueous Li//LiCoO2 cells and the results will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".