Effects of TiO2 Nanotube Size on the Performance of Li-Ion Battery with TiO2 Nanotube as Anode Material
Bibliographic record
Abstract
This paper aims to disclose how the size of TiO2 Nanotubes affects the performance of Li-ion battery with TiO2 nanotubes as anode material. Firstly, TiO2 nanotubes of two different sizes were synthetized by hydrothermal method, namely, L-TiO2 nanotubes and S-TiO2 nanotubes. Next, the morphology, structure, cycle performance, rate performance, and electrochemical performance of the button batteries respectively assembled with L-TiO2 nanotubes and S-TiO2 nanotubes as anode materials (L-battery and S-battery) were tested with a scanning electron microscope (SEM), a transmission electron microscope (TEM), an X-ray diffractometer (XRD), a battery test system, and an electrochemical workstation. The results show that S-battery has better specific discharge capacity (SDC), cycle stability and rate performance than L-battery. The initial DSC of S-battery was as high as 262.6mAh/g; after 100 cycles, the DSC of that battery was still 250.5mAh/g, down by only 0.046%. Even if the rate increased to 2C, the DSC of the battery was maintained at 151.8mAh/g.
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 | 0.001 |
| 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 | 0.001 |
| 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".