Hierarchical Structured Cu/Ni/TiO<sub>2</sub> Nanocomposites as Electrodes for Lithium-Ion Batteries
Bibliographic record
Abstract
The electrochemical performance of anodes made of transition metal oxides (TMOs) in lithium-ion batteries (LIBs) often suffers from their chemical and mechanical instability. In this research, a novel electrode with a hierarchical current collector for TMO active materials is successfully fabricated. It consists of porous nickel as current collector on a copper substrate. The copper has vertically aligned microchannels. Anatase titanium dioxide (TiO 2 ) nanoparticles of ∼100 nm are directly synthesized and cast on the porous Ni using a one-step process. Characterization indicates that this electrode exhibits excellent performance in terms of capacity, reliable rate, and long cyclic stability. The maximum insertion coefficient for the reaction product of Li x TiO 2 is ∼0.85, a desirable value as an anode of LIBs. Cross-sectional SEM and EDS analysis confirmed the uniform and stable distribution of nanosized TiO 2 nanoparticles inside the Ni microchannels during cycling. This is due to the synergistic effect of nano-TiO 2 and the hierarchical Cu/Ni current collector. The advantages of the Cu/Ni/TiO 2 anode include enhanced activity of electrochemical reactions, shortened lithium ion diffusion pathways, ultrahigh specific surface area, effective accommodation of volume changes of TiO 2 nanoparticles, and optimized routes for electrons transport.
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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) | 0.000 | 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 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".