Synergistic Dual-Carbon Networks Bridged Mn-Doped TiNb<sub>2</sub>O<sub>7</sub> Anode for Fast-Charging Lithium-Ion Batteries
Bibliographic record
Abstract
The development of anode materials for lithium-ion batteries must meet the demands for high safety, high energy density, and fast-charging performance. TiNb 2 O 7 is notable for its high theoretical specific capacity, low structural strain, and exceptional fast-charging capability, attributed to its Wadsley–Roth crystal structure. However, its inherently poor conductivity has hindered its practical application. This study employed an integrated internal and external modification strategy to enhance the electrochemical performance of TiNb 2 O 7 . The Mn ions was doped internally via the first hydrothermal reaction while a bridged conductive network with reduced graphene oxide (rGO) and carbon nanotubes (CNTs) was constructed by the second hydrothermal reaction, thereby improving both ionic and electronic conductivity of TiNb 2 O 7 simultaneously. The resulting dual-carbon network-bridged Mn-doped TiNb 2 O 7 (Mn 0.1 -TNO@rGO/CNT) delivered a specific capacity of 280 mAh g –1 at 0.5 C, a high-rate capacity of 177 mAh g –1 at 30 C, and retained 233.9 mAh g –1 after 200 cycles at 0.5 C, corresponding to an 84.1% capacity retention rate and a cycle fade rate of only 0.0795% per cycle. The superior rate performance and cycling stability of Mn 0.1 -TNO@rGO/CNT were maintained over a wide-temperature range. Besides, the strategy of dual-carbon network bridging and Mn-doping effectively prevents the TiNb 2 O 7 spheres from cracking after long cycling. To assess the practical feasibility, the cell assembled using Mn 0.1 -TNO@rGO/CNT with high mass loading around 5 mg cm –2 demonstrated an initial capacity of 240 mAh g –1 at 0.5 C and delivered 60 mAh g –1 at a high rate of 20 C. Furthermore, a full cell paired with a LiNi 0.5 Mn 1.5 O 4 cathode delivered a specific capacity of 81.3 mAh g –1 at 2 C and exhibited a high capacity retention of 68% after 500 cycles at 5 C.
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".