Ultra-Fast Charging with over 5000 Cycles TiNb<sub>2</sub>O<sub>7</sub> Anodes for Lithium-Ion Batteries and All-Solid-State Batteries
Bibliographic record
Abstract
Electric vehicles (EVs) need batteries with ultrafast-charging and excellent safety performance.[1] However, safety concerns with lithium-ion batteries (LIBs), for example thermal runaway reactions, have been a significant challenge especially under fast charging.[2] Additionally, the energy density of batteries is restricted by the limited practical capacity of the electrodes.[3] To address these challenges, next-generation fast-charging anode materials are needed for both LIBs and solid-state lithium-ion batteries (SSLIBs). Titanium niobate (TiNb2O7, TNO) is a promising anode material in this regards, offering fast ionic diffusion kinetics, high theoretical capacity, and a stable material structure.[3] However, its inferior intrinsic electronic conductivity and synthesis-originating crystal defects impede the otherwise excellent electrochemical performance of TNO materials.[4] Recent work by our group has led to the design of single crystal TNO anodes [3] stabilized via Fe3+ doping [5] and engineered into TNO/graphene electrodes with extreme fast charging and long-cycling capability. Electrophoretically nano-assemble carbon-coated FeTNO(C-FeTNO)/reduced graphene oxide (rGO) electrodes, where rGO serves as binder and conductor have been cycled (half-cells in liquid electrolyte) for 5000 cycles at 5C rate retaining > 70% of their capacity. The application of the C-FeTNO/graphene anode in all-solid-state battery featuring a hybrid electrolyte [6] and LFP as cathode will be reported. Acknowledgement: This research was funded by Natural Sciences & Engineering Research Council of Canada (NSERC) and the Quebec FRQNT Scholarship program. The contributions of collaborators from Professor Bevan’s and Professor Gauvin’s groups are greatly appreciated. References: [1] Q. Zhao et al., Designing solid-state electrolytes for safe, energy-dense batteries, Nat. Rev. Mater. 5 (2020) 229-252. https://doi.org/10.1038/s41578-019-0165-5. [2] X.N. Feng et al., Mitigating thermal runaway of lithium-ion batteries, Joule 4 (2020) 743-770. https://doi.org/10.1016/j.joule.2020.02.010. [3] F. Yu, S. Wang, R. Yekani, A. La Monaca, G.P. Demopoulos, Single-crystal TiNb2O7 materials via sustainable synthesis for fast-charging lithium-ion battery anodes, J. Energy Storage 95 (2024) 112482. https://doi.org/https://doi.org/10.1016/j.est.2024.112482. [4] K.J. Griffith et al. Titanium niobium oxide: from discovery to application in fast-charging lithium-ion batteries, Chem. Mater. 33 (2021) 4-18. https://doi.org/10.1021/acs.chemmater.0c02955. [5] F. Yu, B. Miglani, S. Yuan, R. Yekani, K.H. Bevan, G.P. Demopoulos, Fe3+-substitutional doping of nanostructured single-crystal TiNb2O7 for long-stable cycling of ultra-fast charging anodes, Nano Energy 133 (2025) 110494. https://doi.org/https://doi.org/10.1016/j.nanoen.2024.110494. [6] S. Wang, G.P. Demopoulos, High-conductive polymer-in-porous garnet solid electrolyte structure for all-solid-state lithium batteries enabled by molecular engineering, Energy Storage Mater. 71 (2024) 103604. https://doi.org/https://doi.org/10.1016/j.ensm.2024.103604. Figure 1
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".