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 (TiNb 2 O 7 , 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 Fe 3+ 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".