Covalent Carbide Interconnects Enable Robust Interfaces and Thin SEI for Graphite Anode Stability under Extreme Fast Charging
Bibliographic record
Abstract
Abstract Carbonaceous and carbon‐coated electrodes are ubiquitous in electrochemical energy storage and conversion technologies due to their electrochemical stability, lightweight nature, and relatively low cost. However, traditional reliance on conductive additives and binders leads to impermanent electrical pathways. Here, a general approach is presented to fabricate robust electrodes with a progressive failure mechanism by introducing carbide‐based interconnects grown via carbothermal conversion of (5 wt%) titanium hydride nanoparticles. This method concurrently enhances both electrical and mechanical properties within the electrode architecture. The resulting chemical bonding between active materials establishes a novel mechanism to maintain stable electrical pathways during cycling. Employed as Li‐ion battery anodes, these electrodes exhibit improved cyclability, achieving 80% capacity retention after 800 fast‐charge cycles at moderate loading (1 mAh cm − 2 ). High loading cells with areal capacity of 3 mAh cm −2 show significantly improved cycle life over the same number of cycles. This performance improvement is attributed to the absence of significant impedance growth and a thinner solid electrolyte interphase (SEI) layer formed at high current densities (4C) as demonstrated by X‐ray photoelectron spectroscopy and transmission electron microscopy studies. The enhanced conductivity facilitates SEI formation, lowering ionic impedance and mitigating lithium plating, ultimately leading to the reported extended cycle life.
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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) | 0.000 | 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 | 0.002 |
| 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".