High C-Rate Dynamic Lithium (de)Insertion Pathway Investigated via Synchrotron-Based Operando XRD and Operando Scanning x-Ray Microscopy
Bibliographic record
Abstract
Lithium-ion insertion kinetics fundamentally hinges upon phase transformation behavior during (dis)charging and understanding the rate-dependent kinetics is crucial for the development of high-power batteries. At high c-rates, kinetic hysteresis is amplified and phase evolution becomes heterogeneous and unpredictable. Specifically, discharge becomes more sluggish than charging of most battery electrodes including LiNixMnyCozO2 (NMC) and LiFePO4 (LFP). Here, we developed an operando soft x-ray microscopy to simultaneously observe surface charge transfer and bulk lithium diffusion in facet-controlled individual battery particles over a wide range of cycling rates (0.01 – 10C). Our result unambiguously reveals that dynamic asymmetry between fast charging and discharging originates from auto-inhibitory Li-rich and autocatalytic Li-poor surface domains, respectively. In addition, we developed synchrotron-based operando fast XRD to track phase evolution during fast cycling. We directly observed that sluggish Li diffusion at high Li content induces different phase transformations during charging and discharging, with strong phase separation and homogeneous phase transformation during charging and discharging, respectively. Moreover, by electrochemically manipulating the lithium-ion concentration distribution within NCM particles, phase separation pathway could be redirected to solid-solution kinetics even at 7 C-rate. Our work lays the groundwork for developing high-power applications and ultrafast charging protocols 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 | 0.000 |
| 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".