In Situ Characterization of Si-Based Anodes By Coupling Synchrotron X-Ray Tomography and Diffraction
Bibliographic record
Abstract
In the present work, combined synchrotron X-ray tomography/diffraction analyses are performed along the 1st and 10th cycles of a Si-based anode [1]. An electrode volume of 943 × 943 × 208 µm3 is imaged with a voxel size of 0.65 µm. The electrode is made with nanocrystalline/amorphous Si particles synthetized by ball-milling as active material, a porous C paper as a current collector instead of a conventional Cu foil and graphene nanoplatelets as a conductive additive instead of usual carbon black. It is shown that this specific formulation has a major impact on the structural changes of the electrode occurring upon cycling by (i) preventing its macroscopic cracking and related electrical disconnections, (ii) limiting its volume expansion and improving its reversibility, indicative of limited permanent damage in the electrode architecture, (iii) preventing the formation of c-Li15Si4 which is known to be detrimental for the electrode cyclability. However, the capacity fade with cycling remains significant. It can result from the accumulation of SEI products with cycling, progressively obstructing the porous network of the electrode, as supported by a significant decrease of the electrode porosity between the 1st and 10th cycle. The displacement and/or micro-cracking of the Si particles can also induce some electrical disconnections and irreversibility of the lithiation reaction. However, this degradation process occurring at the particle scale cannot be clearly identified from the present in-situ XRCT measurements due to spatial resolution limitation. [1] V. Vanpeene, A. King, E. Maire, L. Roué. In situ characterization of Si-based anodes by coupling synchrotron X-ray tomography and diffraction. Nano Energy 56 (2019) 799-812 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 | 0.001 | 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".