Characterizing Li in partially lithiated layer materials using atomic‐resolution imaging, modeling, and simulation
Bibliographic record
Abstract
Abstract Understanding of phase‐stability and nanoscale structural modulation during lithiation of layer materials demand comprehensive analysis of the Li‐containing phases in the solid‐state reaction products. Conventional chemical analysis methods in the transmission electron microscope (TEM) are not ideal to detect Li in partially intercalated nanodomains because Li atoms do not remain stationary under the focused electron beam. An alternate approach combining density functional theory (DFT) modeling and multislice image simulation has been explored in the present study to analyze the intercalated structures and to detect and quantify Li from the recorded high‐resolution TEM (HRTEM) micrographs of partially intercalated phases. HRTEM micrographs from partially lithiated graphite and MoS 2 show variations in the interlayer spacings, but are not usually directly interpretable. Hypothetical intercalated microstructures of graphite and MoS 2 supercells have been generated using atomic‐scale simulations with systematically varying Li concentrations. The measured interplanar spacings are compared with those of experimentally recorded HRTEM micrographs from lithiated graphite and MoS 2 . The results confirm the coexistence of different lithiated phases at localized domains. This understanding of phase transformation and the lithium quantification provides a basis for understanding the structural accommodation of layered materials during intercalation.
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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".