Investigation of Electron-Beam Induced Damage in Lithium-Ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries are extensively used to transform chemical energy into the electrical energy and vice versa. These batteries are inevitably the best candidates for portable devices such as mobile phones, laptops and specially in the fast-growing market of electrical automobiles. The performance of these batteries is strongly connected to the microstructure of their electrodes. Thus, improvement of lithium-ion batteries requires an accurate structural investigation of their electrodes which is achievable by using electron microscopes. However, the electron microscopes may damage the specimen during the investigation process. Once the beam electrons penetrate into the material, the columbic force of nucleus and electrons can scatter the beam electrons. When beam electrons undergo high angle scattering events, the law of conservation of momentum required momentum transfer to the atoms in the sample. In cases where the transferred energy is large enough to overcome a barrier called displacement energy (Ed), the atom can be displaced from its original lattice site. This displacement of atoms is called knock-on displacement, a significant source of electron beam damage. As a light element, Lithium has very small displacement barrier which makes the lithium base electrodes vulnerable to the high energy beam of electron microscopes. In this study, we use a Density Functional Theory (DFT) based method called Nudged Elastic Band (NEB) to calculate the displacement energies. These values were then applied to the conservation of momentum to find the threshold displacement energy which is the beam energy that creates the knock-on damage inside the material during the electron microscopic investigation. Finding the safe electron beam energy range opens a new door for more accurate structural characterization of lithium compounds as a step for further improvement of lithium ion batteries.
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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 | 0.001 | 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".