Primary Knock-On Damage Prediction During the Electron Microscopy Characterization of Lithium-Containing Materials
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) are being evaluated as a possible energy storage solution to climate change for meeting power and energy demands [1]. Characterizing battery materials with high-resolution electron microscopy is essential for developing LIBs. Because lithium (Li) atoms are light and waekly bonded in Li-compounds, these materials are susceptible to knock-on damage [2]. This damage happens when a beam electron scatters elastically and transmits energy to the atom that is greater than its threshold displacement energy (TDE) [3]. This study investigated knock-on damage by Monte Carlo simulation and theoretical calculations of the probability of knock-on damage, PKOD. To achieve this, the TDEs were calculated using the sudden approximation technique within the Density Functional Theory (DFT) calculations for three groups of materials: LiX (X=F, Cl, Br), and Li2MSiO4 (M = Fe, Co, Mn), and pure elements (Li, Al, Mg). Fig. 1 shows the PKOD of three groups of materials of interest. As illustrated in this figure, pure Li has the largest PKOD, demonstrating the high vulnerability of Li to knock-on damage. It also signifies the strange behavior of Li regarding the appearance of a peak in PKOD in both its pure and compound form. This was also reported in reference [4], where a peak was also discovered in the knock-on damage cross-section of Li. Our theoretical calculations explain this behavior with the abrupt drop in the elastic cross section of Li with increasing energy, resulting from the small TDE and light weight of this element. We developed a Monte Carlo algorithm which utilizes the single scattering method like Win X-ray [5] and CASINO [6] to simulate the electron trajectories while tracking the knock-on damage. Fig. 2 illustrates the results of these simulations in which the green dot represents the place where the displacement of Li atoms. Comparing the amount of knock-on damage in this figure with the PKOD, shown in Fig. 1, demonstrates the consistency between simulations and theoretical calculations. Also, the difference in the amount of knock-on damage in materials within the same group (i.e., identical structures) could be explained by the difference in the TDE of Li in that material group. Nevertheless, other parameters, including elastic cross-section, density and atomic weight, were found to affect this damage. The probability of Li knock-on damage, PKOD, for a) pure elements (Li, Al, Mg), b) LiX (X = F, Cl, Br), and c) Li2MSiO4 (M = Co, Fe, Mn). Monte Carlo simulation of knock-on damage at 30 KeV in a) LiF, b) LiCl, c) LiBr, d) Li2CoSiO4, e) Li2FeSiO4, and f) Li2MnSiO4.
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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 | 0.001 |
| 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 | 0.001 |
| 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".