Challenges of Nanoscale Characterisation of Litihum-Based Energy Materials
Bibliographic record
Abstract
The electric energy revolution is driven by battery technology. New chemistries and control systems enables the widespread use of portable electric devices, such as phones, computers, power tools and automobiles. The upward trend in battery performance relies on continuous research[1]. Scanning transmission electron microscopy (STEM) is a commonly used tool for nano-scale characterisation, as it allows for imaging as well as elemental and chemical analysis, when combined with energy dispersive spectroscopy (EDS) and electron energy loss spectroscopy (EELS)[2], [3]. EELS is a particularly useful technique, as the near-edge structure of the ionization edges often give chemical bonding information. However, the high energy electron beam used in STEM can damage materials[4]. This is especially problematic in the field of battery research. Low-Z elements such as lithium, carbon and oxygen, are particularly susceptible to knock-on damage[4]. Since these elements are common in lithium-ion batteries, understanding how the beam affects materials is critical to the analysis of the materials. Commercially pure chemical compounds (LiF, LiCl, Li2CO3), acquired from MilliporeSigma, are exposed to the microscope beam, under condition favorable for EELS analysis of the Lithium K-edge. An EELS spectrometer is used to serially acquire energy loss spectra of the materials. These time-sensing series show how the near edge structure of lithium evolve as the material is damaged by the beam. In this work, the abherrent effects of the electron microscope on lithium samples is made evident. [1] J. B. Goodenough and K.-S. Park, “The Li-Ion Rechargeable Battery: A Perspective,” Journal of the American Chemical Society, vol. 135, no. 4, pp. 1167–1176, Jan. 2013. [2] D. B. Williams, Transmission electron microscopy: a textbook for materials science, 2nd ed. New York: Springer, 2008. [3] J. I. Goldstein et al., Scanning Electron Microscopy and X-Ray Microanalysis a Text for Biologists, Materials Scientists, and Geologists. Boston, MA: Springer US, 1992. [4] R. F. Egerton, “Mechanisms of radiation damage in beam-sensitive specimens, for TEM accelerating voltages between 10 and 300 kV,” Microsc. Res. Tech., vol. 75, no. 11, pp. 1550–1556, Nov. 2012. 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".