Atomic-Scale Characterization of Microscale Battery Particles Enabled by a High-Throughput Focused Ion Beam Milling Technique
Bibliographic record
Abstract
The cathode materials in lithium-ion batteries (LIBs) require improvements to address issues such as surface degradation, short-circuiting, and the formation of dendrites. One such method for addressing these issues is using surface coatings. Coatings can be sought to improve the durability of cathode materials, but the characterization of the uniformity and stability of the coating is important to assess the performance and lifetime of these materials. For microscale particles, there are, however, challenges associated with characterizing their surface modifications by transmission electron microscopy (TEM) techniques due to the size of these particles. Often, techniques such as focused ion beam (FIB)-assisted lift-out can be used to prepare thin cross sections to enable TEM analysis, but these techniques are very time-consuming and have a relatively low throughput. The work outlined herein demonstrates a FIB technique with direct support of microscale cathode materials on a TEM grid that increases sample throughput and reduces the processing time by 60-80% (i.e., from >5 to ∼1.5 h). The demonstrated workflow incorporates an air-liquid particle assembly followed by direct particle transfer to a TEM grid, FIB milling, and subsequent TEM analysis, which was illustrated with lithium nickel cobalt aluminum oxide particles and lithium manganese nickel oxide particles. These TEM analyses included mapping the elemental composition of cross sections of the microscale particles using energy-dispersive X-ray spectroscopy. The methods developed in this study can be extended to high-throughput characterization of additional LIB cathode materials (e.g., new compositions, coating, end-of-life studies), as well as to other microparticles and their coatings as prepared for a variety of applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".