In Situ Transmission Electron Microscopy of Electrocatalyst Materials: Proposed Workflows, Technical Advances, Challenges, and Lessons Learned
Bibliographic record
Abstract
Abstract In situ electrochemical liquid phase transmission electron microscopy (LP‐TEM) measurements utilize micro‐chip three‐electrode cells with electron transparent silicon nitride windows that confine the liquid electrolyte. By imaging electrocatalysts deposited on micro‐patterned electrodes, LP‐TEM provides insight into morphological, phase structure, and compositional changes within electrocatalyst materials under electrochemical reaction conditions, which have practical implications on activity, selectivity, and durability. Despite LP‐TEM capabilities becoming more accessible, in situ measurements under electrochemical reaction conditions remain non‐trivial, with challenges including electron beam interactions with the electrolyte and electrode, the lack of well‐defined experimental workflows, and difficulty interpreting particle behavior within a liquid. Herein a summary of the current state of LP‐TEM technique capabilities alongside a discussion of the relevant experimental challenges researchers typically face, with a focus on in situ studies of electrochemical CO2 conversion catalysts is provided. A methodological approach for in situ LP‐TEM measurements on CO2R catalysts prepared by electro‐deposition, sputtering, or drop‐casting is presented and include case studies where challenges and proposed workflows for each are highlighted. By providing a summary of LP‐TEM technique capabilities and guidance for the measurements, the goal is for this paper to reduce barriers for researchers who are interested in utilizing LP‐TEM characterization to answer their scientific questions.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".