<i>In-situ</i> TEM study from the perspective of holders
Bibliographic record
Abstract
During the in situ transmission electron microscopy (TEM) observations, the diverse functionalities of different specimen holders play a crucial role. We hereby provide a comprehensive overview of the main types of holders, associated technologies and case studies pertaining to the widely employed heating and gas heating methods, from their initial developments to the latest advancement. In addition to the conventional approaches, we also discuss the emergence of holders that incorporate a micro-electro-mechanical system (MEMS) chip for in situ observations. The MEMS technology offers a multitude of functions within a single chip, thereby enhancing the capabilities and versatility of the holders. MEMS chips have been utilized in environmental-cell designs, enabling customized fabrication of diverse shapes. This innovation has facilitated their application in conducting in situ observations within gas and liquid environments, particularly in the investigation of catalytic and battery reactions. We summarize recent noteworthy studies conducted using in situ liquid TEM. These studies highlight significant advancements and provide valuable insights into the utilization of MEMS chips in environmental-cells, as well as the expanding capabilities of in situ liquid TEM in various research domains.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".