Microstructure-Dependent Thermal Stability of Super-Tetragonal Nanocomposite Films through <i>In Situ</i> TEM/EELS Study
Bibliographic record
Abstract
Smart microstructure design in nanocomposite films allows us to tailor physical properties such as ferroelectricity and thermal stability to broaden applications of next-generation electronic devices. Here, we study the thermal stability of self-assembled PbTiO 3 (PTO)/PbO nanocomposite films with nano-spherical and nanocolumnar microstructures by utilizing an environmental transmission electron microscopy (TEM) combined with electron energy loss spectroscopy (EELS). The real-time study reveals that the microstructure-dependent interphase strain has an effect on the stabilization of the tetragonal phase. Compared to the nano-spherical configuration, the nanocomposite film with the nanocolumnar microstructure can maintain the giant tetragonality of ∼1.20 up to 450 °C, and the tetragonal phase is predicted to be stable at elevated temperatures > 600 °C. Moreover, the temperature-dependent EELS further demonstrates the sensitivity of the chemical bonding of Pb and Ti with O to the PTO lattice distortion, correlating the structural variation and electronic properties at different temperatures. Such in situ heating TEM study provides insights into the thermal stability of nanocomposites with different microstructures and facilitates the advancement of power electronics applications in harsh environments.
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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 | 0.000 |
| 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".