Recent Advances on SEM-Based In Situ Multiphysical Characterization of Nanomaterials
Bibliographic record
Abstract
Functional nanomaterials possess exceptional mechanical, electrical, and optical properties which have significantly benefited their diverse applications to a variety of scientific and engineering problems. In order to fully understand their characteristics and further guide their synthesis and device application, the multiphysical properties of these nanomaterials need to be characterized accurately and efficiently. Among various experimental tools for nanomaterial characterization, scanning electron microscopy- (SEM-) based platforms provide merits of high imaging resolution, accuracy and stability, well-controlled testing conditions, and the compatibility with other high-resolution material characterization techniques (e.g., atomic force microscopy), thus, various SEM-enabled techniques have been well developed for characterizing the multiphysical properties of nanomaterials. In this review, we summarize existing SEM-based platforms for nanomaterial multiphysical (mechanical, electrical, and electromechanical) in situ characterization, outline critical experimental challenges for nanomaterial optical characterization in SEM, and discuss potential demands of the SEM-based platforms to characterizing multiphysical properties of the nanomaterials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".