Imaging of Non-conducting Beam Sensitive Materials using Scanning Electron Microscopy: Practical Applications of ESEM and LVSEM
Bibliographic record
Abstract
Non-conducting and beam sensitive materials like polymers are utilized extensively in modern industry. With growing interest in leveraging the capabilities of these materials by optimizing their morphological and structural characteristics, scanning electron microscopy (SEM) is becoming an increasingly important tool. Despite the advancement of SEM and advent of high caliber instrumentation, numerous challenges impede the imaging and study of beam sensitive materials using SEM. This thesis focuses on Environmental SEM and Low Voltage SEM and attempts to showcase their characterization capabilities through practical examples of respirators/facemasks, and microplastics. A set of characterization protocols for evaluating the structure, chemistry, moisture retention, and wetting properties of face masks and respirators are presented in Chapter 3. Chapter 4 presents efforts undertaken for capturing and compiling SEM images to form the first open-source SEM datasets for microplastics segmentation through deep learning methods. The promising result facilitates automatic quantification and classification of microplastics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".