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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".