Standardized Morphology Analysis of Cellulose Nanocrystals via a Semi-Automated Image Processing Approach
Bibliographic record
Abstract
This thesis addresses the challenges in the investigation of cellulose nanocrystals (CNC) particle size measurements using transmission electron microscopy (TEM) and atomic force microscopy (AFM) image analysis. Standardizing particle size measurements an important step in the design and optimization of the processes employed in the manufacture and utilization of CNCs. Current protocols used in the analyses of CNC particle morphology for TEM and AFM images are largely manual and time-consuming, and often produce inconsistent results between different researchers.\nChapter 1 introduces a new semi-automated image analysis framework that can reliably and quickly detect and classify CNCs from TEM and AFM images, measure their dimensions and each particle’s detailed shape information, and provide additional information about different CNC configurations within the images. Chapter 2 explains the development of this framework, named as CNC-SMART (CNC – Standardized Morphology Analysis for Research and Technology), which utilizes different automated and semi-automated image processing workflows. The viability of this framework is demonstrated in this work using exemplar images obtained for a National Research Council Canada certified reference material, CNCD-1. The results obtained from the SMART approach presented in this work are compared critically against the results obtained from the conventional manual approaches. These comparisons revealed a good agreement between the manual and SMART approaches and proved that CNC-SMART can expeditiously process high-throughput image data using these workflows while being minimally impacted by human error and variability.\nChapter 3 and Chapter 4 are two different demonstrations of CNC-SMART which can be considered case studies in which the SMART approach was adapted and improved for further applications. Case studies are an extension of an inter-laboratory comparison (ILC) research on CNC particle size measurements performed by ten participating research laboratories using TEM and AFM imaging. Collaborating with different researchers and using different image datasets with varying image properties such as noise and contrast helped refine the SMART approach to be a more versatile system with more capabilities that were not primarily considered during the initial development phase.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".