Modeling enzymatic hydrolysis of lignocellulosic substrates using fluorescent confocal microscopy II: Pretreated biomass
Bibliographic record
Abstract
In this study, we extend imaging and modeling work that was done in Part I of this report for a pure cellulose substrate (filter paper) to more industrially relevant substrates (untreated and pretreated hardwood and switchgrass). Using confocal fluorescence microscopy, we are able to track both the structure of the biomass particle via its autofluorescence, and bound enzyme from a commercial cellulase cocktail supplemented with a small fraction of fluorescently labeled Trichoderma reseii Cel7A. Imaging was performed throughout hydrolysis at temperatures relevant to industrial processing (50°C). Enzyme bound predominantly to areas with low autofluorescence, where structure loss and lignin removal had occurred during pretreatment; this confirms the importance of these processes for successful hydrolysis. The overall shape of both untreated and pretreated hardwood and switchgrass particles showed little change during enzymatic hydrolysis beyond a drop in autofluorescence intensity. The permanence of shape along with a relatively constant bound enzyme signal throughout hydrolysis was similar to observations previously made for filter paper, and was consistent with a modeling geometry of a hollowing out cylinder with widening pores represented as infinite slits. Modeling estimates of available surface areas for pretreated biomass were consistent with previously reported experimental results.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".