Investigation of the porous structure of cellulosic substrates through confocal laser scanning microscopy
Bibliographic record
Abstract
At the most fundamental level, saccharification occurs when cell wall degrading enzymes (CWDEs) diffuse, bind to and react on readily accessible cellulose fibrils. Thus, the study of the diffusive behavior of solutes into and out of cellulosic substrates is important for understanding how biomass pore size distribution affects enzyme transport, binding, and catalysis. In this study, fluorescently labeled dextrans with molecular weights of 20, 70, and 150 kDa were used as probes to assess their diffusion into the porous structure of filter paper. Fluorescence microscopy with high numerical aperture objectives was used to generate high temporal and spatial resolution datasets of probe concentrations versus time. In addition, two diffusion models, including a simple transient diffusion and a pore grouping diffusion models, were developed. These models and the experimental datasets were used to investigate solute diffusion in macro- and micro-pores. Nonlinear least squares fitting of the datasets to the simple transient model yielded diffusion coefficient estimates that were inadequate for describing the initial fast diffusion and the later slow diffusion rates observed; on the other hand, nonlinear least squares fitting of the datasets to the pore grouping diffusion model yielded estimations of the micro-pore diffusion coefficient that described the inherently porous structure of plant-derived cellulose. In addition, modeling results show that on average 75% of the accessible pore volume is available for fast diffusion without any significant pore hindrance. The method developed can be applied to study the porous structure of plant-derived biomass and help assess the diffusion process for enzymes with known sizes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".