Direct Visualization of the Enzymatic Digestion of a Single Fiber of Native Cellulose in an Aqueous Environment by Atomic Force Microscopy
Bibliographic record
Abstract
Atomic force microscopy (AFM) was used to study native cellulose films prepared from a bacterial cellulose source, Acetobacter xylinum, using a novel application of the Langmuir-Blodgett technique. These films allowed high-resolution AFM images of single fibers and their microfibril structure to be obtained. Two types of experiments were performed. First, the fibers were characterized using samples that were dried after LB deposition. Next, novel protocols that allowed us to image single fibers of cellulose in films that were never dried were developed. This procedure allowed us to perform in situ AFM imaging studies of the enzymatic hydrolysis of single cellulose fibers in solution using cellulolytic enzymes. The in situ degradation of cellulose fibers was monitored over a 9 h period using AFM. These studies provided the first direct, real-time images of the enzymatic degradation of a single cellulose fiber. We have demonstrated the tremendous potential of AFM to study the mechanism of the enzymatic digestion of cellulose and to identify the most effective enzymes for the digestion of various cellulose structures or isomorphs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".