Preparation of Model Surfaces to Mimic Porous Cellulose Structures
Bibliographic record
Abstract
Abstract Porous cellulose and nanocellulose materials, such as foams and aerogels, are widely used in numerous applications due to their large surface area, sorption capacity, mechanical resilience, and overall versatility. Additionally, porous cellulose materials provide extensive interaction sites that can facilitate a variety of chemical processes. Herein, ultrathin nanocellulose model surfaces with a 2D open‐pore structure are presented that mimic the complexity of porous cellulose fiber materials. A sacrificial templating approach is used by spin coating a mixture of 2,2,6,6‐tetramethylpiperidin‐1‐oxyl‐oxidized cellulose nanofibrils (TOCNFs) and polystyrene (PS) nanoparticles onto a silicon wafer, followed by selective nanoparticle dissolution. Scanning electron microscopy and atomic force microscopy reveal an ultrathin TOCNF layer with hierarchical morphology and spherical open pores (70 nm diameter), with a root mean square roughness of 19 nm. The surface coverage of nanoparticles is controlled primarily by changing the TOCNF concentration, and to a lesser extent, the ratio between PS nanoparticles and TOCNFs. X‐ray photoelectron spectroscopy supports the complete removal of the PS template, leaving behind a pure TOCNFs layer. Open‐pore structured nanocellulose model surfaces provide a tool to investigate interfacial phenomena in porous materials constructed from fibers and/or nanocelluloses, thus advancing the engineering of functional porous cellulose‐based materials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".