pH‐Dependent Morphology Control of Cellulose Nanofiber/Graphene Oxide Cryogels
Bibliographic record
Abstract
The precise control of the ice crystal growth during a freezing process is of essential importance for achieving porous cryogels with desired architectures. The present work reports a systematic study on the achievement of multi-structural cryogels from a binary dispersion containing 50 wt% 2,2,6,6-tetramethylpiperidin-1-oxyl, radical-mediated oxidized cellulose nanofibers (TOCNs), and 50 wt% graphene oxide (GO) via the unidirectional freeze-drying (UDF) approach. It is found that the increase in the sol's pH imparts better dispersion of the two components through increased electrostatic repulsion, while also causing progressively weaker gel networks leading to micro-lamella cryogels from the UDF process. At the pH of 5.2, an optimum between TOCN and GO self-aggregation and dispersion is achieved, leading to the strongest TOCN-GO interactions and their templating into the regular micro-honeycomb structures. A two-faceted mechanism for explaining the cryogel formation is proposed and it is shown that the interplay of the maximized TOCN-GO interactions and the high affinity of the dispersoid complexes for the ice crystals are necessary for obtaining a micro-honeycomb morphology along the freezing direction. Further, by linking the microstructure and rheology of the corresponding precursor sols, a diagram for predicting the microstructure of TOCN-GO cryogels obtained through the UDF process is proposed.
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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) | 0.000 | 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 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".