Pickering multiphase materials using plant‐based cellulosic micro/nanoparticles
Bibliographic record
Abstract
Abstract Pickering multiphase systems stabilized by solid particles have recently attracted increasing attention due to their excellent stability. Among various solid stabilizers, natural and renewable cellulosic micro/nanoparticles that are derived from agricultural and forestry sources have become promising candidates for Pickering stabilization due to their unique morphological features and tunable surface properties. In this review, recent progress on forming and stabilizing Pickering multiphase systems using cellulosic colloidal particles is summarized, including the physicochemical factors affecting their assembly at the interfaces and the preparation methods suitable for producing Pickering emulsions. In addition, relevant application prospects of corresponding Pickering multiphase materials are outlined. Finally, current challenges and future perspectives of such renewable Pickering multiphase systems are presented. This review aims to encourage the utilization of cellulosic micro/nanoparticles as key components in the development of Pickering systems, leading to enhanced performance and unique functionalities.
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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".