Lignocellulose Nanoparticles Extracted from Cattle Dung as Pickering Emulsifiers for Microencapsulating Phase Change Materials
Bibliographic record
Abstract
Nanocelluloses have attracted much attention in both academic and industrial fields. However, nanocelluloses, including cellulose nanocrystals and cellulose nanofibers, are generally produced by a “top-down” strategy with tedious violent chemical reactions and energy-intensive mechanical treatments. Fabrication of nanocellulose via facile and green approaches with a low cost is always challenging and promising. The digestion of grass by ruminants resembles the extraction processing of nanocelluloses from plants. Herein, lignocellulose nanoparticles (LCNPs) were extracted from cattle dung via facile filtration and centrifugation separation methods, indicating that LCNPs occurred naturally in cattle dung and were formed during digestion of grass. LCNPs are mainly composed of lignin, cellulose, and hemicellulose and possess an average diameter of ∼50 nm, high surface charge of −36.2 mV, and outstanding water dispersity. LCNPs show excellent Pickering emulsifying ability just as classic nanocellulose due to their partial wettability with both oil and water phases. LCNP stabilized Pickering emulsions were then employed as templates to prepare phase change material (PCM) microcapsules with melamine-formaldehyde shells to prevent leakage of PCM. The obtained PCM microcapsules display good thermal stability, durability, high PCM core content of 88.9% and phase change enthalpy of 214.3 J g –1, and are promising for thermal energy storage and temperature regulation applications. This study provides a sustainable approach to extract nanocellulose as Pickering emulsifier and will facilitate the high-value-added utilization of cattle dung.
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".