One-pot complexation of phytic acid and polyethyleneimine on cellulosic microfibers towards insulative and flame-resistant foam
Bibliographic record
Abstract
Flame resistance is required for the deployment of bio-based materials, especially those forming cellular structures that endow thermal insulation. This study proposes a one-pot strategy to prepare cellular lignocellulosic composites with excellent flame resistance. Lignocellulosic microfibers were used as the substrate onto which a flame-retardant complex consisting of P-containing phytic acid (PA) and N-containing polyethyleneimine (PEI) was formed. Following the prediction of ab initio molecular dynamics simulation, PA and PEI are integrated onto MF-CTMP following a single-step complexation assembly triggered by pH effects. The PA-PEI modified MF-CTMP can be readily transformed into a composite solid foam by dewatering a wet foam followed by oven drying. At the expense of a slightly reduced thermal insulation (thermal conductivity increase from 33.6 ± 0.6 to 40.0 ± 0.6 mW/(m·K)) the presence of PA-PEI complexes significantly improved the mechanical performance of the foam and uniquely endows it with flame resistance. Compared to unmodified MF-CTMP foams, the composite foams showed significant improvement in the Young's, specific compression, and flexural moduli (increased by 13.5, 5.5, and 7.3 folds, respectively), a high oxygen index (up to 40.8 %) and self-extinguishing effects. The results suggest the suitability of the introduced lignocellulosic foam as an alternative to traditional synthetic polymer-based counterparts as well as inorganic matter for insulation, particularly relevant to the building sector.
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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".