Tri-continuous polymer templates enable scalable fabrication of hierarchical nanoparticle monoliths
Bibliographic record
Abstract
Hierarchical structures with bimodal porosity are crucial in diffusion and confinement-driven applications, such as catalysis and separation. This study introduces the first utilization of polymer blend nanocomposites as templates for isolating nanoparticle monoliths with bimodal porosity. We examined tri-continuous polymer blend nanocomposites of silica nanoparticles (SNPs) in polyethylene (PE), ethylene vinyl acetate (EVA), and polyethylene oxide (PEO) using three-channel confocal microscopy. This allowed visualization of their morphology and its evolution during quiescent annealing. The analysis extends to co-continuous polymer blend nanocomposites, with or without PEO. Our findings highlight the reinforcing effect of sequentially adding polymer phases in tri-continuous blends. This results in a refined morphology and strengthened three-dimensional particle network, as evidenced by a two-order-of-magnitude increase in the terminal modulus in frequency sweep rheometry. Conversely, co-continuous systems exhibit a significantly weaker particle network with a minimal increase in terminal storage modulus, making them prone to collapse during the polymer template removal. The interplay between domain size, nanoparticle jamming within one phase, and consequent particle network robustness enables the material to withstand deformation during polymer removal, facilitating the isolation of hierarchically structured monoliths. This novel templating method offers a scalable approach to fabricating hierarchically porous materials with potential applications in catalysis, energy storage, and gas separation.
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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 | 0.001 |
| 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".