Tailoring pore structure in nanocellulose cryogels: Enhancing thermal and electromagnetic interference shielding properties
Bibliographic record
Abstract
Engineering porosity levels in hierarchical cryogels presents an exciting opportunity for advancing electromagnetic interference (EMI) shielding materials. This study introduces a feasible approach to tailoring micro-scale morphology in cellulose nanofiber (CNF)-based cryogels by simply adjusting the freeze-templating temperature, resulting in tunable porosity and enhanced performance characteristics. By varying the freeze-templating temperature, we successfully controlled pore size (ranging from 31 to 178 μm), which influenced the mechanical strength (decreasing from 59 to 14 kPa). To explore the effect of micro-scale porosity on the EMI shielding performance, we rendered the CNF cryogels conductivity upon integrating poly(3,4-ethylenedioxythiophene) (PEDOT) with the cryogels framework via chemical vapor polymerization. Our results demonstrate that the larger pore sizes promoted an absorption-dominant EMI shielding mechanism, with an average absorbance (A) of 0.59 across the X-band frequency range. A specific EMI shielding effectiveness (SSE/t) of 4801.25 dB cm 2 g −1 was achieved for samples with larger porosities, highlighting the decent performance of these engineered cryogels. Our findings reveal a straightforward yet effective strategy for optimizing porosity to achieve appreciable shielding effectiveness, contributing to the advancement of sustainable, high-performance EMI shielding solutions.
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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".