Surfactant-Mediated Highly Conductive Cellulosic Inks for High-Resolution 3D Printing of Robust and Structured Electromagnetic Interference Shielding Aerogels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological fusion of emerging three-dimensional (3D) printing of aerogels with gel processing enables the fabrication of lightweight and functional materials for diverse applications. However, 3D-printed constructs via direct ink writing for fabricating electrically conductive structured biobased aerogels suffer several limitations, including poor electrical conductivity, inferior mechanical strength, and low printing resolution. This work addresses these limitations via molecular engineering of conductive hydrogels. The hydrogel inks, namely, CNC/PEDOT-DBSA, featured a unique formulation containing well-dispersed cellulose nanocrystal decorated by a poly(3,4-ethylene dioxythiophene) (PEDOT) domain combined with dodecylbenzene sulfonic acid (DBSA). The rheological properties were precisely engineered by manipulating the solid content and the intermolecular interactions among the constituents, resulting in 3D-printed structures with excellent resolution. More importantly, the resultant aerogels following freeze-drying exhibited a high electrical conductivity (110 ± 12 S m –1 ), outstanding mechanical properties (Young’s modulus of 6.98 MPa), and fire-resistance properties. These robust aerogels were employed to address pressing global concerns about electromagnetic pollution with a specific shielding effectiveness of 4983.4 dB cm 2 g –1 . Importantly, it was shown that the shielding mechanism of the 3D printed aerogels could be manipulated by their geometrical features, unraveling the undeniable role of additive manufacturing in materials design.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it