Surfactant-Mediated Highly Conductive Cellulosic Inks for High-Resolution 3D Printing of Robust and Structured Electromagnetic Interference Shielding Aerogels
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.
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) | 0.000 | 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".