Flexible 3D‐Printed Cellulosic Constructs for EMI Shielding and Piezoresistive Sensing
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
Abstract Advances in materials science and sustainability have positioned cellulose nanofibers (CNFs) as an important nanomaterial for creating complex 3D architectures through 3D printing techniques. However, the inherent limitations of 3D‐printed CNF‐based materials, such as poor electrical conductivity and restricted mechanical flexibility, pose barriers to their application in next‐generation electronics. The research addresses these challenges by integrating CNF‐based 3D printed frameworks with a conductive polymer via a process known as “cold chemical vapor polymerization” (CCVP). The procedure initiates with the direct ink writing (DIW) of the CNF hydrogel, which then undergoes saturation with Fe 3+ ions and freeze‐drying to produce ion‐embedded CNF frameworks. Subsequently, interconnected conductive pathways of poly(3,4‐ethylenedioxythiophene) (PEDOT) are generated within these structures using CCVP. This methodology allows for precise customization of electrical conductivity, resulting in the production of highly conductive (546 S m −1 ) and mechanically flexible (70% compressible) patterned constructs. This advancement is highlighted by the development of grid‐based structures designed for electromagnetic interference (EMI) shields. These innovative shields demonstrate an absorbance of 0.71 and a specific EMI shielding effectiveness of 3406.45 dB cm 2 g −1 . Furthermore, these aerogels function as highly sensitive piezoresistive sensors, demonstrating the versatility of this sustainable approach for advancing wearable electronics and multifunctional technologies.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it