Hyperbranched lignin nanoparticles-enabled nanohybrid film for high-performance electromagnetic interference shielding and dual-mode thermal management
Bibliographic record
Abstract
Electromagnetic interference (EMI) becomes a critical environmental and health concern with the proliferation of numerous electronic devices. However, achieving uniform dispersion and alignment of magnetic and conductive nanofillers to prepare EMI shielding films remains challenging. In this study, a bio-based multifunctional nanohybrid film, namely CNFs/MXene/T-Fe 3 O 4 /H-LNPs (CMFL), was constructed by integrating hyperbranched lignin nanoparticles (H-LNPs), cellulose nanofibrils (CNFs), MXene, and tetraethyl orthosilicate (TEOS) modified Fe 3 O 4 (T-Fe 3 O 4 ). It is hypothesized that the hydroxyl-rich H-LNPs, serving as effective dispersants, facilitate the uniform dispersion of T-Fe 3 O 4 through electrostatic repulsion. Furthermore, these H-LNPs, acting as nano-bonding agents, not only promote the ordered alignment and interfacial bonding of MXene lamellae, but also endow the resultant CMFL film with excellent mechanical properties with tensile strength of 41.89 MPa. Thanks to the synergy of polarization, conduction, and hysteresis losses, the optimized CMFL film sample can achieve 61.3 dB of EMI shielding effectiveness at a thickness of 67 μm. Moreover, the nanohybrid film demonstrates dual-mode thermal management enhanced by photothermal conversion and Joule heating effects. It can rapidly heat to 76.1 °C under 1.0 kW·m −2 of solar irradiation and stabilize at 68.3 °C under a 3 V input. This study offers a sustainable approach to developing advanced bio-based materials for EMI shielding and thermal management applications.
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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) | 0.001 | 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".