3D-Printed Interfacially Jammed Emulsion Aerogels
Bibliographic record
Abstract
3D printing ultralightweight porous structures using direct ink writing (DIW) while maintaining their mechanical robustness is highly challenging. This difficulty is amplified when low ink concentrations are used to create complex geometries. Herein, this shortfall was addressed by interfacially jammed emulsion gels. The gel emerged from the electrostatic interaction among synergized nanomaterials (graphene oxide (GO) and cellulose nanocrystals (CNCs)) in the aqueous phase and a ligand in the oil phase. This interaction led to the jamming of the nanoparticles and the creation of stable emulsion gels. The formed interfacial assemblies were further treated by post-jamming ionic cross-linking with NaHCO 3, which dictated the emulsion gels’ rheological characteristics, enhancing the ink’s viscoelastic properties for high-resolution 3D printing. The customizable emulsion system allows control over porosity from the macro- to the micro-scale and generates complex geometries with desired compositions. By manipulating post-annealing processes and varying concentrations, it is possible to achieve aerogels that feature a remarkably low density (∼2.63 mg/cm 3 ) and adjustable mechanical robustness (elastic modulus of 0.45 MPa). Additionally, this method allows for producing aerogels with flexible or stiff characteristics as required, alongside the capability to tailor specific electromagnetic shielding effectiveness (ranging from 6791 to 19615 dB cm 2 /g), showcasing the technique’s versatility and engineerability.
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 | 0.001 |
| 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".