A Journey from Structured Emulsion Templates to Multifunctional Aerogels
Bibliographic record
Abstract
Abstract Interfacial jamming and assembly, facilitated by nanoparticle surfactant (NPS) complexation, demonstrate a remarkable efficacy in stabilizing multiphase systems, evident in structured liquid streams and structured Pickering emulsions. However, the utilization of structured liquid templates to tune multiple porosity levels of ultra‐flyweight aerogels is barely discussed. In this study, a structured Pickering emulsion is prepared through mixing an aqueous dispersion of graphene oxide (GO) with an organic (hexane) solution containing an active ligand. The emulsion is jetted into the same organic phase, resulting in “dual jamming”. This process produced worm‐like aerogels with porosity that can be precisely tailored at four different levels: i) voids between filaments, ii) cavities produced by evaporation of trapped hexane droplets, iii) pores generated from sublimation of water in the bulk of GO emulsion, and iv) microscopic regions trapped between GO flakes or fractures/holes within GO nanosheets. These aerogels exhibit ultra‐low density (1.67–2.3 mg cm−3), high compressibility, and shape recovery. The multi‐scale porosity, created by structural design, endows aerogels with a record‐level fluid sorption capacity (e.g., 615 g g−1 for chloroform). Additionally, the aerogels demonstrate an absorption‐dominant electromagnetic interference (EMI) shielding mechanism, achieving a remarkable specific EMI shielding (SSE/t) of 67 178 dB cm2 g−1.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".