Liquid‐Templating Aerogels
Bibliographic record
Abstract
Abstract Modern materials science has witnessed the era of advanced fabrication methods to engineer functionality from the nano‐ to macroscales. Versatile fabrication and additive manufacturing methods are developed, but the ability to design a material for a given application is still limited. Here, a novel strategy that enables target‐oriented manufacturing of ultra‐lightweight aerogels with on‐demand characteristics is introduced. The process relies on controllable liquid templating through interfacial complexation to generate tunable, stimuli‐responsive 3D‐structured (multiphase) filamentous liquid templates. The methodology involves nanoscale chemistry and microscale assembly of nanoparticles (NPs) at liquid–liquid interfaces to produce hierarchical macroscopic aerogels featuring multiscale porosity, ultralow density (3.05–3.41 mg cm−3), and high compressibility (90%) combined with elastic resilience and instant shape recovery. The challenges are overcome facing ultra‐lightweight aerogels, including poor mechanical integrity and the inability to form predefined 3D constructs with on‐demand functionality, for a multitude of applications. The controllable nature of the coined methodology enables tunable electromagnetic interference shielding with high specific shielding effectiveness (39 893 dB cm2 g−1), and one of the highest‐ever reported oil‐absorption capacities (487 times the initial weight of aerogel for chloroform), to be obtained. These properties originate from the engineerable nature of liquid templating, pushing the boundaries of lightweight materials to systematic function design and applications.
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) | 0.001 | 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".