Additive manufacturing of aerogels: Recent advancements and innovations
Bibliographic record
Abstract
Aerogels are a unique class of nanostructured materials renowned for their exceptional properties, including ultralow density, outstanding thermal insulation, and low dielectric constant. Traditionally, aerogels have been fabricated through sol-gel processing in various geometries; however, the emergence of additive manufacturing (AM) has revolutionized their processing by introducing unprecedented design flexibility. AM offers significant advantages such as customized geometries, tailored structures, and enhanced scalability for practical and industrial applications. This review provides an in-depth analysis of recent advancements in the AM of aerogels, beginning with a discussion on the fundamentals of aerogel materials, including their sol-gel processing, drying techniques, and formability. Both organic and inorganic aerogels are explored, emphasizing their unique properties and potential applications. The review then delves into the concept of AM, categorizing its various approaches, and examines cutting-edge AM techniques for aerogel fabrication, such as direct ink writing (DIW), direct cryo writing (DCW), inkjet printing, and vat polymerization (VP). Furthermore, post-processing strategies to enhance the structural and functional properties of additively manufactured aerogels are discussed, highlighting their transformative potential across diverse fields.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".