Impact Driven Liquid Encapsulation: Promises, Development, and Future Prospects
Bibliographic record
Abstract
Abstract Encapsulation creates a protective outer layer(s) around a core cargo, which safeguards the cargo in aggressive surroundings. It also serves as a platform to impart various desired characteristics to the core cargo, including shell‐functionalization and targeted release characteristics. Encapsulation can be broadly classified into three categories: physical, chemical, and physicochemical techniques. This perspective focuses on an emerging class of impact‐driven physical encapsulation techniques, which offers several lucrative prospects compared to conventional encapsulation methods, including straightforward execution and ultrafast yet controlled wrapping. Two different categories of impact‐driven methods for achieving stable, ultrafast encapsulation of various core liquid analytes with one or more wrapping layers are discussed, namely, elastocapillary wrapping with ultrathin sheet(s) and a liquid–liquid encapsulation framework, where thin liquid film(s) are used to wrap liquid analytes, with an emphasis on the latter. The promising prospects of both approaches are discussed, recent developments are outlined, and areas of future research that can lead to a truly versatile and comprehensive encapsulation platform applicable to a broad range of practical applications are highlighted.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".