Colloidosomes as a Protocell Model: Engineering Life‐Like Behaviour through Organic Chemistry
Bibliographic record
Abstract
Abstract The bottom‐up synthesis of self‐assembled micro‐compartmentalised systems that mimic basic characteristics of living cells is rapidly evolving. These types of systems are termed “protocells” and can be chemically programmed to grow and divide, to send and receive chemical signals, to transcript and translate chemical information, to adhere to surfaces or to other protocells, and to perform rudimental enzyme‐mediated metabolic processes. An emerging protocell model that is attracting great attention is the colloidosome. Colloidosomes are microcapsules with a chemically crosslinked, semipermeable membrane composed of amphiphilic nanoparticles. Colloidosomes display important advantages over other protocell models (e. g., vesicles and coacervate micro‐droplets) due to their physical‐chemical properties that can be easily tuned through the careful engineering of their synthetic building blocks. In this review, we deliver an overview of the different types of colloidosomes that have been developed thus far and discuss how organic chemistry contributes to the design and bottom‐up synthesis of novel types of colloidosomes endowed with advanced chemically programmed bio‐inspired functions.
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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 | 0.000 |
| 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".