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Record W4404718759 · doi:10.1002/marc.202400626

Coacervate Droplets as Biomimetic Models for Designing Cell‐Like Microreactors

2024· review· en· W4404718759 on OpenAlexafffund
Tsvetomir Ivanov, Thao P. Doan‐Nguyen, Mohammed A. Belahouane, Zhen Dai, Shoupeng Cao, Katharina Landfester, Lucas Caire da Silva

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typereview
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsMcGill University
FundersMax-Planck-GesellschaftBundesministerium für Bildung und ForschungFonds de recherche du Québec – Nature et technologiesAlexander von Humboldt-Stiftung
KeywordsCoacervateMicroreactorNanotechnologyArtificial cellChemistryMaterials scienceChromatographyOrganic chemistryBiochemistryMembrane

Abstract

fetched live from OpenAlex

Coacervates are versatile compartments formed by liquid-liquid phase separation. Their dynamic behavior and molecularly crowded microenvironment make them ideal materials for creating cell-like systems such as synthetic cells and microreactors. Recently, combinations of synthetic and natural molecules have been exploited via simple or complex coacervation to create compartments that can be used to build hierarchical chemical systems with life-like properties. This review highlights recent advances in the design of coacervate compartments and their application as biomimetic compartments for the design of cell-like chemical reactors and cell mimicking systems. It first explores the variety of materials used for coacervation and the influence of their chemical structure on their controlled dynamic behavior. Then, the applications of coacervates as cell-like systems are reviewed, focusing on how they can be used as cell-like microreactors through their ability to sequester molecules and provide a distinct and regulatory microenvironment for chemical reactions in aqueous media.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.096
GPT teacher head0.357
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2024
Admission routes2
Has abstractyes

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