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Record W4417514218 · doi:10.26434/chemrxiv-2025-b6k8m

Temporal Control over Complex, Simple, and Multiphase Coacervates using Ureolysis and Ammonium Carbonate Decomposition

2025· preprint· en· W4417514218 on OpenAlexaff
Shana Shirin Valapra, Tsvetomir Ivanov, Lucas Caire da Silva, Guillermo Monreal Santiago

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoacervateDissolutionDecompositionAmmoniumAmmonium carbonateIonic strengthStabilizer (aeronautics)

Abstract

fetched live from OpenAlex

Active compartmentalization is a fundamental property of life, as well as a key element in cell regulation. In order to build synthetic systems with emergent cell-like properties, we need to develop active compartments that form, disappear, and change properties autonomously over time. Here, we report a new strategy to induce temporal changes in coacervates using the urea-urease reaction. This reaction triggers the dissolution of complex coacervates after a controllable delay, as it increases ionic strength through the production of ammonium carbonate. This delayed dissolution can be directly used for a broad range of complex coacervates without any synthetic effort. Furthermore, ammonium carbonate can also decompose over time, evaporating from the solution. This combination of enzymatic synthesis and spontaneous decomposition of salt leads to transient coacervate dissolution, which can be used for the controlled release of cargo and for the modulation of a compartmentalized reaction. Cycles of dissolution/reformation can be repeated multiple times without any waste generation, although the number of cycles is limited by the denaturation of urease. Finally, by combining polyelectrolytes with a protected dipeptide, ureolysis can trigger the formation of multiphase coacervates, as well as for the sequential formation, dissolution, and aggregation of simple and complex coacervates.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.338
Teacher spread0.302 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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