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Record W4401921802 · doi:10.1039/d4cs00549j

Chemistry, applications, and future prospects of structured liquids

2024· review· en· W4401921802 on OpenAlexafffund
Ahmadreza Ghaffarkhah, Seyyed Alireza Hashemi, Ali Akbar Isari, Mahyar Panahi‐Sarmad, Feng Jiang, Thomas P. Russell, Orlando J. Rojas, Mohammad Arjmand

Bibliographic record

VenueChemical Society Reviews · 2024
Typereview
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationU.S. Department of Energy
KeywordsReconfigurabilityCharacterization (materials science)Focus (optics)NanotechnologyClass (philosophy)ChemistryComputer scienceMaterials sciencePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Structured liquids are emerging functional soft materials that combine liquid flowability with solid-like structural stability and spatial organization. Here, we delve into the chemistry and underlying principles of structured liquids, ranging from nanoparticle surfactants (NPSs) to supramolecular assemblies and interfacial jamming. We then highlight recent advancements related to the design of intricate all-liquid 3D structures and examine their reconfigurability. Additionally, we demonstrate the versatility of these soft functional materials through innovative applications, such as all-liquid microfluidic devices and liquid microreactors. We envision that in the future, the vast potential of the liquid-liquid interface combined with human creativity will pave the way for innovative platforms, exemplified by current developments like liquid batteries and circuits. Although still in its nascent stages, the field of structured liquids holds immense promise, with future applications across various sectors poised to harness their transformative capabilities.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.747

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 science0.0000.000
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 head0.029
GPT teacher head0.326
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2024
Admission routes2
Has abstractyes

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