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Record W1964268008 · doi:10.1063/1.4840695

Understanding tetrahedral liquids through patchy colloids

2013· article· en· W1964268008 on OpenAlexaff
Ivan Saika‐Voivod, Frank Smallenburg, Francesco Sciortino

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTetrahedronMaterials scienceColloidStructure factorCrystallizationCompressibilityMonte Carlo methodChemical physicsMolecular dynamicsRange (aeronautics)Radial distribution functionRing (chemistry)Colloidal crystalMolecular physicsNanotechnologyCrystallographyComposite materialMechanicsPhysicsComputational chemistryChemistryThermodynamicsPhysical chemistryMathematics

Abstract

fetched live from OpenAlex

We investigate the structural properties of a simple model for tetrahedral patchy colloids in which the patch width and the patch range can be tuned independently. For wide bond angles, a fully bonded network can be generated by standard Monte Carlo or molecular dynamics simulations of the model, providing a good method for generating defect-free random tetrahedral networks. This offers the possibility of focusing on the role of the patch angular width on the structure of the fully bonded network. The analysis of the fully bonded configurations as a function of the bonding angle shows how the bonding angle controls the system compressibility, the strength of the pre-peak in the structure factor, and ring size distribution. Comparison with models of liquid water and silica allows us to find the best mapping between these continuous potentials and the colloidal one. Building on previous studies focused on the connection between angular range and crystallization, the mapping makes it possible to shed new light on the glass-forming ability of network-forming tetrahedral liquids.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.081
GPT teacher head0.272
Teacher spread0.191 · 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 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

Citations43
Published2013
Admission routes1
Has abstractyes

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