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Record W4416026016 · doi:10.48550/arxiv.2511.04423

Dust Collisions in Protoplanetary Disks: Atomic Simulations of the Surface Free Energy

2025· preprint· W4416026016 on OpenAlexfundno aff
Liam S. Morrissey, D. S. Ebel, Linn E. J. Eriksson, Anastasis Georgiou, Ziyu Huang, Mordecai‐Mark Mac Low, Thomas Pfeil

Bibliographic record

VenueArXiv.org · 2025
Typepreprint
Language
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsSilicatePlanetesimalMolecular dynamicsFragmentation (computing)AstrochemistryMolecular cloudDusty plasmaSticking coefficient

Abstract

fetched live from OpenAlex

Coagulation of dust particles in protoplanetary disks is the first step on the journey to the formation of planets. The surface free energy (SFE) of the dust particles determines the effectiveness of particles sticking to each other after collision, as well as the critical collision velocity above which fragmentation will occur. Studies of SFE have focused on the simplest silicate, silica, usually at standard temperature and pressure. However, protoplanetary dust grains have a wide variety of mineralogical compositions, temperatures, and a low-pressure environment lacking in water vapor. We perform molecular dynamics simulations using a ReaxFF-type potential of the SFE of silica, albite, and anorthite at temperatures ranging from 30 to 700 K in a true vacuum. We find that the SFE drops by tens of percent with increasing temperature or shifting to more complex silicate compositions. More dramatically, we find that the values of the SFE in a vacuum are two orders of magnitude higher than those usually measured in terrestrial laboratories. Our results confirm previous work that suggests that hydroxylation by monolayers of water produces this reduction in SFE in experiments. The coagulation of dust grains thus appears to depend critically on the cleanliness of their surfaces, as well as their temperature and composition.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.022
GPT teacher head0.254
Teacher spread0.233 · 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 designObservational
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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