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Simulations of the Solid, Liquid, and Melting of 1-n-Butyl-4-amino-1,2,4-triazolium Bromide

2005· article· en· W6884478246 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2005
Typearticle
Languageen
FieldEngineering
TopicEnergetic Materials and Combustion
Canadian institutionsnot available
Fundersnot available
KeywordsMelting pointIntermolecular forceMolecular dynamicsIonLiquid stateBromideRadial distribution functionStructure factorAb initio

Abstract

fetched live from OpenAlex

Molecular dynamics simulations are used to study the solid and liquid properties and to predict the melting point of 1-n-propyl-4-amino-1,2,4-triazolium bromide ([patr][Br]) using a force field based on the one developed by Canongia Lopes et al. (J. Phys. Chem. B 2004, 108, 2038) for dialkyl substituted imidazolium salts, which was modified by including terms from the general AMBER force field. Electrostatic charges for the intermolecular interactions were determined from gas-phase ab initio electron structure calculations of the triazolium cation. Simulations of the solid state at 100 K reproduced the experimental density to within 4%. Simulations from 100 K to the melting point and the liquid from 333 to 500 K were performed to determine the temperature dependence of the densities of the two phases. The structures of the solid and liquid phases are characterized with radial distribution functions, which show that there are strong spatial correlations among neighboring ion pairs in liquid [patr][Br]. The dynamic behavior of the ions in the liquid state is also studied by computing velocity autocorrelation functions and the mean-square displacements between the ions. The melting point is determined by simulating void-induced melting. Changes in the density, intermolecular energy, and Lindemann index are used as indicators of the melting transition. The computed melting point is 360 ? 10 K, which is within 10% of the experimental value 333 K.

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 score0.055
Threshold uncertainty score0.184

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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