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Record W3033404995 · doi:10.3390/sym12060920

Theoretical Insights into the Structure of the Aminotris(Methylenephosphonic Acid) (ATMP) Anion: A Possible Partner for Conducting Ionic Media

2020· article· en· W3033404995 on OpenAlexafffund
Henry Adenusi, Gregory A. Chass, Enrico Bodo

Bibliographic record

VenueSymmetry · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMcMaster University
FundersUniversity of Hong KongMcMaster University
KeywordsProtonChemical physicsProtonationIonIonic bondingMolecular dynamicsCharge (physics)MoleculeElectrical conductorPhase (matter)Materials scienceComputational chemistryTopology (electrical circuits)ChemistryNanotechnologyPhysicsOrganic chemistryQuantum mechanics

Abstract

fetched live from OpenAlex

We present a computational characterisation of Aminotris(methylenephosphonic acid) (ATMP) and its potential use as an anionic partner for conductive ionic liquids (ILs). We argue that for an IL to be a good candidate for a conducting medium, two conditions must be fulfilled: (i) the charge must be transported by light carriers; and (ii) the system must maintain a high degree of ionisation. The result trends presented herein show that there are molecular ion combinations that do comply with these two criteria, regardless of the specific system used. ATMP is a symmetric molecule with a total of six protons. In the bulk phase, breaking the symmetry of the fully protonated state and creating singly and doubly charged anions induces proton transfer mechanisms. To demonstrate this, we used molecular dynamics (MD) simulations employing a variable topology approach based on the reasonably reliable semiempirical density functional tight binding (DFTB) evaluation of the atomic forces. We show that, by choosing common and economical starting compounds, we can devise a viable prototype for a highly conductive medium where charge transfer is achieved by proton motion.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.331

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 science0.0010.000
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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

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