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Record W2322358677 · doi:10.1021/jp405978d

Bonding in Sodium Chloride Nanotubes: A New Analysis via Madelung Constants and Cohesive Energies

2013· article· en· W2322358677 on OpenAlexaff
Mark D. Baker, A. D. Baker, Christopher R. H. Hanusa, K. Paltoo, E. Danzig, Jane Belanger

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Guelph
FundersCity University of New York
KeywordsIonic bondingDensity functional theoryMadelung constantMaterials scienceChemical physicsIonic potentialSodiumComputational chemistryThermodynamicsPhysical chemistryChemistryIonCrystallographyPhysicsLattice energyOrganic chemistryCrystal structureMetallurgy

Abstract

fetched live from OpenAlex

In this paper, the bonding, relative stabilities, and local ionic charges occurring in sodium chloride nanotubes are discussed. A new methodology is introduced which employs a linear relationship between nanotube cohesive energies determined via density functional theory (DFT) and weighted-average Madelung constants (MC(wa)). The slope of the linear plots reveals ionic charges and gives insights into ionic and other contributions to the bonding. Comparisons between ionic and cohesive bonding energies indicate that, as the nanotubes become longer, ionic bonding provides the principal contribution to the increased stabilization. Furthermore, comparisons between the total cohesive and electrostatic energies are used to calculate the percent ionicities. The nanotubes discussed in this paper show that percent ionicity ranges from 47 to 58%. Increasing lengths and decreasing widths of the tubes favor higher ionic character. A linear relationship linking the average ionic coordination number and MC(wa) is also presented for the first time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations12
Published2013
Admission routes1
Has abstractyes

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