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Effect of External Electric Field upon Lower Alkanols

2014· article· en· W1787720367 on OpenAlexvenueno aff
Józef Mazurkiewicz, Piotr Tomasik

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2014
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsElectric fieldPolarizabilityDipoleChemistryMethanolMoleculeField (mathematics)Computational chemistryAnalytical Chemistry (journal)Chemical physicsMolecular physicsAtomic physicsOrganic chemistryPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Applying the Monte Carlo method, molecules of methanol, ethanol and 1-butanol are brought to temperature of 300 K and under the influence of external electric field of 0.01 a.u their electronic spectra are simulated with HyperChem 8.0 involving ZINDO/S semiempirical method. Particular molecules differently react to the electric field applied as shown by the electronic spectra simulated in the range of 250-2.84 nm. Total energy of the methanol and ethanol molecules turn slightly more negative in the electric field whereas that energy found for 1-butanol more significantly turn to less negative. HyperChem 8.0 software is used together with the AM1 method for optimization of the conformation of the molecules of methanol, ethanol, 1-propanol and 1-butanol. Then polarizability, charge distribution, potential and dipole moment for molecules placed in the external electric field of 0.000, 0.001, 0.01 and 0.05 a.u. are calculated. External field induces a slightly field strength dependent polarizability of the molecules and the electron density redistribution at particular atoms. Total dipole moment (DM) for particular alkanols increases with the strength of the field applied. There is particularly sharp increase in DM at 0.05 a.u. field. Key words: Butanol; Computer simulations; Ethanol; Methanol; Propanol

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)0.002
metaresearch head score (Gemma)0.001
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 score0.258
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0000.001
Scholarly communication0.0000.003
Open science0.0020.000
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 head0.002
GPT teacher head0.288
Teacher spread0.286 · 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
Published2014
Admission routes1
Has abstractyes

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