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Record W2007830735 · doi:10.1021/ct600334w

The Hydrated Electron as a Pseudo-Atom in Cavity-Bound Water Clusters

2007· article· en· W2007830735 on OpenAlexaff
Alexis Taylor, Chérif F. Matta, Russell J. Boyd

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsMount Saint Vincent UniversityDalhousie University
Fundersnot available
KeywordsCluster (spacecraft)ElectronAtomic physicsAtom (system on chip)Ab initioHydrogenSolvated electronChemistryPopulationElectron localization functionHydrogen atomAb initio quantum chemistry methodsMoleculeChemical physicsPhysicsPhysical chemistryGroup (periodic table)Quantum mechanicsRadiolysis

Abstract

fetched live from OpenAlex

Anionic water clusters, (H2O)n(-), of various sizes, n = 1-8, have been investigated using high-level ab initio calculations and the quantum theory of atoms in molecules, which provides a topological analysis of the electron density. The results of the current study indicate that the distribution of the excess electron is dependent on the geometry of the cluster. Non-nuclear attractors (NNAs), with associated pseudo-atomic basins and populations, are observed only in the highly symmetric clusters in which several non-hydrogen-bonded (NHB) hydrogen atoms are oriented toward a central cavity. For the latter cases, the non-nuclear attractor can be considered a pseudo-atom, possessing a significant portion of the excess electron within the cavity, consistent with the cavity-bound model of the solvated electron. In some cases, the population of the NNA is more than 0.2 electrons, and it contributes in excess of 20 kJ/mol to the energy of the system. Furthermore, the less symmetric systems, which tend to orient the NHB hydrogen atoms away from the center of the cluster, tend to delocalize the excess electron to a greater extent over several atoms at the surface of the cluster, consistent with the surface-bound model of the excess electron.

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)0.001
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 score0.258
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.274
Teacher spread0.269 · 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

Citations36
Published2007
Admission routes1
Has abstractyes

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