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Record W1964135474 · doi:10.1088/0953-4075/45/4/045205

Multiple ionization and complete fragmentation of OCS by impact with highly charged ions Ar<sup>4+</sup>and Ar<sup>8+</sup>at 15 keV q<sup>−1</sup>

2012· article· en· W1964135474 on OpenAlexafffund
B. Wales, Tomonori Motojima, Jun Matsumoto, ZiJian Long, Wing‐Ki Liu, H. Shiromaru, Joseph Sanderson

Bibliographic record

VenueJournal of Physics B Atomic Molecular and Optical Physics · 2012
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsFragmentation (computing)IonAtomic physicsIonizationCharged particleNuclear physics

Abstract

fetched live from OpenAlex

We have used time- and position-sensitive detection in a coincidence arrangement to study the multiple ionization of OCS in collisions with Ar4+ and Ar8+ at 15 keV q−1 followed by complete breakup of the molecule OCS—Oa++ Cb+ + Sc+. We have compared our results with theoretical values derived by simulating the breakup, from ground state distributions of bond lengths and bond angle assuming a point-like ion and purely Coulombic potential. This comparison shows that in general the experimental distributions of energy release are broader and peak at lower energy than calculated. Better agreement between measurement and calculation is however found with increasing the final charge state. Furthermore, the amount of induced bending is considerably less for the high charge states. However, even where total energy release is close to Coulombic (6+) individual fragment ion energy distributions differ from the expected values because of the degree of bending. Using Newton and Dalitz plots, we are able to identify the extent of concerted and stepwise processes. Our results indicate a higher degree of asymmetric bond processes at a low charge state (3+) where small amounts (<7%) of the stepwise processes are also measurable.

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 categoriesMeta-epidemiology (narrow)
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 score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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.

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

Citations31
Published2012
Admission routes2
Has abstractyes

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