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Record W2771765027 · doi:10.1177/1469066717722156

Applications of the fractional calculus to study the physical theory of ion motion in a quadrupole ion trap

2017· article· en· W2771765027 on OpenAlexaff
Sarkhosh Seddighi Chaharborj, Abbas Moameni

Bibliographic record

VenueEuropean Journal of Mass Spectrometry · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsQuadrupole ion trapIon trapQuadrupoleIonTrap (plumbing)Radio-frequency quadrupoleFractional calculusStability (learning theory)PhysicsAtomic physicsChemistryIon sourceQuantum mechanicsComputer science

Abstract

fetched live from OpenAlex

In this article, fractional calculus has been applied to study the motion of ions in a three-dimensional radio frequency quadrupole ion trap; we have called this arrangement a fractional quadrupole ion trap. The main purpose of the article is to show that by controlling the fractional parameter of a trapped ion, one can gain a more efficient mass separation. In what follows, we will see that with decreasing the fractional parameter, we can achieve a smaller first stability region. Note that a small stability diagram will result in a good and acceptable mass separation. Various methods can be proposed to obtain a desired ion acceleration with a sufficient accuracy for good mass separation, which is similar to the one obtained by a fractional ion trap. Some of these methods are using the effects of a damping force, a magnetic field or both on the confinement of particles in the quadrupole ion trap. The first stability regions are plotted for all of the aforementioned methods, and simulation results are provided to compare them with those for the fractional case.

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)0.001
metaresearch head score (Gemma)0.002
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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