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Record W6902764239 · doi:10.7538/zpxb.2022.0045

Design and Simulation of a Double Potential Well Flat Ion Trap

2023· article· en· W6902764239 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsIONICS Mass Spectrometry (Canada)
Fundersnot available
KeywordsIonIon trapIon trappingTrap (plumbing)Flux (metallurgy)Ion gun

Abstract

fetched live from OpenAlex

A double potential well flat ion trap was designed as an ion accumulating and focusing device for multi-reflection time-of-flight mass analyzer. Two potential wells of the trap were severally used for accumulation of injected ions, controlled cooling and ejection of trapped ions, which had features to accumulate ions continuously and cool ion for almost one period. While a flat ion trap with only one potential well is a traditional and excellent ion accumulating and focusing device, which has an intermittent ion accumulation and an ion cooling around half period. Compared with the single potential well flat ion trap, the double potential well flat ion trap had better performance on ion accumulation and focusing in principle. Four steps to run the trap were simulated and researched by an ion optics simulator of SIMION 8.1. The first step was ion accumulation, ions were successively injected into the trap, and then stored and accumulated in the first potential well. The second step was ion transfer, the trapped ions were transferred from the first potential well to the second potential well. The third step was ion cooling, the trapped ions were confined in the second potential well, and were simultaneously cooled by collisions with 0.5 Pa He. The fourth step was ion ejection, the trapped ions were ejected from the trap and focused into the next device. Ion optics simulations indicated that the trap had a period from 1 ms to above 10 ms, a total ion transmission efficiency of 83%, and an ion flux of at most 1.6×106 ions. In addition, ion packets ejected from the trap had thermalized with radial diameters of 1.0 and 1.0 mm, angular standard deviations of 24 mrad and 16 mrad, and an energy standard deviation of 15 eV. This trap can be coupled with and provide high-flux and full-focusing ion packets for multi-reflection time-of-flight mass analyzers, due to an excellent suitability between the operation of the analyzer and the period, the ion transmission efficiency, the ion flux, and ejected ion packets of the trap.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.199
GPT teacher head0.515
Teacher spread0.316 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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