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Record W7106228419 · doi:10.1016/j.mne.2025.100337

Ionic liquid ion sources for focused ion beam applications: A review

2025· article· en· W7106228419 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMicro and Nano Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIon beamIonic liquidIonFocused ion beamCommercializationIonic bondingIon source

Abstract

fetched live from OpenAlex

Focused ion beam (FIB) technology has transformed materials science by enabling precise micro- and nano-scale modifications through ion beam interactions. Originally developed for semiconductor doping and device fabrication, FIBs use different ionization sources such as liquid metals (e.g., gallium), gas field ionization, and plasma sources. Recent advancements include the use of Ionic Liquid Ion Sources (ILIS), which promise enhanced capabilities for materials research and applications. Recent progress in the Ionic Liquid Ion Sources- Focused ion beam (ILIS-FIB) technology is presented in this overview paper. ILIS-FIB systems operate similarly to conventional systems but employ ionic liquids (ILs) as ion sources, ionizing IL molecules at the emitter tip with applied voltage and using standard focusing components to refine the ion beam. Challenges which are reviewed in this article, include maintaining pure ionic emission for stable operation, necessitating optimization of tip emitting properties, IL characteristics, and voltage settings. It was reviewed in this paper that, ILIS-FIB systems use room-temperature ILs with low melting points, low vapor pressures, and customizable chemical compositions to ensure pure ion emission and improve beam performance for emerging applications. Despite challenges in beam composition and commercial readiness, ILIS-FIB research focuses on developing mathematical models to predict beam stability and performance, advancing theoretical groundwork for refinement and eventual commercialization of ILIS-based FIB technologies in materials science. This overview can shed light on the understanding of ionic liquid ion sources for Focused Ion Beam applications. • Reviews recent advancements in Ionic Liquid Ion Source–Focused Ion Beam (ILIS-FIB). • ILIS-FIB operational principles. • Advantages of ILIS, such as low vapor pressure. • Identifies key challenges, including stable beam operation and optimization of emitter tip.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.745
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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