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Record W4393376951 · doi:10.1063/5.0195101

Electric charging effects on insulating surfaces in cryogenic liquids

2024· article· en· W4393376951 on OpenAlexaff
W. Korsch, Mark Broering, Ashok Timsina, K. K. H. Leung, Joshua Abney, Dmitry Budker, B. W. Filippone, Jiachen He, Suman Kandu, Mark McCrea, M. Roy, Christopher Swank, Weijun Yao

Bibliographic record

VenueReview of Scientific Instruments · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum, superfluid, helium dynamics
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMaterials scienceDeuteriumElectric fieldIonDipoleUltracold neutronsLiquid nitrogenDielectricPolystyreneCryostatLiquid airElectronAdsorptionCryogenicsAnalytical Chemistry (journal)FemtosecondAtomic physicsNeutronLaserOptoelectronicsChemistryOpticsComposite materialCondensed matter physicsPhysical chemistry

Abstract

fetched live from OpenAlex

This paper presents a new technique to study the adsorption and desorption of ions and electrons on insulating surfaces in the presence of strong electric fields in cryoliquids. The experimental design consists of a compact cryostat coupled with a sensitive electro-optical Kerr device to monitor the stability of the electric fields. The behavior of nitrogen and helium ions on a poly(methyl methacrylate) (PMMA) surface was compared to a PMMA surface coated with a mixture of deuterated polystyrene and deuterated polybutadiene. Ion accumulation and removal on these surfaces were unambiguously observed. Within the precision of the data, both surfaces behave similarly for the physisorbed ions. The setup was also used to measure the (quasi-)static dielectric constant of PMMA at T ≈ 70 K. The impact of the ion adsorption on the search for a neutron permanent electric dipole moment in a cryogenic environment, such as the nEDM@SNS experiment, is discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.009
GPT teacher head0.266
Teacher spread0.257 · 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 designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

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