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New Materials for LEO, GEO and Planetary Environments: Preliminary Results from MISSE-17 Experiment

2025· article· en· W4411567724 on OpenAlexaff
Jacob I. Kleiman, Z. Iskanderova, Richard Ng, Adrian Tang

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsAstrobiologyGeologyEarth scienceGeographyPhysics

Abstract

fetched live from OpenAlex

Abstract ITL developed coatings and surface treatments that include prototypes of multifunctional coatings with charge-dissipative and dust mitigation properties, including Lunar dust Work Function Matching Coating (WFMC) that can be applied to surfaces of various space materials. Selected samples were flown on MISSE-17 Material Experiment, to evaluate their resistance to space environmental factors like thermal cycling, vacuum, VUV and atomic oxygen. Two thermal control materials, i. e. a 1 mil KaptonHN with a doped diamond-like coating (DLC) on it and an aluminized 1 mil KaptonHN with a DLC film on it were selected for the ram direction. For the wake position, a doped DLC on a 1 mil aluminized KaptonHN and a doped DLC on a silver-coated Teflon FEP were selected, to check the influence of both the thermal cycling and UV exposure. In addition, two samples of Lunar dust Work Function Matching Coating (WFMC) on Kapton HN (WFMC/Kapton) were prepared and added, one in ram direction and one in wake direction. The MISSE-17 Experiment was launched from Kennedy Space Centre on March 15, 2023 and returned on SpX-29 flight in December 2023. Samples faced atomic oxygen fluence of ~8.0E+20 atoms/cm 2 in ram direction and 3.76E+18 atoms/cm 2 in wake direction over 145 days and 157 days, respectively. The results discussed in this paper include weight and surface morphology changes, the thermal optical properties and surface analysis.

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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.203
Teacher spread0.191 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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