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Record W4221002067 · doi:10.1088/2051-672x/ac604d

Polydimethylsiloxane brushes and the search for extraterrestrial life

2022· article· en· W4221002067 on OpenAlexafffund
Kevin Golovin, Behrooz Khatir, Letícia Recla, Zahra Azimi Dijvejin, Xiaoxiao Zhao

Bibliographic record

VenueSurface Topography Metrology and Properties · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Toronto
FundersCanada Foundation for Innovation
KeywordsPolydimethylsiloxaneMaterials scienceCoatingComposite materialSurface finishSurface roughnessTribologySiliconeDeposition (geology)Geology

Abstract

fetched live from OpenAlex

Abstract The low temperature and high pressure tribological properties of polydimethylsiloxane brushes with ice are explored to demonstrate their feasibility as an exterior coating for an off-world cryobot. Successful deposition of the brushes on silicon and glass was confirmed with a contact angle hysteresis < 2° and a surface roughness below 1 nm. The friction factor of the brushes roughly doubled when the temperature was lowered from +20 °C to −20 °C, but it decreased by 55% when the normal force was increased from 0.5 N to 16 N. When sheared, adhered ice slid on the brushes at a shear stress around 21 kPa, and this did not increase with an additional normal pressure of up to 98 kPa. A glass rod coated with the brushes served as a cryobot surrogate and was frozen within cores of −10 °C ice 1–3 cm high. Weight attached to the rod enabled it to cleanly slide completely through the ice cores at the ambient −10 °C, i.e. without melting the ice. Together, these results indicate that polydimethylsiloxane brushes may be a feasible exterior coating for an off-world cryobot that would enable it to slide through the frozen surface of potentially life-harboring bodies such as Europa or Enceladus, avoiding the need to melt the entire cryobot’s exterior.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.043
GPT teacher head0.245
Teacher spread0.202 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

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