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Record W3097100235 · doi:10.1063/12.0000843

Triboluminescent sensor for detection of impacts of submillimeter explosion fragments

2020· article· en· W3097100235 on OpenAlexaff
J. Geoffrey Chase, Samuel Goroshin, David L. Frost

Bibliographic record

VenueAIP conference proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceExplosive materialParticle (ecology)Particle sizeOptoelectronicsMicroscale chemistryRange (aeronautics)AluminiumOpticsComposite materialPhysicsChemistry

Abstract

fetched live from OpenAlex

Fine metallic fragments in the millimeter and submillimeter size ranges may result from high-velocity impact with an obstacle or from explosive dispersal of particles. The size, velocity, and spatial distribution of the fine fast-flying particles are difficult to determine with available diagnostic systems. A novel particle impact detector based on a high-sensitivity tribolumi- nescent (TL) screen that is described in this paper can, in principle, fill this niche. The light-generating impact screen utilizes a TL manganese-doped zinc sulfide (ZnS:Mn) powder. The polycrystalline bulk TL material is synthesized in-house using the self-propagating high-temperature synthesis (SHS) reaction between sulfur and zinc. The multilayered sensor screen is comprised of aluminum foil, a layer of coarse polycrystalline particles, and transparent plastic backing. The sensor is optically coupled to a photomultiplier via a fiber optic taper. The operation of the system is demonstrated by impacting the screen with particles in the 0.1–0.6 mm size range accelerated by a helium-driven light-gas gun to speeds in the 0.3–0.8 kms−1 range. The sensor is shown to resolve particle impacts even in dense flows, allowing correlation of the signal amplitude with particle kinetic energy, and can resolve the bow shock as a precursor signature prior to particle impact.

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 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 score0.409
Threshold uncertainty score0.556

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 head0.030
GPT teacher head0.238
Teacher spread0.208 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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