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Record W764103238 · doi:10.1063/1.4918517

Functionalization of polymer powders for SLS-processes using an atmospheric plasma jet in a fluidized bed reactor

2015· article· en· W764103238 on OpenAlexaff
Marius Sachs, Adeliene Schmitt, Jochen Schmidt, Wolfgang Peukert, Karl‐Ernst Wirth

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsInstitute of Particle Physics
FundersDeutsche Forschungsgemeinschaft
KeywordsSurface modificationMaterials sciencePolymerWettingFluidized bedChemical engineeringAtmospheric-pressure plasmaSelective laser sinteringJet (fluid)Composite materialAdhesionPlasmaSinteringOrganic chemistryChemistryAerospace engineering

Abstract

fetched live from OpenAlex

Recently additive manufacturing processes such as selective laser sintering (SLS) of polymers have gained more importance for industrial applications [1]. Tailor-made modification of polymers is essential in order to make these processes more efficient and to cover the industrial demands. The so far used polymer materials show weak performance regarding the mechanical stability of processed parts. To overcome this limitation, a new route to functionalize the surface of commercially available polymer particles (PA12; PE-HD; PP) using an atmospheric plasma jet in combination with a fluidized bed reactor has been investigated. Consequently, an improvement of adhesion and wettability [2] of the polymer surface without restraining the bulk properties of the powder is achieved. The atmospheric plasma jet process can provide reactive species at moderate temperatures which are suitable for polymer material. The functionalization of the polymer powders improves the quality of the devices build in a SLS-process.

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)0.001
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 score0.048
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.091
GPT teacher head0.290
Teacher spread0.199 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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