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Effect of Extrusion Parameters on Properties of Powder Coatings Determined by Infrared Spectroscopy

2017· article· en· W2761979378 on OpenAlexvenueno aff
Alexander Lepschi, Jakob Kilgus, Markus Brandstetter, Jürgen Miethlinger

Bibliographic record

VenueJournal of Coating Science and Technology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPlastics extrusionExtrusionMaterials scienceDifferential scanning calorimetryCoatingCompoundingReactive extrusionComposite materialInfrared spectroscopyThermocoupleChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In polymer extrusion, compounding is a continuous mixing process that is also used to produce highly reactive powder coatings. A premixed batch of powder coating is added to the feeding section and extruded, preferably by a co-rotating twin-screw extruder. One essential parameter in the processing of highly reactive materials is the melt temperature: If it is too high, pre-reactions occur during the extrusion process, which may cause high rejection rates. We studied the melt temperature of an epoxy/carboxyl-based powder coating using a retractable thermocouple at 3 different axial positions along the barrel of a ZSK34 co-rotating twin-screw extruder. The influence of different processing conditions on the reactivity of a highly reactive powder coating was examined by infrared spectroscopy and differential scanning calorimetry. Furthermore, the specific energy input and the color change in the finished powder coating at different processing points were investigated. Multivariate data analysis was used to correlate mid-infrared spectra, melt temperatures, specific energy inputs, enthalpies of reaction and changes in color

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)0.002
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 score0.005
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.257
Teacher spread0.248 · 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
Published2017
Admission routes1
Has abstractyes

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