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Record W4407039710 · doi:10.3390/mi16020178

Development of Visibly Opaque Polyolefin Sheets While Preserving Infrared-Light Transparency

2025· article· en· W4407039710 on OpenAlexafffund
Md. Saiful Hoque, Mehnab Ali, Xiaoruo Sun, Asad Asad, Patricia I. Dolez, James D. Hogan, Dan Sameoto

Bibliographic record

VenueMicromachines · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesPublic Works and Government Services Canada
KeywordsPolyolefinOpacityTransparency (behavior)InfraredMaterials scienceOptoelectronicsNanotechnologyOpticsComputer sciencePhysicsComputer security

Abstract

fetched live from OpenAlex

, FeO yellow, FeO light brown, and FeO dark brown-were each mixed with polyethylene (PE) wax in a 1:1 ratio and blended with LLDPE at concentrations of 1, 3, and 5 wt%. Tensile strength tests showed minimal changes at lower pigment concentrations, with values near that of pure LLDPE (14 MPa), and slight reductions at 5 wt%. IR transparency tests, conducted using both direct and reflected heat sources, showed that white-pigmented sheets maintained over 85% transparency, while colored pigments exhibited slightly reduced IR transmittance, ranging from 70% to 91%. Fourier Transform Infrared Spectroscopy (FTIR) analysis confirmed that the critical IR transparency range of 8-12 μm remained unaffected with both pure and pigmented sheets. On the other hand, ultraviolet-visible (UV-VIS) testing showed that white-pigmented sheets experienced enhanced visible-light absorption with increasing pigment concentration and thickness, while color-pigmented sheets exhibited high opacity. Additionally, micro-structuring was performed on the LLDPE sheets to further modify their IR properties, which resulted in effective scattering of IR radiation. These findings highlight the potential of pigmented LLDPE sheets for applications requiring both visual opacity and IR transparency, such as thermal management and camouflage, as well as applications requiring tunable IR properties.

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 score0.023
Threshold uncertainty score0.592

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.240
Teacher spread0.228 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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