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Record W4200474921 · doi:10.3390/app12010208

Sodium Iodide as a Contrast Agent for X-ray Micro-CT of a Wood Plastic Composite

2021· article· en· W4200474921 on OpenAlexaff
Dengcheng Feng, Michael L. Turner, Philip D. Evans

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrostructureMaterials scienceComposite materialWood-plastic compositeComposite numberAqueous solutionIodideSodiumSodium iodideChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

The properties of wood plastic composites (WPCs) depend on their microstructure, particularly the level and geometry of wood reinforcement in the composite. We hypothesize that impregnating a WPC with a radiocontrast agent will increase the contrast between wood and plastic, allowing better visualization of its microstructure and numerical analysis of the geometry of its wood reinforcement. A commercial WPC was scanned using X-ray micro-CT, impregnated with aqueous sodium iodide, and then rescanned. CT data from both scans were visualized, and we analyzed the geometry of wood reinforcement and levels of wood, plastic, zinc borate (ZB), and voids in the WPC. ZB occurred mainly as discrete particles between wood flakes, and interfacial voids formed a network of cracks within the WPC. Sodium iodide labeling made it possible to clearly visualize wood and plastic in the WPC and quantify levels of different phases and the geometry of wood particles. However, sodium iodide was not an ideal contrast agent because it swelled wood particles, closed interfacial voids, and partially dissolved ZB particles. We suggest methods of overcoming these limitations and conclude that advances in labeling are necessary to improve our understanding of the relationship between the microstructure of WPCs and their 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 score0.016
Threshold uncertainty score0.636

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 studies0.0000.001
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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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