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Record W4386253629 · doi:10.1002/cben.202300014

Recent Developments in Biobased Foams and Foam Composites for Construction Applications

2023· article· en· W4386253629 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemBioEng Reviews · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCelluloseFire retardantCommercializationNanofiberComposite materialBusinessChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract A surge of research into renewable foams has yielded an array of high‐performance polymeric materials, many of which exhibit promising properties for next generation thermal insulating materials. Biobased materials are of particular interest, due to growing concerns towards enhancing the circular economy while reducing fossil fuel dependency in the construction industry. This review outlines recent developments in biobased foams based on biobased polyurethanes (BPU), biobased phenol formaldehyde (BPF) and cellulose nanofibers (CNF) foams. These three areas of polymers are of particular interest due to their early stage of market adoption, yet significant industrial potential. As our focus is on construction materials, we will review their thermal, mechanical, and fire‐retardant performance, their synthesis/fabrication methods and future prospects. Improving the scalability, reproducibility and cost‐effectiveness of their production is vital for successful commercialization adoption.

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.

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 score0.488
Threshold uncertainty score0.409

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 head0.045
GPT teacher head0.305
Teacher spread0.260 · 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