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Record W1140057221 · doi:10.1520/stp157420130107

An Investigation on Bio-Based Polyurethane Foam Insulation for Building Construction

2014· book-chapter· en· W1140057221 on OpenAlexaff
Phalguni Mukhopadhyaya, Minh‐Tan Ton‐That, Tri-Dung Ngo, Nathalie Legros, J-F. Masson, S. Bundalo-Perc, David van Reenen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPolyurethaneFoam concreteComposite materialMaterials scienceArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Bio-based renewable construction material is an old concept. Wood, straw, and other products of nature have been used for millennia around the world. However, in modern construction, the ratio of bio-based to non-renewable building materials is very low. This is primarily due to performance requirements. Purely bio-based construction materials sometimes have performance levels not quite equal to modern construction materials. The biggest challenge for the development of bio-based construction materials is to bring environmental friendliness and high engineering performance together in a single material. This paper presents results from a laboratory screening study on the development and the assessment of rigid partially bio-based polyurethane (PU) foams (seven different formulations) that contain lignin-based polyols, up to 20 % of polyol weight. The formulation strategy, morphology, and hygrothermal performance of rigid bio-based PU foams are presented and compared with the traditional petroleum-based reference PU foam. This study demonstrates that partially bio-based rigid PU foam with appropriate formulation can have characteristics that may be suitable for the construction industry applications. For market acceptance, further investigation is needed on long-term performance and durability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2014
Admission routes1
Has abstractyes

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