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Record W4391071345 · doi:10.1016/j.heliyon.2024.e24510

Reaction to fire, thermal, and mechanical properties of materials based on recycled paper granules bound with starch and clay mortar

2024· article· en· W4391071345 on OpenAlexfundno aff
Lydie Marcelle Thieblesson, Răzvan Calotă, Nastasia Saca, Adrian Simion, Ilinca Năstase, Alina Girip

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldEngineering
TopicRecycled Aggregate Concrete Performance
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsMortarThermal conductivityMaterials scienceFlexural strengthComposite materialFiller (materials)Fire retardantEnvironmentally friendlyStarchFire performanceThermalFire resistanceChemistry

Abstract

fetched live from OpenAlex

The objective of this work is to produce an ecologically friendly material for use in Ivory Coast's construction sector in the future. These materials should have good thermal qualities and be flame resistant in addition to helping to achieve interior comfort. The fundamental components under consideration are freely accessible in Ivory Coast and include clay mortar as a fire retardant, potato starch as a binder, and recycled paper granules as a filler. The suggested ecologically friendly material's manufacturing process is fully described in detail. After conditioning, the team created multiple samples, taking into account that each test that the materials are put through requires various probe sizes for the thermal conductivity test, the reaction to fire test, and the flexural strength test. The best result regarding thermal conductivity of composites was obtained when 10 % clay is added in the mixture, namely between 0.057 … 0.068 W/(mK). During the ignitability tests the flame did not propagate to a height greater than 15 cm throughout the 60 s test time, so it can be concluded that the materials match minimally in the class E of reaction to fire. The flexural strength of tested materials was under 0.8 MPa.

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 score0.015
Threshold uncertainty score0.411

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 head0.013
GPT teacher head0.210
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.

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
Published2024
Admission routes1
Has abstractyes

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