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Record W4220792926 · doi:10.18280/rcma.320103

Glass Waste Based Geopolymers and Their Characteristics

2022· article· en· W4220792926 on OpenAlexvenueno aff
Taha H. Abood AL-Saadi, Rana K. Abdulnabi, Muna Noori Ismael, Hazim Faleh Hassan, Mohanad Kadhim Mejbel

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSodium silicateMortarSodium hydroxidePorosityCompressive strengthComposite materialFoaming agentGeopolymerAggregate (composite)Volume (thermodynamics)Thermal stabilityAlkali metalPolymerChemical engineeringChemistry

Abstract

fetched live from OpenAlex

Inorganic polymer materials (Geopolymers) are synthesized using alumino-silicate sources as solid components with an alkaline solution. This material is used as an alternative for building materials and provides thermal protection as foaming materials. This paper presents the preparation of these materials by the reaction between glass waste (from brown color bottles BP) with sodium hydroxide NaOH and sodium aluminum (AN5) solutions as alkali activators. For the preparation of mortars (BP-N5 and BP-AN5), sand was used as aggregate. The compressive strengths were assessed (24 and 6 MPa) respectively before heat treatment, the hydrolytic stability (PH and conductivity) tests were performed. Furthermore, hardened mortars have been heated at very high temperatures in the range of 200℃ to 800℃ within two hours. Based on the nature of the foaming behavior of such materials, various variables have changed; (80-140) % volume increase and porosity rise through the process of heat treatment, particularly at 600℃ and 800℃. On the other hand, (3.5-7) % mass reduction occurred. It can be said that the more significant porosity with different geometrical configurations (sizes and shapes) of such materials can be considered as acoustic insulation and thermal materials.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.827

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)0.0010.000

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 head0.035
GPT teacher head0.243
Teacher spread0.209 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicConcrete and Cement Materials ResearchFrench-language works237,207