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Record W3045057938 · doi:10.14447/jnmes.v22i3.a06

Preparation and performance of energy-saving and environment-friendly autoclaved

2019· article· en· W3045057938 on OpenAlexvenueno aff
Feihua Yang, Yi Zhu, Jun Li, Changlong Wang, Zhenzhen Ren, Xiaowei Cui

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersNatural Science Foundation of Shaanxi ProvinceNatural Science Foundation of Hebei ProvinceChina Postdoctoral Science Foundation
KeywordsEnvironmentally friendlyBusinessComputer scienceBiology

Abstract

fetched live from OpenAlex

In order to realize the resource utilization of solid waste, autoclaved aerated concrete (ACC) was prepared by hydrothermal synthesis with quartz tailing sand (QTS) as the main siliceous material. The effects of fineness and content of QTS on the properties, hydration products and microstructures of AAC were investigated by particle size analysis, physical and mechanical properties test, X-ray diffraction analysis (XRD), fourier transform infrared spectoscopy (FT-IR), and scanning electron microscope (SEM). The results show that the AAC containing 65% QTS (in mass percentage) with a specific surface area (SSA) of 320 m 2 kg -1 can achieve a compressive strength of 4.43 MPa and bulk density of 560 kg m -3 , which qualifies the requirements of B06, A3.5 of AAC sample regulated by the composition and morphology GB/T 11969-2008. The small size of QTS particles and the high thickness of the slurry are harmful to form a good pore structure of AAC. When the blending percentage of QTS is too high, the unreacted QTS particle increase and accumulate within the system, which reduces the space among them and thus influences the growth and crystallization of hydration products and the properties of AAC. Phase analyses show that the main crystalline phases in the AAC samples are tobermorite, C-S-H, calcite, residual quartz and residual minerals from QTS.

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 score0.027
Threshold uncertainty score0.260

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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