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Record W4378837250 · doi:10.1002/cjce.24979

Preparation of Ca(<scp>OH</scp>)<sub>2</sub> nanoparticles by impinging stream reaction precipitation method

2023· article· en· W4378837250 on OpenAlexvenueno aff
Jianwei Zhang, Jin Zhang, Xin Dong, Ying Feng, Juchao Niu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsnot available
FundersLiaoning Revitalization Talents ProgramNational Natural Science Foundation of China
KeywordsDispersantNanoparticlePrecipitationParticle sizeThermal decompositionMaterials scienceMicrostructureDecompositionCrystal (programming language)Chemical engineeringAnalytical Chemistry (journal)MineralogyChemistryNanotechnologyChromatographyDispersion (optics)Organic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract In this paper, Ca(OH) 2 nanoparticles are prepared by impinging stream co‐precipitation method. The process of preparing nanoparticles by impinging stream is optimized. The influence of process variable, such as dispersant type, dispersant dosage, circulating flow rate, reactant concentration ratio of OH − and Ca 2+ , reaction temperature and circulation time, on the particle size of Ca(OH) 2 nanoparticle are investigated. The results show that the concentration ratio of reactants has a significant effect on the size of Ca(OH) 2 nanoparticles. The optimal process conditions are obtained by single factor experiment, PEG6000, 3.3% dispersant dosage, Q = 1000 L · h −1 , C = 1.88, T = 46.68°C and t = 60 min. The average particle size of Ca(OH) 2 nanoparticles prepared under these conditions is 107.67 nm. According to the microstructure analysis, the prepared Ca(OH) 2 nanoparticles samples have high purity and a good crystal structure. The powder dispersed with a marked hexagonal crystal shape and good thermal decomposition.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.012
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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