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Record W2254391460 · doi:10.5539/mas.v10n1p121

Factorial Design Analysis of Zn(II) Ions Adsorption on Thermally Treated Rice Husk

2015· article· en· W2254391460 on OpenAlexvenueno aff
Keat Khim Ong, A. T. Ahmad Farhan, W. M. Z. Wan Yunus, Ahmad Mujahid Ahmad Zaidi, M. L. Jabit, A. Fitrianto, K. M. Safidin, Ummul Fahri Abdul Rauf, Fadhlina Che Ros, Abdul Ghapor Hussin, Mashkoor Ahmad

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
FundersInstitut Penyelidikan dan Kemajuan Pertanian MalaysiaNational Defence University of Malaysia
KeywordsHuskAdsorptionFactorial experimentMetal ions in aqueous solutionChemistryMetalInteractionNuclear chemistryAqueous solutionMaterials scienceMathematicsBotany

Abstract

fetched live from OpenAlex

<p class="zhengwen"><span lang="EN-GB">Adsorption of Zn(II) ions from aqueous solutions by thermally treated rice husk was investigated using factorial experimental design to study effects of heating temperature and period of rice husk, pH, initial Zn(II) ion concentration, adsorption temperature and contact time, and adsorbent dosage. Main and interaction effects of these factors were analyzed using statistical techniques and the results were analyzed statistically using the Student’s t-analysis and analysis of variance which were used to determine significant factors that affect the percentage removal of Zn(II). These significant factors were heating temperature of rice husk, pH, initial metal concentration, contact time, and adsorbent dosage. The interaction between two different effects also affects the percentage removal of Zn(II) ions. These include the interactions between heating temperature of rice husk and initial metal concentration, pH and initial metal concentration, and pH and the adsorbent dosage.</span></p>

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)0.000
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 score0.678
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.115
GPT teacher head0.355
Teacher spread0.240 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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