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Bath Adsorption Study of Methylene Blue Dye Onto Sunflower Seeds Husks Activated Carbon

2013· article· en· W1573331232 on OpenAlexvenueno aff
Jasim M. Salman, Faisal Dahi ALmutairi

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
Fundersnot available
KeywordsActivated carbonPotassium hydroxideMethylene blueAdsorptionFreundlich equationNuclear chemistryHuskChemistryLangmuir adsorption modelSunflower seedDesorptionLangmuirAqueous solutionSunflowerChemical engineeringOrganic chemistryBotanyCatalysis

Abstract

fetched live from OpenAlex

Mesoporous activated carbon prepared from sunflower seeds husks (SSH) using physiochemical activation (potassium hydroxide treatment and carbon dioxide gasification). The optimum conditions for preparing activated carbon from (SSH) were found to be activation temperature of 500 oC, activation time of 1.0 h and chemical impregnation ratio of 1:1. The adsorptions of methylene blue (MB) onto sunflower seed husks activated carbon (SSHAC) were studied with respect to initial MB concentration. The experimental data were analyzed by the Langmuir isotherm, the Freundlich isotherm and the Temkin isotherm. Equilibrium data fitted well with the Langmuir model with maximum adsorption capacity of 410 mg/g at 30 oC for MB concentration range of 50-300 mg/L. Desorption-adsorption studies for the spent SSHAC (saturated with MB) using ethanol as solvent showed that regeneration efficiency around 85%. The results indicated that the SSHAC is very effective for the adsorption of MB from aqueous solutions and can be regenerate to use for more than two adsorption cycles.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.278
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2013
Admission routes1
Has abstractyes

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