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Adsorptive Removal of Methyl Red from Aqueous Solution onto Charred and Aminated Sugarcane Waste

2013· article· en· W2115232503 on OpenAlexvenueno aff
Jagjit Kour, Shankar Pandey, Puspa Lal Homagai, Megh Raj Pokhrel, Kedar Nath Ghimire

Bibliographic record

VenueJournal of Membrane and Separation Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsnot available
FundersTribhuvan University
KeywordsAqueous solutionChemistryPulp and paper industryAdsorptionOrganic chemistryNuclear chemistry

Abstract

fetched live from OpenAlex

Adsorptive removal of Methyl Red (MR) from aqueous solution onto chemically modified charred sugarcane waste (CSW) and aminated sugarcane waste (ASW) has been investigated. The surface modification was characterized by FTIR, SEM, elemental analysis and Boehm titration. The effect of pH, contact time and MR concentrations were studied by batch equilibrium method. Maximum dye removal was observed at pH 2 onto CSW while that for ASW at pH 7. The dye can be quantitatively removed onto the surface of these adsorbents at a contact time of 3 h. Maximum adsorption capacity (qmax) for the CSW and ASW were found to be 125.0 mg/g and 142.85 mg/g, respectively. Adsorption kinetic data were tested using pseudo-first order, pseudo-second order and intra-particle diffusion models. Kinetic studies revealed that the adsorptive removal of the dye onto the adsorbents followed pseudo-second order kinetics model. The obtained results indicated an excellent alternative for the treatment of dye contaminated wastewater using such chemically modified sugarcane waste at low cost with better efficiency.

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)0.0000.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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