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Record W3198506156 · doi:10.1002/ceat.202100295

Metal Ion Adsorption Using Coconut Shell Powder Activated by Chemical and Physical Treatments

2021· article· en· W3198506156 on OpenAlex
Paula Fabiane Pinheiro do Nascimento, Eduardo Lins de Barros Neto, João Fernandes de Sousa, Vitor Trocolli Ribeiro, Lindemberg de Jesus Nogueira Duarte, Ricardo Paulo Fonsêca Melo, Francisco Wendell Bezerra Lopes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Engineering & Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversité de Sherbrooke
FundersUniversidade Federal do Rio Grande do Norte
KeywordsAdsorptionChemistryDesorptionMetal ions in aqueous solutionLangmuirLangmuir adsorption modelMicroemulsionMetalMaterials scienceChemical engineeringNuclear chemistryChromatographyOrganic chemistryPulmonary surfactant

Abstract

fetched live from OpenAlex

Abstract A comparative evaluation is presented of physical and chemical treatments performed on coconut shell powder, in relation to the adsorption capacity of Cu 2+ /Cd 2+ ions. The chemical treatment consisted of impregnation with monoethanolamine (mass ratio of 1:1) or a microemulsion (composed of 10 wt % sodium octanoate). The physical treatment was through steam explosion ( P = 5 bar, T = 210 °C). Treatment efficiency was assessed using physical characterization of the adsorber material. Assessment of the ion adsorption capacity was performed in batch mode. The comparative investigation was carried out based on the Langmuir model constants, kinetic constants, and thermodynamic parameters. The efficiency of the modifications was also evaluated through adsorption‐desorption 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.

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 score0.015
Threshold uncertainty score0.835

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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