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Record W2140248588 · doi:10.6000/1927-5129.2013.09.66

Biosorption of Heavy Metals from Acid Mine Drainages onto Pig Bristles, Poultry Feathers and Crustacean Shells Industrial Biowastes

2013· article· en· W2140248588 on OpenAlexvenueno aff
Fernando I. Ramírez-Paredes, Teresa Manzano-Muñoz, Juan Carlos García-Prieto, J. Felipe Bello-Estévez, Galina G. Zhadan, Valery L. Shnyrov, John Kennedy, Manuel G. Roig

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsnot available
Fundersnot available
KeywordsBiosorptionFeatherChemistryBioremediationAdsorptionAqueous solutionMetal ions in aqueous solutionEnvironmental chemistryMetalChitinNuclear chemistryContaminationSorptionBiologyEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

The removal of metals ions from aqueous solutions plays an important role in water pollution control. In this study, a biosorption process for the bioremediation of heavy metal-contaminated acid mine drainages, located in Western Spain, has been developed. The process is based on the physico-chemical properties for the adsorption, ion exchange, and complexation of metal ions by biopolymers keratin and chitin from different industrial biowastes such as pig bristles, poultry feathers and crustacean shells. The selectivity for metals, the first order kinetics and yields of the corresponding biosorption processes of uranium and other metals polluting such acid mine drainages by such biosorbents are described. The biowaste rich in keratin (pig bristles) seems to show a higher biosorption capacity than that of bioresidues rich in chitin (crustacean shells). Moreover, factors such as the lower contamination by metals of acid waters, the lower influent water volume/biosorbent mass ratio, the configuration of the packed-bed reactor and the partial hydrolysis of keratin increase both the capacity and the rate of the process of metal biosorption onto the biosorbent.

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 categoriesInsufficient payload (model declined to judge)
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 score0.207
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.226
Teacher spread0.203 · 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.

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

Explore more

Same venueJournal of Basic & Applied SciencesSame topicAdsorption and biosorption for pollutant removalFrench-language works237,207