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Record W2023868136 · doi:10.1002/cjce.20515

Comparative analysis for three different immobilisation strategies in the hexavalent chromium biosorption process using <i>Bacillus sphaericus</i> S‐layer

2011· article· en· W2023868136 on OpenAlexvenueno aff
Diana Marcela Carrero, Johanna Maritza Morales, Andrea Carolina Garcia, Nathalia Florez, Paula Delgado, Jenny Dussán, Andrés Córdoba, Andrés Fernando González Barrios

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsnot available
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsBiosorptionBacillus sphaericusHexavalent chromiumChromiumEnvironmental scienceEnvironmental engineeringEnvironmental chemistryChemistryBiologyMaterials scienceBacillalesMetallurgyAdsorptionBacteria

Abstract

fetched live from OpenAlex

Abstract Hexavalent chromium constitutes an important water pollutant due to its cancerogenic properties. In countries such as Colombia it is widely used as a preservative in the leather industry. Bacillus sphaericus S‐layer exhibits high metal‐binding capacity hence the use of this microorganism to remove metals such as chromium from polluted regions based on immobilised cells systems is suggested to be an interesting proposal. The paper describes thermodynamic adsorption process of Cr(VI) on immobilised biomass of B. sphaericus cells on polyurethane foam, sol–gel, and sawdust. The adsorption isotherms describe different immobilisation mechanisms regarding the support utilised. For example, polyurethane and sawdust immobilisation process were described with the classical Langmuir model while sol–gel followed Generalised Freundlich–Kiselev behaviour. Moreover, through determining breakthrough curves in packed columns with each medium we found surface diffusion and mass bulk transfer rate as crucial factors when scaling‐up the process.

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)0.000
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 score0.031
Threshold uncertainty score0.509

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 head0.075
GPT teacher head0.283
Teacher spread0.208 · 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

Citations7
Published2011
Admission routes1
Has abstractyes

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