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Record W2594179691 · doi:10.18331/brj2017.4.1.4

Simultaneous biosorption and bioethanol production from lead-contaminated media by Mucor indicus

2017· article· en· W2594179691 on OpenAlexvenueno aff
Saman Samadi, Keikhosro Karimi, Sanaz Behnam

Bibliographic record

VenueBiofuel Research Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsnot available
FundersIsfahan University of Technology
KeywordsBiosorptionAdsorptionBiomass (ecology)MucorEthanolChemistryEthanol fuelMorphology (biology)BiofuelFood scienceBotanyBiologyNuclear chemistryFermentationBiochemistryBiotechnologyOrganic chemistryAgronomyPenicillium

Abstract

fetched live from OpenAlex

Mucor indicus with different morphologies was used for ethanol production and Pb2+ biosorption. With increasing Pb2+ concentration in the cultivation medium, the fungus morphology changed from purely filamentous to mostly filamentous and the biosorption capacity was increased. The maximum adsorption capacity predicted by Langmuir model was 118 mg/g for purely filamentous form. All morphologies were also cultivated in the presence of high Pb2+ concentration (300 mg/L) in consecutive stages. After the first stage of cultivation, the live biomass was separated and cultivated in a new medium similar to the first stage and cultivation was performed within five stages. All morphologies of M. indicus were able to grow and produce ethanol in the presence of lead at all stages but with lower yields than those cultivated in the absence of lead. The highest ethanol yields of 0.46 and 0.35 g ethanol per g consumed glucose were obtained by mostly filamentous morphology at the first and the last stages, respectively. The presence of lead decreased the glucose consumption rate of all morphologies and the yeast-like morphology consumed glucose within a shorter time than the other morphologies. Different morphologies were able to adsorb lead ions considerably (97–99%) within the five consecutive stages.

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)0.001
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 score0.187
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.307
Teacher spread0.262 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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