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Kinetic analysis of Fe(II)-promoted ethanol preparation from cornstalks

2018· article· en· W2803878906 on OpenAlexaff
Kun Chen, Dehuan Liu, Xu Chen, Fan Zhiwen

Bibliographic record

VenueBioResources · 2018
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsMemorial University of Newfoundland
FundersChongqing Municipal Education CommissionChina Scholarship CouncilChongqing UniversityNational Natural Science Foundation of China
KeywordsEthanolFermentationYield (engineering)Ethanol fuelHydrolysisSubstrate (aquarium)CellulaseEthanol fermentationLiquefactionChemistryChromatographyPulp and paper industryFood scienceMaterials scienceBiochemistryOrganic chemistryBiologyComposite material

Abstract

fetched live from OpenAlex

This paper presents a kinetic study of fuel grade ethanol production by simultaneous saccharification and fermentation from Fe(II)-catalyzed cornstalks. The study observed the optimal conditions of ethanol production as: inoculation proportion (ratio of Pachysolen tannophilus to Saccharomyces cerevisiae) 2:1, fermentation temperature 32 °C, inoculation quantity 20%, addition amount of Fe2+ 4 mg/g (substrate), and cellulase dosage 30 U/g (substrate). An ethanol yield of 0.335 mg/g was obtained from cornstalks pretreated using liquefaction under optimum conditions. A 30.4% increase in the yield was observed when compared with the control group without the addition of Fe2+. The relationship between ethanol yield and fermentation time could be described through a Langmuir isotherm model. The findings of this study will help researchers better understand and describe the complex characteristics of ethanol production from cornstalks with Fe2+ promoter, which will be very useful in improving production yields.

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 score0.171
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.225
Teacher spread0.212 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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