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Record W4406376573 · doi:10.1051/e3sconf/202560202007

Enrichment of Second Generation Ethanol Concentration Obtained from Napier Grass Pretreated with Sulfuric Acid and Hydrothermal Pretreatment

2025· article· en· W4406376573 on OpenAlexaff
Vanarat Phakeenuya, Elizabeth Jayex Panakkal, Marttin Paulraj Gundupalli, Roungdao Klinjapo, Malinee Sriariyanun, Prapakorn Tantayotai

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Alberta
FundersThailand Science Research and Innovation
KeywordsHydrothermal circulationEthanolSulfuric acidChemistryNuclear chemistryPulp and paper industryFood scienceChemical engineeringBiochemistryInorganic chemistryEngineering

Abstract

fetched live from OpenAlex

Enhancing sugar concentration is crucial for improving ethanol yield in biorefinery processes, enabling more efficient downstream recovery. This study investigates the hydrothermal pretreatment of Napier grass with 2% sulfuric acid to boost sugar recovery and ethanol production by incorporating a concentration step. After pretreatment, the liquid fractions were concentrated two-fold and four-fold through rotary evaporation and freeze-drying, resulting in a significant increase in sugar levels, with a 3.5-fold rise in sugar concentration achieved through rotary evaporation compared to unconcentrated samples. However, ethanol production was limited by elevated levels of inhibitors, such as acetic acid and furfural. The maximum ethanol concentration reached was 2.43%, from a liquid fraction concentrated four-fold. These results highlight the necessity of concentration techniques to improve sugar recovery, while also emphasizing the importance of removing inhibitors to increase ethanol yields and enhance the overall efficiency of biorefining processes.

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 score0.047
Threshold uncertainty score0.352

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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