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Record W1523672974 · doi:10.15376/biores.7.3.3921-3934

Substrate characteristic analysis for anaerobic digestion: A study on rice and triticale straw

2012· article· en· W1523672974 on OpenAlexaff
Anna Teghammar, Richard P. Chandra, J. N. Saddler, Mohammad J. Taherzadeh, Ilona Sárvári Horváth

Bibliographic record

VenueBioResources · 2012
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCrystallinityCelluloseBiogasLigninCellulosic ethanolMaterials scienceAdsorptionFourier transform infrared spectroscopyPulp and paper industryAnaerobic digestionCellulaseEnzymatic hydrolysisChemistryMethaneNuclear chemistryChemical engineeringHydrolysisWaste managementComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Different substrate characteristic analyses have been studied on rice and triticale straw pretreated with NMMO (N-methylmorpholine-N-oxide) prior to biogas production. Simons’ stain, water retention value (WRV), and enzymatic adsorption were used to measure the change in the accessible surface area of the lignocellulosic substrates. FTIR was used to measure the change in cellulosic crystallinity and Time-of-Flight-Secondary-Ion-Spectroscopy (ToF-SIMS) to measure the ratio of cellulose to lignin on the sample surface. All methods showed increased accessible surface area and a decrease in crystallinity after the pretreatments. These qualities were linked to improved biogas production. In the future, the tested methods could replace the time-consuming methane potential analysis to predict the methane production of lignocellulosic materials. Simons’ stain, enzymatic adsorption, and crystallinity measurement by FTIR can be regarded as the recommended methods for the prediction of the improved biogas production as a result of the pretreatment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.358

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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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