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Reduction Characteristics of Iron Oxide by the Hemicellulose, Cellulose, and Lignin Components of Biomass

2020· article· en· W3035574417 on OpenAlexafffund
Rufei Wei, Haiming Li, Yi‐Feng Lin, Lebiao Yang, Hongming Long, Chunbao Xu, Jiaxin Li

Bibliographic record

VenueEnergy & Fuels · 2020
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaAnhui University of TechnologyNational Natural Science Foundation of China
KeywordsHemicelluloseLigninCelluloseChemistryIron oxideThermogravimetric analysisXylanHydrogenPyrolysisMaterials scienceChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The direct reduction characteristics of iron oxide by cellulose, hemicellulose (it is difficult to prepare; xylan is used in its place in this work), and lignin were determined using X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermogravimetric Fourier transform infrared (TG-FTIR) combined with several other chemical analyses. The reduction of iron oxide by biomass is determined by gas and fixed carbon in biomass. The characteristic temperature of gas-based reduction ranges from 788 to 823 K, while the characteristic temperature of carbon-based reduction ranges between 1085 and 1154 K. Lignin plays a major role in the reduction of iron oxide mainly by its fixed carbon, while cellulose is mainly achieved by reducing the gases that come from its volatile components. The order of carbon reduction ability of the three kinds of biomass components from the strong to weak was lignin > hemicellulose > cellulose. The capacity for lignin was determined to depend on the morphology and quality of the lignin residue. The thin film structures or with higher carbon content in lignin accelerated the reduction reaction. There are two sources of hydrogen involved in the reduction of iron oxides by the three biomass components. Hydrogen from cellulose- and hemicellulose-based reduction of iron oxide is derived from cellulose and hemicellulose through its own pyrolysis, while hydrogen from lignin-based reduction is derived from carbon or carbon monoxide reacting with water.

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 score0.002
Threshold uncertainty score0.433

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 head0.180
Teacher spread0.171 · 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

Citations36
Published2020
Admission routes2
Has abstractyes

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