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Record W3044716114 · doi:10.3390/polym12071623

High Acid Biochar-Based Solid Acid Catalyst from Corn Stalk for Lignin Hydrothermal Degradation

2020· article· en· W3044716114 on OpenAlexaff
Qimeng Jiang, Guihua Yang, Fangong Kong, Pedram Fatehi, Xiaoying Wang

Bibliographic record

VenuePolymers · 2020
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNational Natural Science Foundation of China
KeywordsBiocharCatalysisStalkLigninHydrothermal carbonizationChemistryCarbonizationYield (engineering)Solid acidSpecific surface areaNuclear chemistryOrganic chemistryMaterials scienceChemical engineeringAdsorptionPyrolysisComposite material

Abstract

fetched live from OpenAlex

Solid acid catalysts generally show the disadvantage of low acid amount and low recycling rate. To solve these problems, corn stalk-based solid acid catalysts were synthesized through carbonization and sulfonation processes in this work. The results showed that besides the rod-like structure inherited from raw corn stalk, the catalysts contained some small broken pieces on the surface, and the specific surface area varied from 1120 to 1640 m2/g. The functional groups (-SO3H) were successfully introduced onto the surface of the obtained solid acid catalysts. The acid amount varied between 1.2 and 2.4 mmol/g, which was higher than most of solid acid catalysts. The catalyst produced at 800 °C for 6 h in carbonation and then at 150 °C for 8 h in sulfonation had larger specific surface area and more sulfonate groups. In the degradation of lignin, the use of catalyst led to the generation of more aromatic compounds (65.6 wt. %) compared to that without using the catalyst (40.5 wt. %). In addition, a stable yield of reaction (85%) was obtained after four reuses. Therefore, corn stalk is suitable for high-value utilization to prepare high-acid amount biochar-based catalyst.

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 score0.013
Threshold uncertainty score0.813

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 head0.012
GPT teacher head0.206
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

Citations12
Published2020
Admission routes1
Has abstractyes

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