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Record W2942129598 · doi:10.1007/s11705-019-1814-3

Synthesis of hydroxymethylfurfural and furfural from hardwood and softwood pulp using ferric sulphate as catalyst

2019· article· en· W2942129598 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers of Chemical Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsMcGill University
Fundersnot available
KeywordsSoftwoodHardwoodFurfuralChemistryFerricPulp (tooth)Cellulosic ethanolCatalysisOrganic chemistryPulp and paper industryCelluloseBotany

Abstract

fetched live from OpenAlex

Hydroxymethylfurfural (HMF) and furfural are promising chemicals for the creation of a bio-based economy. The development of an inexpensive catalytic system for converting cellulosic biomass into these chemicals is an important step in this regard. Ferric sulphate is a common, cheap and non-toxic Lewis acid that has been used to catalyse reactions such as wood depolymerisation. In this work, ferric sulphate was used to help the production of HMF and furfural from hardwood and softwood pulps. It was found that for hardwood pulp, the use of ferric sulphate alone gave a maximum HMF yield of 31.6 mol-%. The addition of the ionic liquid [BMIM]Cl or HCl as co-catalysts did not lead to an increase in the yields obtained. A prior decationisation step, however, resulted in HMF yields of 50.4 mol-%. Softwood pulp was harder to depolymerise than hardwood, with a yield of 28.7% obtained using ferric sulphate alone. The maximum HMF yield from softwood, 37.9 mol-%, was obtained using a combination of ferric sulphate and dilute HCl. It was thus concluded that ferric sulphate is a promising catalyst for HMF synthesis from cellulosic biomass.

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.

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 score0.189
Threshold uncertainty score0.646

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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