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Record W2765330169 · doi:10.3390/molecules22111850

Sustainable Bio-Based Phenol-Formaldehyde Resoles Using Hydrolytically Depolymerized Kraft Lignin

2017· article· en· W2765330169 on OpenAlexaff
Homaira Siddiqui, Nubla Mahmood, Zhongshun Yuan, Ferdinando Crapulli, Luana Dessbesell, Amin S. Rizkalla, Ajay K. Ray, Chunbao Xu

Bibliographic record

VenueMolecules · 2017
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead UniversityWestern University
Fundersnot available
KeywordsPhenolLigninFormaldehydeCuring (chemistry)ChemistryKraft paperOrganic chemistryPhenolsNuclear chemistryPolymer chemistryChemical engineering

Abstract

fetched live from OpenAlex

In this study bio-based bio-phenol-formaldehyde (BPF) resoles were prepared using hydrolytically depolymerized Kraft lignin (DKL) as bio-phenol to partially substitute phenol. The effects of phenol substitution ratio, weight-average molecular weight (Mw) of DKL and formaldehyde-to-phenol (F/P) ratio were also investigated to find the optimum curing temperature for BPF resoles. The results indicated that DKL with Mw ~ 1200 g/mol provides a curing temperature of less than 180 °C for any substitution level, provided that F/P ratios are controlled. Incorporation of lignin reduced the curing temperature of the resin, however, higher Mw DKL negatively affected the curing process. For any level of lignin Mw, the curing temperature was found to increase with lower F/P ratios at lower phenol substitution levels. At 25% and 50% phenol substitution, increasing the F/P ratio allows for synthesis of resoles with lower curing temperatures. Increasing the phenol substitution from 50% to 75% allows for a broader range of lignin Mw to attain low curing temperatures.

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 categoriesMeta-epidemiology (narrow)
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 score0.046
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.225
Teacher spread0.216 · 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.

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

Citations35
Published2017
Admission routes1
Has abstractyes

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