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Record W2000173784 · doi:10.1002/app.32240

Synthesis of novolac‐type phenolic resins using glucose as the substitute for formaldehyde

2010· article· en· W2000173784 on OpenAlexaff
Mingcun Wang, Zhongshun Yuan, Shuna Cheng, Mathew Leitch, Chunbao Xu

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsLakehead University
Fundersnot available
KeywordsHexamethylenetetraminePhenolFormaldehydeThermal stabilityGel permeation chromatographyChemistryCuring (chemistry)Thermosetting polymerSulfuric acidCondensation polymerNuclear chemistryPhenol formaldehyde resinPolymer chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract Novel Novolac type phenolic resins were prepared using glucose as the substitute for toxic formaldehyde (a carcinogenic chemical). The resins were synthesized with varying molar ratios of phenol to glucose, catalyzed by strong acid (such as sulfuric acid) at 120–150°C. Analysis of the resins using gel permeation chromatography (GPC) and proton nuclear magnetic resonance ( 1 H‐NMR) showed that they were broadly distributed oligomers derived from the Fridel‐Crafts condensation of phenol and glucose. Using hexamethylenetetramine (HMTA) as the curing agent, the phenol‐glucose resins could be thermally cured and exhibited exothermic peaks at 130–180°C, typical of thermosetting phenolic resins. The cured resins showed satisfactory thermal stability, e.g., they started to decompose at >280°C with residual carbon yields of above 58% at 600°C. Based on the thermal properties, phenol‐glucose resin with a molar ratio of 1 : 0.5 is promising as it could be cured at a lower temperature (147°C) and exhibited a satisfactorily good thermal stability: it started to decompose at >300°C with a residual carbon yield of >64% at 600°C. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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)0.001
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 score0.014
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.269
Teacher spread0.254 · 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

Citations20
Published2010
Admission routes1
Has abstractyes

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