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Record W2033814089 · doi:10.1002/app.21115

Effect of wood on the curing behavior of commercial phenolic resin systems

2004· article· en· W2033814089 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

VenueJournal of Applied Polymer Science · 2004
Typearticle
Languageen
FieldEngineering
TopicEpoxy Resin Curing Processes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCuring (chemistry)Differential scanning calorimetryMaterials scienceActivation energyComposite materialPolymer chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Differential scanning calorimetry (DSC) was used to study the effect of wood on the curing behavior of two types of commercial oriented‐strand‐board phenolic resins. DSC analysis showed that the curing behavior of the core resin differed significantly from that of the face resin in terms of the peak shape, peak temperature, and activation energy. The addition of wood to the resins moved the two separated peaks in the DSC curves of the core resin adjacent to each other. It also accelerated the addition reactions in the curing processes of both the core and face resins. The two peaks in the DSC curves were the result of the high pH values of the resins. These two peaks became either jointed together or overlapped when the pH value of the resin was reduced. Wood also reduced the activation energies for both the core and face resins by decreasing the pH values of the curing systems. Moreover, the effects of wood on the curing behavior of the resins among the five species studied were similar. The lowest activation energy for a phenolic resin probably appeared at pH 10–11 under alkaline conditions. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 95: 185–192, 2005

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)0.001
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 score0.046
Threshold uncertainty score0.349

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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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