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Record W2096132048 · doi:10.1002/mren.201300112

Synthesis and Characterization of Phenol Formaldehyde Novolac Resin Derived from Liquefied Mountain Pine Beetle Infested Lodgepole Pine Barks

2013· article· en· W2096132048 on OpenAlexaff
Yong Zhao, Boya Zhang, Ning Yan, Ramin Farnood

Bibliographic record

VenueMacromolecular Reaction Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLiquefactionFormaldehydePhenolMountain pine beetleSulfuric acidBark (sound)Phenol formaldehyde resinCuring (chemistry)ChemistryPinus contortaOrganic chemistryPolymer chemistryBotanyForestry

Abstract

fetched live from OpenAlex

In this study, mountain pine beetle (MPB, Dendroctonus ponderosae Hopkins) infested lodgepole pine ( Pinus contorta Dougl.) barks are liquefied in phenol using either sulfuric acid or hydrochloric acid as the catalyst and two types of bark‐based phenol‐formaldehyde novolac resins, namely liquefied bark‐novolac resin S and liquefied bark‐novolac resin C are synthesized using the liquefied bark fractions. Compared to using hydrochloric acid in bark liquefaction, sulfuric acid catalyzed bark liquefaction reveals a higher liquefaction yield with a lower free phenol content and a higher molecular weight in the liquefied bark fraction. Liquefied bark‐novolac resins are found to have higher molecular weights, higher curing activation energies, and faster curing rates than the lab‐made control resins without bark components. The uncured liquefied bark‐novolac resins have higher thermal stability than the uncured lab‐made control resins. After curing with hexamethylenetetramine (HMTA), liquefied bark‐novolac resins show a similar post‐cured thermal stability to the lab‐made control resins. The findings suggest that the liquefied barks from MPB infested lodgepole pine are suitable for synthesizing novolac resins.

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 score0.045
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 head0.003
GPT teacher head0.167
Teacher spread0.163 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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