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Record W2149133272 · doi:10.1002/aic.14716

Engineering biomass into formaldehyde‐free phenolic resin for composite materials

2014· article· en· W2149133272 on OpenAlexafffund
Yongsheng Zhang, Zhongshun Yuan, Chunbao Xu

Bibliographic record

VenueAIChE Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsU.S. Environmental Protection Agency
KeywordsOrganosolvCuring (chemistry)FormaldehydeLigninPhenolDifferential scanning calorimetryComposite numberChemistryPhenol formaldehyde resinThermal stabilityKraft paperChemical engineeringOrganic chemistryMaterials sciencePolymer chemistryComposite material

Abstract

fetched live from OpenAlex

The use of formaldehyde to prepare phenol‐formaldehyde (PF) resins is one of the primary challenges for the world‐wide PF industry with respect to both sustainability and human health. This study reports a novel one‐pot synthesis process for phenol‐5‐hydroxymethylfurfural (PHMF) resin as a formaldehyde‐free phenolic resin using phenol and glucose, and the curing of the phenolic resin with a green curing agent organosolv lignin (OL) or Kraft lignin (KL). Evidenced by 13 C NMR, the curing mechanism involves alkylation reaction between the hydoxyalkyl groups of lignin and the ortho‐ and para‐carbon of PHMF phenolic hydroxyl group. The curing kinetics was studied using differential scanning calorimetry and the kinetic parameters were obtained. The OL/KL cured PHMF resins were tested in terms of thermal stability, and mechanical properties for their applications in fiberglass reinforced composite materials. The results obtained demonstrated that OL/KL can be promising curing agents for the PHMF resins. © 2015 American Institute of Chemical Engineers AIChE J , 61: 1275–1283, 2015

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 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 score0.012
Threshold uncertainty score0.634

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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

Citations51
Published2014
Admission routes2
Has abstractyes

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