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Record W2528303529

Production of Bio-Based Phenol Formaldehyde Foams

2016· article· en· W2528303529 on OpenAlexfundno aff
Bing Li

Bibliographic record

VenueScholarship@Western (Western University) · 2016
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsGovernment of Ontario
KeywordsPulp and paper industryEnvironmental scienceFormaldehydeProduction (economics)Waste managementChemistryEngineeringOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Considering the declining non-renewable fossil resources, there is increasing interest in the development of more environmentally conscious, sustainable and cost-effective substitutes for chemical production. Lignin, a main component in lignocellulosic biomass, has been considered to be a potential substitute for petroleum-based phenol due to its phenolic structure.\nThis PhD dissertation aimed at producing bio-based phenol formaldehyde (BPF) foams using bio-phenols, including but not limited to, kraft lignin (KL), organosolv lignin (OL), hydrolysis lignin (HL), and bio-crude oil from white birth bark. The challenge of the existing process of producing BPF foams is that a low phenol substitution ratio, generally less than 30%, can be achieved due to much lower reactivity of bio-phenols compared with phenol. The use of conventional foaming technology was considered as the main reason for the low phenol substitution ratio in the production of BPF foams. Therefore, a novel foaming technology was developed for the production of BPF foams with high phenol substitution ratios of up to 50%, taking into account both fundamental foaming principle and characteristics of BPF resoles.\nKL and OL were used as petroleum-based phenol substitutes without any pretreatments for BPF foam production. The optimal composition of the blowing agent was found to be (5 wt.% hexanes + 5 wt.% pentane) for the 50% BPF foams, (7.5 wt.% hexanes + 2.5 wt.% pentane) for the 40% BPF foams, 10 wt.% hexanes for the 30% BPF foams. The results of cone calorimeter and limiting oxygen index (LOI) tests suggested that substituting phenol with lignin does not impact significantly on the combustion properties of the PF foams. Being even better, the introduction of KL in the PF foam could reduce the emission of toxic CO during the combustion.\nHL was de-polymerized by a proprietary low-temperature/no pressure de-polymerization process for the preparation of de-polymerized hydrolysis lignin (DHL) with Mn of 638 g/mol, Mw of 1910 g/mol, and PDI of 2.99. Then the DHL was utilized as a phenol substitute for the synthesis of foamable BPF resole resins, followed by employing the above mentioned new modified foaming technology for BPF foam production. White birch bark was hydrothermally liquefied in ethanol-water (1:1, w/w) mixture into phenolic bio-crude oil, followed by production of foamable BPF resoles and then BPF foams.\nAll BPF foams produced exhibited low apparent density, strong compressive strength, low thermal conductivity, and satisfactory closed cell structure, all of which were comparable to conventional PF foams. The new foaming technology demonstrated to be promising for industrial applications, which reduced the use of both petroleum-based phenol and toxic formaldehyde by up to 50% in the preparation of BPF foams. The utilization of lignin for manufacturing of value-added bio-based materials could not only produce high-value bio-based PF foams as promising insulation and fire-retarding materials, but also greatly benefit the forestry and agriculture sectors with additional revenue streams.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.046
GPT teacher head0.252
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2016
Admission routes1
Has abstractyes

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