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Record W2966874045 · doi:10.24908/iqurcp.13381

Chemical Recycling of Polystyrene with Tertiary Amine Switchable Hydrophilicity Solvents

2019· article· en· W2966874045 on OpenAlexaffvenue
Amelia Churaman, Ross Jansen van Vuuren

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2019
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsQueen's University
Fundersnot available
KeywordsX-ray photoelectron spectroscopyPolystyreneExpanded polystyreneAqueous solutionChemical engineeringSolventMaterials scienceVolume (thermodynamics)ChemistryOrganic chemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Nearly 40 metric tonnes of expanded polystyrene (XPS) waste is collected through Kingston’s curbside recycling program annually before being outsourced to companies with the means to recycle it. However, transporting XPS foam products is not economically viable. Therefore, a significant challenge is finding an efficient way to reduce the volume of the XPS foam prior to transportation since it has a very low density, consisting of up to 98% air. Currently, XPS products are compacted physically which requires the use of expensive compactors and energy-intensive compression processes. In 2011, Jessop et al. demonstrated that N,N-dimethylcyclohexylamine (DMCHA), a solvent with relatively low toxicity and volatility, can be used to recycle XPS using a greener approach. DMCHA is relatively hydrophobic under neutral conditions (e.g., in water), but becomes more hydrophilic when exposed to carbonated water (CO2 dissolves in water, forming carbonic acid which protonates the DMCHA). I have worked on optimizing this process via the following steps. Firstly, the XPS is dissolved in a small volume of DMCHA in its hydrophobic, neutral form. Then, the PS-DMCHA mixture is added to carbonated water causing the DMCHA to become hydrophilic and to dissolve in the aqueous solution, resulting in a layer of PS on the surface. The PS can then be easily collected and air-dried. By adding a 30 wt.% PS/DMCHA solution to carbonated water at 60oC, I have been able to achieve a typical purity of 95 wt.% of the final XPS (i.e., 5 wt% of DMCHA remains in the XPS), determined by 1H NMR.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.059
GPT teacher head0.306
Teacher spread0.247 · 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

Citations0
Published2019
Admission routes2
Has abstractyes

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