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Record W2966826786 · doi:10.3762/bjnano.10.157

Upcycling of polyurethane waste by mechanochemistry: synthesis of N-doped porous carbon materials for supercapacitor applications

2019· article· en· W2966826786 on OpenAlex
Christina Schneidermann, Pascal Otto, Desirée Leistenschneider, Sven Grätz, Claudia Eßbach, Lars Borchardt

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

VenueBeilstein Journal of Nanotechnology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
FundersBundesministerium für Bildung und Forschung
KeywordsSupercapacitorMaterials sciencePolyurethanePorosityCarbon fibersChemical engineeringNitrogenAqueous solutionElectrolyteSolventElectrochemistryElectrodeComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

We developed an upcycling process of polyurethane obtaining porous nitrogen-doped carbon materials that were applied in supercapacitor electrodes. In detail, a mechanochemical solvent-free one-pot synthesis is used and combined with a thermal treatment. Polyurethane is an ideal precursor already containing nitrogen in its backbone, yielding nitrogen-doped porous carbon materials with N content values of 1–8 wt %, high specific surface area values of up to 2150 m 2 ·g −1 (at a N content of 1.6 wt %) and large pore volume values of up to 0.9 cm 3 ·g −1 . The materials were tested as electrodes for supercapacitors in aqueous 1 M Li 2 SO 4 electrolyte (100 F·g −1 ), organic 1 M TEA-BF 4 (ACN, 83 F·g −1 ) and EMIM-BF 4 (70 F·g −1 ).

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 score0.003
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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