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Record W2314437451 · doi:10.1021/bm2003434

Printing of Polymer Microcapsules for Enzyme Immobilization on Paper Substrate

2011· article· en· W2314437451 on OpenAlexaff
Anne Savolainen, Yufen Zhang, Dominic Rochefort, Ulla Holopainen‐Mantila, Tomi Erho, Jouko Virtanen, Maria Smolander

Bibliographic record

VenueBiomacromolecules · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSubstrate (aquarium)InkwellMaterials sciencePolymerImmobilized enzymeLaccaseChemical engineeringCoatingChemistryNanotechnologyPolymer chemistryComposite materialOrganic chemistryEnzyme

Abstract

fetched live from OpenAlex

Poly(ethyleneimine) (PEI) microcapsules containing laccase from Trametes hirsuta (ThL) and Trametes versicolor (TvL) were printed onto paper substrate by three different methods: screen printing, rod coating, and flexo printing. Microcapsules were fabricated via interfacial polycondensation of PEI with the cross-linker sebacoyl chloride, incorporated into an ink, and printed or coated on the paper substrate. The same ink components were used for three printing methods, and it was found that laccase microcapsules were compatible with the ink. Enzymatic activity of microencapsulated TvL was maintained constant in polymer-based ink for at least eight weeks. Thick layers with high enzymatic activity were obtained when laccase-containing microcapsules were screen printed on paper substrate. Flexo printed bioactive paper showed very low activity, since by using this printing method the paper surface was not fully covered by enzyme microcapsules. Finally, screen printing provided a bioactive paper with high water-resistance and the highest enzyme lifetime.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.218
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 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

Citations47
Published2011
Admission routes1
Has abstractyes

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