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Record W2033244845 · doi:10.1002/jbm.a.34421

Enhanced catalytic activity from proteinoid microspheres

2012· article· en· W2033244845 on OpenAlexaff
Stephen Quirk

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsKimberly-Clark (Canada)
FundersGeorgia Institute of Technology
KeywordsCatalysisPhosphodiester bondMaterials scienceHydrolysisZincSubstrate (aquarium)EnzymeCombinatorial chemistryPhosphateOrganic chemistryChemistryBiochemistryMetallurgy

Abstract

fetched live from OpenAlex

Creating materials that are capable of catalyzing enzymatic reactions could be important to the treatment of both acute and chronic wounds, as well as other topical diseases. As a first step in the design of catalytic biomaterials, a new class of proteinoid microsphere (PM), that includes amino acids found in phosphatase enzyme active sites, has been constructed. This material can significantly enhance catalytic activity for phosphoester hydrolysis, with observed specific activity increases between 35- and 55-fold. Further specific activity increases occur when metal cations, notably iron or zinc, are added to the PMs. Specific activity increases between 140- and 300-fold for these metal modified systems are measured. The phosphatase activity increase is demonstrated for both aromatic phosphate esters as well as the high-energy phosphate bond of adenosine triphosphate. PMs bind substrate heterogeneously on their surfaces in an enthalpically driven reaction that is defined by an overall favorable free energy, but unfavorable entropy. The catalytic PMs have been successfully blended with polyolefin foam and extruded with PLA. These materials remain fully active.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.077
GPT teacher head0.398
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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