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Record W2462694323 · doi:10.3390/jfb7030017

Synchrotron-Based in Situ Characterization of the Scaffold Mass Loss from Erosion Degradation

2016· article· en· W2462694323 on OpenAlex

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

VenueJournal of Functional Biomaterials · 2016
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceSynchrotronPolycaprolactoneDegradation (telecommunications)ScaffoldIn situErosionPLGACharacterization (materials science)Composite materialBiomedical engineeringChemical engineeringNanotechnologyNanoparticleChemistryOpticsComputer sciencePolymer

Abstract

fetched live from OpenAlex

The mass loss behavior of degradable tissue scaffolds is critical to their lifespan and other degradation-related properties including mechanical strength and mass transport characteristics. This paper presents a novel method based on synchrotron imaging to characterize the scaffold mass loss from erosion degradation in situ, or without the need of extracting scaffolds once implanted. Specifically, the surface-eroding degradation of scaffolds in a degrading medium was monitored in situ by synchrotron-based imaging; and the time-dependent geometry of scaffolds captured by images was then employed to estimate their mass loss with time, based on the mathematical model that was adopted from the literature of surface erosion with the experimentally-identified model parameters. Acceptable agreement between experimental results and model predictions was observed for scaffolds in a cylindrical shape, made from poly(lactic-co-glycolic) acid (PLGA) and polycaprolactone (PCL). This study illustrates that geometry evaluation by synchrotron-based imaging is an effective means to in situ characterize the scaffold mass loss as well as possibly other degradation-related properties.

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)0.000
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 score0.423
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.228
Teacher spread0.212 · 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