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Record W7106319910 · doi:10.5281/zenodo.17664228

Characterization of mechanical properties and microstructure of hydrogel scaffolds by X‑ray propagation-based imaging

2023· article· W7106319910 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Language
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
Fundersnot available
KeywordsCharacterization (materials science)MicrostructureSynchrotron radiationSynchrotronSelf-healing hydrogelsTomographyMicroscopeImage resolution

Abstract

fetched live from OpenAlex

Characterization of the mechanical properties and internal microstructure of scaffold, once implanted, is imperative in tissue engineering and medicine regeneration. Notably, this has been challenging to date as various conventional characterization methods by, for example, mechanical testing (for mechanical properties) and microscope (for internal microstructure) are destructive as they require removing scaffolds from the implantation site and processing samples for characterization. Synchrotron radiation X‑ray propagation-based computed tomography (SR-PBI-CT) is feasible and promising for nondestructive monitoring of hydrogel scaffolds. With the high brightness and high coherence of synchrotron radiation source, SR-PBI imaging is able to visualize and distinguish low-density soft tissues and hydrogels with high spatial resolution and scan speed, while the feature of large imaging depth with X-ray CT ensures the reconstruction of the three-dimensional (3D) scaffold. As inspired, this study aimed to perform a study on the characterization of mechanical properties and microstructure of hydrogel scaffolds by the SR-PBI-CT imaging.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.230
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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