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Record W7037066005

DEVELOPMENT OF MECHANICAL CHARACTERIZATION METHOD OF HYDROGEL SCAFFOLDS USING SYNCHROTRON PROPAGATION-BASED IMAGING

2023· dissertation· en· W7037066005 on OpenAlexfundaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicOrthoptera Research and Taxonomy
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanadian Light Source
KeywordsCharacterization (materials science)Biocompatible materialGelatinMicrostructureSynchrotronTissue engineeringCompression (physics)Self-healing hydrogelsCompression test
DOInot available

Abstract

fetched live from OpenAlex

Hydrogel-based scaffolds have been widely used in soft tissue regeneration due to their biocompatible and tissue-like environment for maintaining cellular functions and tissue regeneration. Understanding the mechanical properties and internal microstructure of hydrogel-based scaffold, once implanted, is imperative in many tissue engineering applications and longitude studies. 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 propagation-based imaging – computed tomography (PBI-CT) is feasible and promising for non-destructive monitoring of hydrogel scaffolds. As inspired, this study aimed to perform a study on the characterization of mechanical properties and microstructure of hydrogel scaffolds based on the PBI-CT.\nThe hydrogel solutions were prepared from 3% w/v alginate + 1% w/v gelatin and then printed by using the needle with a diameter of 200 μm, to form scaffolds with a dimension of 10×10×5 mm3. After successfully crosslinking of scaffolds, some of the scaffolds were degraded in a 37 °C media of phosphate buffered saline over 3 days, for the subsequent examination, along with those without degradation. The scaffolds both with and without degradation were subject to compressive testing, where the compression speed was set at 0.1 mm/s to reach the strain of 10%, 20%, 30%, 40%, and 50%, respectively. Once reached, the strain was held for 5 minutes for measuring the force and thus the stress within scaffolds, yielding the stress–strain curves. After that, the scaffolds were imaged and examined by SR-PBI-CT at the BMIT-ID beamline at Canadian Light Source (CLS). During the imaging process, the scaffolds were mechanically loaded, respectively, with the strains same as the ones in the aforementioned compressive testing, and at each strain, the scaffold was imaged and scanned with a pixel size of 13 µm for analysis.\nFrom the stress-strain curves obtained in the compression testing, the Young’s modulus was evaluated to characterize the elastic behavior of scaffolds: with the range between around 5-25 kPa; from the images captured by SR-PBI-CT, the scaffolds microstructures were examined in terms of the strand cross-section area, pore size, and hydrogel volume. Further, from the SR-PBI-CT images, the stress within hydrogel of scaffolds were evaluated, showing the agreement with those obtained from compression testing. These results have illustrated that the mechanical properties and microstructures of scaffolds, ether being degraded or not, can be examined and characterized by the SR-PBI-CT imaging, in a non-destructive manner. This would represent a significant advance for facilitating longitude studies on the scaffolds once implanted in-vivo.

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)0.000
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 score0.414
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.203
Teacher spread0.187 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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