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Record W2622512746 · doi:10.1016/j.jmbbm.2017.06.005

Mammalian fibroblast cells avoid residual stress zone caused by nanosecond laser pulses

2017· article· en· W2622512746 on OpenAlexafffund
Candace Colpitts, Amin M. Ektesabi, Rachael A. Wyatt, Bryan D. Crawford, Amirkianoosh Kiani

Bibliographic record

VenueJournal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Ontario Institute of TechnologyUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsResidual stressMaterials scienceSiliconLaserAblationLaser ablationStress (linguistics)Groove (engineering)Composite materialOpticsOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

This study investigates the effects of laser irradiation on crystalline silicon and its application in biomaterials. We used an analytical model to predict the ablation depth and pit size resulting from laser exposure of silicon samples. The temperatures generated are predicted correlate with laser power, and to result in the formation of a residual stress zone bordering the ablated groove. Different crystal orientations found in the substrate confirm that there was crystal distortion, which consequently induces these residual stress zones. Mouse embryonic fibroblasts avoid the stress areas and accumulate outside of these zones. Higher laser power results in broader residual stress zone and a larger zone of cellular exclusion. We argue that residual stress resulting from high-energy laser ablation of silicon may be a promising avenue to explore as a method for patterning cell growth on these materials.

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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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