MétaCan
Menu
Back to cohort
Record W2531137806 · doi:10.5301/jabfm.5000327

Mammalian Fibroblast Cells Show Strong Preference for Laser-Generated Hybrid Amorphous Silicon-SiO <sub>2</sub> Textures

2016· article· en· W2531137806 on OpenAlexaff
Candace Colpitts, Amin M. Ektesabi, Rachael A. Wyatt, Bryan D. Crawford, Amirkianoosh Kiani

Bibliographic record

VenueJournal of Applied Biomaterials & Functional Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsSiliconMaterials scienceLaserMicroscale chemistryWaferAmorphous siliconLaser ablationAmorphous solidOptoelectronicsHybrid silicon laserSilicon oxideNanosecondNanotechnologyOpticsCrystalline siliconChemistryCrystallography

Abstract

fetched live from OpenAlex

BACKGROUND: In this study, we investigated a method to produce bioactive hybrid amorphous silicon and silicon oxide patterns using nanosecond laser pulses. METHODS: Microscale line patterns were made by laser pulses on silicon wafers at different frequencies (25, 70 and 100 kHz), resulting in ablation patterns with frequency-dependent physical and chemical properties. RESULTS: Incubating the laser-treated silicon substrates with simulated body fluid demonstrated that the physicochemical properties of the laser-treated samples were stable under these conditions, and favored the deposition of bone-like apatite. More importantly, while NIH 3T3 fibroblasts did colonize the untreated regions of the silicon wafers, they showed a strong preference for the laser-treated regions, and further discriminated between substrates treated with different frequencies. CONCLUSIONS: Taken together, these data suggest that laser materials processing of silicon-based devices is a promising avenue to pursue in the production of biosensors and other bionic devices.

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 score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.197
Teacher spread0.180 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied Biomaterials & Functional MaterialsSame topicLaser Material Processing TechniquesFrench-language works237,207