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Record W2058714058 · doi:10.1002/jbm.a.33103

Response of murine bone marrow‐derived mesenchymal stromal cells to dry‐etched porous silicon scaffolds

2011· article· en· W2058714058 on OpenAlexafffund
Mohamad Hajj‐Hassan, Maedeh Khayyat‐Kholghi, Huifen Wang, Vamsy P. Chodavarapu, Janet E. Henderson

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2011
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsMaterials scienceWaferSiliconPorous siliconEtching (microfabrication)FabricationScanning electron microscopeDry etchingNanotechnologyBiomedical engineeringComposite materialOptoelectronicsPathology

Abstract

fetched live from OpenAlex

Porous silicon shows great promise as a bio-interface material due to its large surface to volume ratio, its stability in aqueous solutions and to the ability to precisely regulate its pore characteristics. In the current study, porous silicon scaffolds were fabricated from single crystalline silicon wafers by a novel xenon difluoride dry etching technique. This simplified dry etch fabrication process allows selective formation of porous silicon using a standard photoresist as mask material and eliminates the post-formation drying step typically required for the wet etching techniques, thereby reducing the risk of damaging the newly formed porous silicon. The porous silicon scaffolds supported the growth of primary cultures of bone marrow derived mesenchymal stromal cells (MSC) plated at high density for up to 21 days in culture with no significant loss of viability, assessed using Alamar Blue. Scanning electron micrographs confirmed a dense lawn of cells at 9 days of culture and the presence of MSC within the pores of the porous silicon scaffolds.

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 head0.051
GPT teacher head0.322
Teacher spread0.272 · 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

Citations13
Published2011
Admission routes2
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part ASame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207