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Record W4395068988 · doi:10.1038/s43246-024-00503-6

3D printed titanium carbide MXene-coated polycaprolactone scaffolds for guided neuronal growth and photothermal stimulation

2024· article· en· W4395068988 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommunications Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of Toronto
FundersMax-Planck-Gesellschaft
KeywordsPolycaprolactonePhotothermal therapyMaterials scienceTitanium carbideTitaniumStimulationBiomedical engineeringNanotechnologyComposite materialMetallurgyNeuroscienceMedicineBiologyPolymer

Abstract

fetched live from OpenAlex

Abstract The exploration of neural circuitry is paramount for comprehending the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in 3D remains a formidable challenge. Here, we introduce a method for engineering the growth of 3D neural circuits with the capability for optical stimulation. We fabricate bioactive interfaces by melt electrospinning writing (MEW) 3D polycaprolactone (PCL) scaffolds followed by coating with titanium carbide (Ti 3 C 2 T x MXene). Beyond enhancing hydrophilicity, cell adhesion, and electrical conductivity, the Ti 3 C 2 T x MXene coating enables optocapacitance-based neuronal stimulation, induced by localized temperature increases upon illumination. This approach offers a pathway for additive manufacturing of neural tissues endowed with optical control, facilitating functional tissue engineering and neural circuit computation.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.008
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.048
GPT teacher head0.323
Teacher spread0.276 · 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