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Record W4386241090 · doi:10.1101/2023.08.28.555002

3D Printed Ti <sub>3</sub> C <sub>2</sub> T <sub>x</sub> MXene/PCL Scaffolds for Guided Neuronal Growth and Photothermal Stimulation

2023· preprint· en· W4386241090 on OpenAlexaff
Jianfeng Li, Payam Hashemi, Tianyi Liu, Ka My Dang, Michael G. K. Brunk, Xin Mu, Ali Shaygan Nia, Wesley D. Sacher, Xinliang Feng, Joyce K. S. Poon

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
FundersMax-Planck-Gesellschaft
KeywordsMaterials sciencePolycaprolactoneElectrospinningPhotothermal therapyCoatingNanotechnologyComposite materialPolymer

Abstract

fetched live from OpenAlex

Abstract The exploration of neural circuitry is essential for understanding the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in three dimensions continues to present a formidable challenge. Here, we present a method for engineering the growth of three-dimensional (3D) neural circuits with the capability for optical stimulation. We fabricated bioactive interfaces by melt electrospinning writing (MEW) of 3D printed 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 enabled optocapacitance-based neuronal stimulation due to illumination-induced local temperature increases. This work presents a strategy for additive manufacturing of neural tissues with optical control for 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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.026
GPT teacher head0.241
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

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