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Record W4408534857 · doi:10.1002/advs.202416176

High‐Performance Polymer‐derived Ceramics in LCD 3D Printing

2025· article· en· W4408534857 on OpenAlexafffund
Hamidreza Yazdani Sarvestani, Vahid Karamzadeh, Apoorv Kulkarni, Thomas Lacelle, Michael B. Jakubinek, Behnam Ashrafi

Bibliographic record

VenueAdvanced Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsMaterials scienceCeramicCuring (chemistry)Composite materialPyrolysisPolymer3D printingPorosityThermal stabilityFabricationCompressive strengthChemical engineering

Abstract

fetched live from OpenAlex

Abstract This study demonstrates the fabrication of high‐strength, lightweight polymer‐derived ceramics (PDCs) using silicon oxycarbide (SiOC)‐precursor formulations with liquid crystal display (LCD) vat photopolymerization (VPP) technology. Complex geometries, such as gyroids and stochastic lattices, are successfully 3D‐printed and evaluated under varying feature thicknesses and pyrolysis temperatures (800 °C and 1200 °C). Photorheology and thermogravimetric analysis (TGA) validated the efficient curing and pyrolysis characteristics of a printable precursor formulation based on vinyl methoxysiloxane homopolymer (VMM‐010), which demonstrated rapid curing, low viscosity, and compatibility with LCD 3D printing, ensuring precise layering and efficient resin removal. Micro‐CT scans confirmed its structural integrity and absence of voids, even in relatively thick components (≈3 mm). The VMM‐based PDC lattices achieved specific compressive strengths up to 9.4 MPa g⁻¹ cm 3 , a 50‐fold improvement over comparable lattices produced with a high‐porosity SiOC PDC, and exceptional high‐temperature stability, maintaining structural integrity after 2 h at 1500 °C. Compositional analysis revealed lower free carbon content and improved ceramic phase formation, driving the enhanced mechanical and thermal performance of the VMM‐based ceramic. These findings underscore the scalability, reliability, and superior performance of VMM formulations for LCD 3D printing, offering new possibilities for high‐performance ceramic applications in aerospace, automotive, and biomedical industries.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.220
Teacher spread0.215 · 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.

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

Citations12
Published2025
Admission routes2
Has abstractyes

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