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Record W3179496977 · doi:10.1126/sciadv.abc5028

Damage-tolerant 3D-printed ceramics via conformal coating

2021· article· en· W3179496977 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

VenueScience Advances · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
FundersRice University
KeywordsConformal coatingCeramicCoatingMaterials scienceToughnessConformal map3d printedComposite materialDamage tolerancePolymerBiomedical engineeringComposite numberEngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

Ceramic materials, despite their high strength and modulus, are limited in many structural applications due to inherent brittleness and low toughness. Nevertheless, ceramic-based structures, in nature, overcome this limitation using bottom-up complex hierarchical assembly of hard ceramic and soft polymer, where ceramics are packaged with tiny fraction of polymers in an internalized fashion. Here, we propose a far simpler approach of entirely externalizing the soft phase via conformal polymer coating over architected ceramic structures, leading to damage tolerance. Architected structures are printed using silica-filled preceramic polymer, pyrolyzed to stabilize the ceramic scaffolds, and then dip-coated conformally with a thin, flexible epoxy polymer. The polymer-coated architected structures show multifold improvement in compressive strength and toughness while resisting catastrophic failure through a considerable delay of the damage propagation. This surface modification approach allows a simple strategy to build complex ceramic parts that are far more damage-tolerant than their traditional counterparts.

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)0.000
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 score0.785
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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