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Record W3208898731 · doi:10.1016/j.isci.2021.103372

Scalable visible light 3D printing and bioprinting using an organic light-emitting diode microdisplay

2021· article· en· W3208898731 on OpenAlexfundno aff
Kavin Kowsari, Wonhye Lee, Seung‐Schik Yoo, Nicholas X. Fang

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMassachusetts Institute of TechnologyHarvard Medical School
KeywordsOLEDScalabilityMaterials scienceNanotechnologyBiocompatible materialBiocompatibilityDigital micromirror deviceDigital Light ProcessingComputer scienceOptoelectronicsBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

To address current unmet needs in terms of scalability and material biocompatibility for future photocrosslinking-based additive manufacturing technologies, emergent platform designs are in inexorable demand. In particular, a shift from the present use of cell-damaging UV light sources in light-based three-dimensional (3D) bioprinting methods demands new platforms. We adopted an organic light-emitting diode (OLED) microdisplay as a digital visible light modulator to create a 3D printing platform modality that offers scalability and multi-material capability while forgoing the need for UV photocrosslinking. We formulate biocompatible inks that are visible light-crosslinkable with relatively quick photoinitiation rates. We demonstrated successful attachment and rapid growth of primary human dermal fibroblast-adult (HDF-a) cells on biological substrates fabricated using the OLED platform. This platform incites new possibilities by providing a simple-yet-effective means for low-cost, high-throughput, and multi-material 3D fabrication of functional structures made of polymers, ceramic composites, and biomaterials.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.019
GPT teacher head0.279
Teacher spread0.259 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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