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3D printing families: laser, powder, and nozzle-based techniques

2023· book-chapter· en· W4320000229 on OpenAlex
Ali Mousavi, Elena Provaggi, Deepak M. Kalaskar, Houman Savoji

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

Venue3D Printing in Medicine · 2023
Typebook-chapter
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
Keywords3D printing3D bioprintingFabricationNanotechnologyMaterials scienceExtrusionComputer scienceTissue engineeringEngineeringBiomedical engineeringMedicineMetallurgy

Abstract

fetched live from OpenAlex

Three-dimensional (3D) printing is a process in which the raw material, in the form of powder, liquid, or solid filament, is deposited layer-by-layer to build up a physical 3D object. This chapter aims to provide a comprehensive overview of the 3D printing techniques suitable for medical applications. Here, we highlight the main innovations and breakthroughs achieved in the past three decades and categorize the additive manufacturing technologies available into resin-, powder-, extrusion-, and droplet-based systems. Additionally, this chapter discusses the recent technological advances and challenges in the bioprinting of tissue constructs and organs from a hardware perspective. Bioprinting has been investigated for the fabrication of several biological constructs, ranging from skin, bone, vascular, and cartilage tissues, as well as for the fabrication of high-throughput microarrays for toxicological analysis and drug screening. Future development in bioprinting techniques and bioink materials will certainly allow the fabrication of customized tissues and organs.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.031
GPT teacher head0.287
Teacher spread0.256 · 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