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Record W2807569471 · doi:10.1002/admt.201800068

3D‐Printed Microfluidic Devices for Materials Science

2018· article· en· W2807569471 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Materials Technologies · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlexander von Humboldt-Stiftung
KeywordsFabricationMicrofluidics3d printedNanotechnology3D printingMaterials scienceRapid prototypingExtrusionManufacturing engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Abstract Microfluidics (MFs) has emerged as a valuable and in some cases, unique platform for the synthesis and assembly of inorganic and polymeric materials. 3D printing enables time‐, labor‐, and cost‐efficient prototyping of MF devices, their durability during operation, and the ability to implement complex designs, however the applications of 3D‐printed MF devices in materials science are still in their infancy. Here, the synthesis and assembly of a diverse range of materials, including spraying‐based synthesis of inorganic NPs and conductive filaments, extrusion‐based fabrication of hydrogel fibers and sheets, and the preparation of composite solid films are reported. The properties are examined and potential applications of these materials are shown, the advantages of material fabrication in 3D‐printed MF devices are highlighted, and the directions for their further development are identified.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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