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Record W3041528634 · doi:10.1016/j.matdes.2020.108957

Direct 3D-printing of phosphate glass by fused deposition modeling

2020· article· en· W3041528634 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

VenueMaterials & Design · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsPhosphate glassMaterials science3D printingExtrusionLayer (electronics)Deposition (geology)Fused deposition modelingPhosphateNanotechnologyComposite materialOptoelectronicsDoping

Abstract

fetched live from OpenAlex

Additive manufacturing of oxide glass enables on-demand, low-cost manufacturing of complex optical components for numerous applications, opening new opportunities to explore functionalities inaccessible otherwise. Here, we report a straightforward extrusion-based 3D-printing approach, deploying the fused deposition modeling (FDM) process, to produce optically transparent phosphate glasses with complex geometries and preserved structural and photoluminescence properties. Using a customized entry-level FDM desktop printer with a layer resolution of 100 μm, highly dense and transparent europium-doped phosphate glass structures can be fabricated from glass filaments pulled using a fiber-drawing tower from the parent glass preform. Combined with the suggested strategies for performance and quality improvement, professional-grade FDM printers can offer better layer resolutions. This direct approach for 3D-printing phosphate glass may open up new horizons not only for developing cutting-edge optical components but also for promoting new biomedical solutions upon making use of alternative biocompatible phosphate compositions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.702

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 communication0.0000.000
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 head0.025
GPT teacher head0.203
Teacher spread0.178 · 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