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Record W6920500705 · doi:10.60692/kmqs0-w1v25

Progress towards wafer-scale fabrication based on gel casting technique for 1–3 randomised piezocomposite μUS linear array

2022· article· en· W6920500705 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsFabricationTransducerCastingAcoustic impedanceViscositySuspension (topology)3D printingShear (geology)Composite number

Abstract

fetched live from OpenAlex

Microultrasound (μUS) linear arrays operating at frequencies over 25 MHz have applications in high resolution biomedical imaging. 1–3 connectively piezoceramic – polymer composite ("piezocomposite") material is attractive for fabrication of these devices due to its high effective electromechanical coupling coefficient and low acoustic impedance for better acoustic matching between transducer and tissue. However, a major concern with this type of material comes from interference between the fundamental thickness-mode resonance and spurious modes, which is usually generated by wave propagation and reflection within the repetitive and symmetrical structure of classical piezocomposite. In general, a fine spatial scale is required of the material structure to suppress the spurious modes; however, the fabrication process is challenging using standard dice-and-fill methods at the fine scales required for high frequencies. A promising way to overcome this challenge is to manipulate the lateral geometry and spacing of the piezoceramic pillars with a random distribution. In this work, gel casting in association with a micromoulding technique has been developed for manufacturing 1–3 randomised piezocomposite active material for μUS linear arrays. 48 vol% solid loading of piezoceramic powder with 30 wt% Hydantoin resin content was employed to prepare a low viscosity aqueous suspension. Through varying powder size, it was found that the suspension with 1.22 µm powder had the highest viscosity, ~ 0.47 Pa.s, and a short gelation time, ~ 10 mins. However, all suspensions had viscosities less than 1 Pa.s at a shear rate of 100 s−1, indicating that they had good flowability. The green body samples showed mean flexural strength 49.7 ± 2.49 MPa. After piezocomposite fabrication with randomised pillars, surface planarisation was used to obtain reliable edge definition of photolithographically-defined electrodes. 20-element arrays with 50-μm element pitch were configured using a bilayer lift-off process. The 1–3 randomised piezocomposite demonstrated its capability to minimise the effects of spurious modes in the thickness mode frequency range, while the thickness resonances provided k33 = 0.67. Without a matching layer, the array produced a − 6 dB bandwidth of 38.4%- and − 20-dB pulse length of 0.26 μs. These results show that 1–3 randomised piezocomposite fabricated from gel-casting associated with a micromoulding technique is feasible for fabrication of μUS linear arrays and may offer a route to small wafer-scale production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.020
GPT teacher head0.237
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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