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Record W7117323989 · doi:10.1002/smtd.202501468

Methods of Soft PDMS Microlens Arrays Fabrication via Air‐Expansion‐Induced Molding with 3D‐Printed Templates

2025· article· en· W7117323989 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

VenueSmall Methods · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced optical system design
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsMicrolensPhotoresistPolydimethylsiloxaneFabricationMolding (decorative)PhotolithographySoft lithographyMoldMicrofluidicsLens (geology)

Abstract

fetched live from OpenAlex

Conventional methods for microlens array (MLA) fabrication suffer from high costs, long prototyping time, and limited geometric control. To address these problems, this study introduces an air-expansion-induced molding method for soft lens array fabrication. This method works by heating the air trapped in 3D-printed photoresist microcavities. The photoresist becomes convex due to the thermal expansion of the trapped air and is then solidified after UV curing as a mold for Polydimethylsiloxane (PDMS) casting. The deformation of the photoresist is theoretically analyzed and experimentally verified. It is found that the fabricated MLAs exhibit sub-10 nm surface roughness and their curvature scales with both microcavity depth and temperature. The MLAs achieve an imaging resolution of 228.1 line pairs per millimeter and enable multi-focal plane imaging of microalgae in a microfluidic chip.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.190
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.334
Teacher spread0.305 · 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