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Record W3035212970 · doi:10.1116/6.0000203

Self-detached membranes with well-defined pore size, shape and distribution fabricated by underexposure photolithography

2020· article· en· W3035212970 on OpenAlexafffund
Tingjie Li, Peipei Jia, Kar Man Leung, Qiuquan Guo, Jun Yang

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMembranePhotoresistPhotolithographyWaferMicroporous materialPolymerLayer (electronics)Filtration (mathematics)Composite materialNanotechnology

Abstract

fetched live from OpenAlex

An underexposure photolithography method was developed to fabricate self-detached polymer micropore membranes with uniform pore size, shape, and arrangement. The key to this technique is to control and adjust the gradient of exposure dose projected into the film of photoresist. This new approach abandons sacrificial layers used in previous techniques. Negative SU-8 was chosen as an example photoresist to demonstrate its feasibility. Membranes with specially tailored sizes and shapes of micropores could be produced on diverse substrates. The coefficient of variation of pore size was only 1%, much lower than that for conventional microporous membranes. Moreover, due to self-detaching without a sacrificial layer, the membranes were flat and free of residual stress and deformation. This novel photolithographic approach opened a new avenue to manufacture high-quality membranes that could broaden the applications of microparticle filtration, separation, and sorting.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.178
Teacher spread0.173 · 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".

Quick stats

Citations3
Published2020
Admission routes2
Has abstractyes

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Same venueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and PhenomenaSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207