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Record W2149254526 · doi:10.1039/b912770d

Wet-etching of structures with straight facets and adjustable taper into glass substrates

2009· article· en· W2149254526 on OpenAlexafffund
Nikola Pekas, Qing Zhang, Matthieu Nannini, David Juncker

Bibliographic record

VenueLab on a Chip · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcGill University and Génome Québec Innovation CentreMcGill University
FundersMcGill University
KeywordsEtching (microfabrication)Materials scienceMicrofabricationFabricationIsotropyIsotropic etchingDissolutionHydrofluoric acidSubstrate (aquarium)Composite materialContact angleNanotechnologyLayer (electronics)OpticsMetallurgyChemistry

Abstract

fetched live from OpenAlex

Wet etching of glass by hydrofluoric acid is widely used in microfabrication, but is limited by the isotropic nature of the process that leads to rounded sidewalls and a 90 degrees angle between the etch front and the surface of the substrate. For many applications such as microvalving, or for further processing such as spin-coating, well controlled, gently sloping sidewalls are often preferred. Here, we present a new approach for forming straight facets and for adjusting the sidewall angle in wet-etched glass substrates by controlling the lateral dissolution of an etch mask during etching. The etch mask comprises a Ti-Au bilayer where Au serves to protect the Ti. During isotropic etching of glass by HF the Ti layer is etched away laterally at the same time, which leads to straight, gently sloping sidewalls. We introduce two methods for controlling the sidewall angle. The first one is based on adjusting the thickness of Ti which controls the lateral etch rate, and thus the angle; the thinner the Ti, the slower its lateral etch rate and the steeper the angle in the etched glass. The second method involves a cathodic bias applied to the Ti-Au etch mask which again regulates the dissolution rate of Ti; the more negative the bias the slower the lateral etch rate. Both methods offer accurate control of the sidewall angle over a wide range, can be readily integrated into existing fabrication processes, and will be particularly useful for making channels with trapezoidal cross-sections, valve seats with gentle profiles, or for patterning electrodes across and inside of microfluidic channels.

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 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 score0.043
Threshold uncertainty score0.395

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 head0.004
GPT teacher head0.193
Teacher spread0.188 · 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 teacher head, 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

Citations39
Published2009
Admission routes2
Has abstractyes

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