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Record W2109595472 · doi:10.1088/0957-4484/26/4/042001

Low-cost fabrication technologies for nanostructures: state-of-the-art and potential

2015· article· en· W2109595472 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

VenueNanotechnology · 2015
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNanolithographyNanotechnologyElectronicsPhotolithographyScalabilityMaterials scienceNanostructureLithographyComputer scienceFabricationEngineeringOptoelectronicsElectrical engineering

Abstract

fetched live from OpenAlex

In the last decade, some low-cost nanofabrication technologies used in several disciplines of nanotechnology have demonstrated promising results in terms of versatility and scalability for producing innovative nanostructures. While conventional nanofabrication technologies such as photolithography are and will be an important part of nanofabrication, some low-cost nanofabrication technologies have demonstrated outstanding capabilities for large-scale production, providing high throughputs with acceptable resolution and broad versatility. Some of these nanotechnological approaches are reviewed in this article, providing information about the fundamentals, limitations and potential future developments towards nanofabrication processes capable of producing a broad range of nanostructures. Furthermore, in many cases, these low-cost nanofabrication approaches can be combined with traditional nanofabrication technologies. This combination is considered a promising way of generating innovative nanostructures suitable for a broad range of applications such as in opto-electronics, nano-electronics, photonics, sensing, biotechnology or medicine.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.310

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 head0.008
GPT teacher head0.216
Teacher spread0.207 · 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