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Record W1591895175 · doi:10.1002/9783527646821.ch5

Nanomaterials: Laser‐Induced Nano/Microfabrications

2012· other· en· W1591895175 on OpenAlexaff
A. Y. Vorobyev, Chunlei Guo, H. Sakata, Moriaki Wakaki, Qing Wang, V. Sametoglu, Ying Y. Tsui, Matthias Nagel, Thomas Lippert

Bibliographic record

VenueNanomaterials · 2012
Typeother
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLaserNanomaterialsFemtosecondMaterials scienceNanotechnologySubstrate (aquarium)Nano-FabricationOptoelectronicsOpticsComposite materialPhysics

Abstract

fetched live from OpenAlex

In conjunction with the increasing availability of cost-efficient laser units during the recent years, laser-based micromachining techniques have been developed as an indispensable industrial instrument of "tool-free" high-precision manufacturing techniques for the production of miniaturized devices made of nearly every type of materials. Laser cutting and drilling, as well as surface etching, have grown meanwhile to mature standard methods in laser micromachining applications where a well-defined laser beam is used to remove material by laser ablation. As an accurately triggerable nonmechanical tool, the ablating laser beam directly allows a subtractive direct-write engraving of precise microscopic structure patterns on surfaces, such as microchannels, grooves, and well arrays, as well as for security features. Therefore, laser direct-write (LDW) techniques imply originally a controlled material ablation to create a patterned surface with spatially resolved three-dimensional structures, and gained importance as an alternative to complementary photolithographic wet-etch processes. However, with more extended setups, LDW techniques can also be utilized to deposit laterally resolved micropatterns on surfaces, which allows, in a general sense, for the laser-assisted "printing" of materials.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.003

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations8
Published2012
Admission routes1
Has abstractyes

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