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Record W2324011670 · doi:10.1115/imece2008-68085

Mechanical and Electrical Properties of Micro-Lines Fabricated by Laser-Assisted Maskless Microdeposition

2008· article· en· W2324011670 on OpenAlexaff
Hamidreza Alemohammad, Ehsan Toyserkani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceLaserMicroscale chemistryNanoindentationMicrostructureComposite materialLaser power scalingElectrical resistivity and conductivityOptoelectronicsOptics

Abstract

fetched live from OpenAlex

The present paper is concerned with the analysis of the microstructural properties of silver micro-lines produced by Laser-Assisted Maskless Microdeposition (LAMM). LAMM is a laser based direct write method used in microscale layered manufacturing. In LAMM, liquid-suspended nanoparticles of a variety of materials are deposited in a layer-by-layer fashion and cured by a laser radiation. In this work, conductive micro-lines of silver with widths of 20 μm are fabricated, and their microstructures as well as electrical and mechanical properties are studied. Investigations show that the microstructures are affected by the laser power and the laser scanning velocity. To find the effect of laser processing parameters on the electrical performance of the samples, the conductivity of the samples are expressed in terms of the effective energy absorbed during laser radiation. It is shown that the conductivity of the sintered samples is increased up to 2 × 105 S.m−1 by raising the effective energy density. In addition, mechanical properties, i.e. modulus of elasticity of one of the fabricated samples are obtained using the nanoindentation test.

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 score0.014
Threshold uncertainty score0.405

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 head0.016
GPT teacher head0.197
Teacher spread0.181 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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