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Record W4409785398 · doi:10.1016/j.apsusc.2025.163258

Raman spectroscopic characterization of femtosecond Laser ablation of Silicon in air and liquid medium

2025· article· en· W4409785398 on OpenAlexafffund
Melika Afshar, Jaspreet Walia, Pierre Berini, Fabio Variola, V. R. Bhardwaj

Bibliographic record

VenueApplied Surface Science · 2025
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsNexen (Canada)University of Ottawa
FundersUniversity of Ottawa
KeywordsCharacterization (materials science)Raman spectroscopyMaterials scienceFemtosecondSiliconLaser ablationLaserAblationAnalytical Chemistry (journal)OpticsNanotechnologyOptoelectronicsChemistryChromatography

Abstract

fetched live from OpenAlex

Laser ablation of materials is mostly conducted in ambient air leading to high precision localized morphological and structural changes along with some redeposition of debris on the surface. While material ablation in a liquid medium can reduce thermal damage and debris, the fluid properties can influence morphological and structural changes. In this article, using Raman spectroscopy, we study structural changes induced by a single femtosecond laser pulse in silicon (100) immersed in water, isopropanol and glycerin and compare with air for a Gaussian and vortex beam carrying orbital angular momentum. We show that the extent of recrystallization after laser interaction is higher in air. In liquids, the laser modified regions exhibited greater percentage of poly-crystalline and amorphous phases. • Amorphous and polycrystalline phases in ablated silicon is more in liquids than air • Recrystallization of laser irradiated silicon is higher in air. • Phase change in isopropanol is lower compared to water and glycerin. • Vortex pulse causes more phase change in silicon in water than Gaussian pulse.

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 score0.033
Threshold uncertainty score0.280

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 head0.005
GPT teacher head0.221
Teacher spread0.217 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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