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Record W2513707435 · doi:10.1364/oe.24.020494

Picosecond laser-induced water condensation in a cloud chamber

2016· article· en· W2513707435 on OpenAlexaff
Haiyi Sun, Liu Y, Jingjing Ju, Ye Tian, Yafeng Bai, Yaoxiang Liu, Shengzhe Du, Cheng Wang, Tie-Jun Wang, Jiansheng Liu, See Leang Chin, Ruxin Li, Zhizhan Xu

Bibliographic record

VenueOptics Express · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLaser-Matter Interactions and Applications
Canadian institutionsUniversité Laval
FundersMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsCloud chamberMaterials scienceLaserAirflowPicosecondOpticsCondensationSnowSaturation (graph theory)Protein filamentLaser power scalingMeteorologyPhysicsNuclear physics

Abstract

fetched live from OpenAlex

We investigated water condensation in a laboratory cloud chamber induced by picosecond (ps) laser pulses at ~350 ps (800 nm/1-1000 Hz) with a maximum peak power of ~25 MW. The peak power was much lower than the critical power for self-focusing in air (~3-10 GW depending on the pulse duration). Sparks, airflow and snow formation were observed under different laser energies or repetition rates. It was found that weaker ps laser pulses can also induce water condensation by exploding and breaking down ice crystals and/or water droplets into tiny particles although there was no formation of laser filament. These tiny particles would grow until precipitation in a super-saturation zone due to laser-induced airflow in a cold region with a large temperature gradient.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.258
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2016
Admission routes1
Has abstractyes

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