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Record W2488528762 · doi:10.1088/0963-0252/25/5/053002

Plasma–liquid interactions: a review and roadmap

2016· review· en· W2488528762 on OpenAlexaff
Peter Bruggeman, Mark J. Kushner, Bruce R. Locke, Han Gardeniers, W. G. Graham, David B. Graves, C.H.M. Hofman-Caris, Dragana Marić, Jonathan P. Reid, Elisa Ceriani, David Fernández Rivas, John E. Foster, S C Garrick, Yury Gorbanev, Satoshi Hamaguchi, Felipe Iza, Helena Jablonowski, Edita J. Klimova, Juergen F. Kolb, František Krčma, Petr Lukeš, Zdenko Machala, Iuri Marinov, Davide Mariotti, Selma Mededovic Thagard, Daisuke Minakata, Erik C. Neyts, Joanna Pawłat, Zoran Petrović, Rachel Pflieger, Stephan Reuter, D.C. Schram, S Schröter, Manabu Shiraiwa, Barbora Tarabová, P A Tsai, Jan R. R. Verlet, Thomas von Woedtke, K. Wilson, Kyuichi Yasui, G. N. Zvereva

Bibliographic record

VenuePlasma Sources Science and Technology · 2016
Typereview
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversity of Alberta
FundersDivision of PhysicsNational Science FoundationUniversiteit LeidenDivision of Chemical, Bioengineering, Environmental, and Transport SystemsEuropean Cooperation in Science and TechnologyFusion Energy SciencesUniversity of MinnesotaU.S. Department of Energy
KeywordsPlasmaMultidisciplinary approachPlasma chemistryNanotechnologyChemistryComputer scienceAerospace engineeringBiochemical engineeringMaterials sciencePhysicsEngineeringNuclear physicsPolitical science

Abstract

fetched live from OpenAlex

Abstract Plasma–liquid interactions represent a growing interdisciplinary area of research involving plasma science, fluid dynamics, heat and mass transfer, photolysis, multiphase chemistry and aerosol science. This review provides an assessment of the state-of-the-art of this multidisciplinary area and identifies the key research challenges. The developments in diagnostics, modeling and further extensions of cross section and reaction rate databases that are necessary to address these challenges are discussed. The review focusses on non-equilibrium plasmas.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.029
GPT teacher head0.339
Teacher spread0.310 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1,648
Published2016
Admission routes1
Has abstractyes

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