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Record W4387194788 · doi:10.1039/d3va00148b

Ion density-enhanced electrostatic precipitation using high voltage nanosecond pulses

2023· article· en· W4387194788 on OpenAlex
Boxin Zhang, Indu Aravind, Sisi Yang, Sizhe Weng, Bofan Zhao, Grace Johnson, Lucas Brown, Jason S. Olfert, Heejung Jung, Stephen B. Cronin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Science Advances · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Alberta
FundersAir Force Office of Scientific ResearchArmy Research OfficeDivision of ChemistryDivision of Chemical, Bioengineering, Environmental, and Transport SystemsCalifornia Department of TransportationNational Science FoundationU.S. Department of TransportationNational Commission for Science and Technology
KeywordsNanosecondElectrostatic precipitatorMaterials scienceCoaxialTransient (computer programming)IonPlasmaVoltagePulse (music)OptoelectronicsAnalytical Chemistry (journal)OpticsChemistryElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This study evaluates the beneficial effects of discharging nanosecond pulse transient plasma (NPTP) in a coaxial electrostatic precipitator for capturing nanoscale soot particles (∼50 nm) produced by an ethylene flame.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.018
GPT teacher head0.298
Teacher spread0.280 · 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