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Record W3134578875 · doi:10.1063/5.0039253

Plasma-activated water from DBD as a source of nitrogen for agriculture: Specific energy and stability studies

2021· article· en· W3134578875 on OpenAlex

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

VenueJournal of Applied Physics · 2021
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcGill University
FundersIndian Institute of Science
KeywordsDielectric barrier dischargeChemistryNitrogenPlasmaOxygenReactive nitrogen speciesEnvironmental chemistryReactive oxygen speciesChemical engineeringAnalytical Chemistry (journal)Organic chemistryBiochemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Successful application of plasma-activated water (PAW) as an alternate source of nitrogen for agricultural application requires low specific energy consumption. This work reports on a dielectric barrier discharge (DBD) plasma reactor for the generation of PAW having low specific energy (SE) consumption. The SE to produce N in PAW was 3.26 GJ/kg of N, which is 68% lower than the lowest value reported to date for DBD-PAW systems. The PAW generated was characterized for its physico-chemical parameters, most of which showed a linear increase with activation time (ta). The concentration of hydrogen ion and that of the nitrate, which is the desired product for agricultural application, remained stable for four weeks in the PAW. The results indicate that minimal reactive oxygen species was formed in the plasma zone and only reactive nitrogen species (RNS) was formed confirming selectivity toward RNS.

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)0.000
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 score0.035
Threshold uncertainty score0.309

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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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