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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 OpenAlexaff
P. S. Ganesh Subramanian, J Ananthanarasimhan, P. Leelesh, Rao Harsha, Anand M. Shivapuji, Pierre‐Luc Girard‐Lauriault, Lakshminarayana Rao

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.

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 score0.001
Threshold uncertainty score0.002

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 head0.026
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

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

Citations41
Published2021
Admission routes1
Has abstractyes

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