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Record W4412470765 · doi:10.1021/acsaenm.5c00400

Scalable and Continuous Generation of Plasma-Treated Solutions Designed for Healthcare Applications

2025· article· en· W4412470765 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

VenueACS Applied Engineering Materials · 2025
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsWomen's Health Research Institute
FundersMinisterium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg
KeywordsScalabilityPlasmaComputer scienceHealth careMaterials sciencePhysicsOperating system

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The clinical application of physical-plasma-treated solutions (PTS) within the human body has become increasingly encouraging for many intracorporal disorders where the expected effectiveness of direct plasma application is opposed by limited accessibility. In order to increase the likelihood of intracorporal application of PTS, the interaction of more sophisticated technologies and materials is urgently required to optimize the scalable, sterile, and continuous transfer of biologically reactive species (reactive oxygen and nitrogen species, RONS) into liquids. In this study, we present an innovative fluidic system characterized by the separation of plasma discharge and liquid by a semipermeable membrane to achieve these goals. In addition to the in-depth characterization of membrane–plasma interactions and RONS transfer to different solutions, the biomedical efficacy of the generated PTS was investigated in vitro. Our findings demonstrate the functionality of a transmembraneous RONS transport using a semipermeable membrane and the potential of the system to treat tumors within the human body.

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 score0.106
Threshold uncertainty score0.483

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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