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Record W3212429971 · doi:10.1021/acs.est.1c01724

Enhancing Interface Reactions by Introducing Microbubbles into a Plasma Treatment Process for Efficient Decomposition of PFOA

2021· article· en· W3212429971 on OpenAlex
Han Zhang, Pan Li, Zhang Ai, Zhuyu Sun, Jinxia Liu, Paul Héroux, Yanan Liu

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 & Technology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsMcGill University
FundersScience and Technology Commission of Shanghai MunicipalityMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsPerfluorooctanoic acidChemistryEffluentEnvironmental chemistryWater treatmentDecompositionWastewaterSewage treatmentTap waterDegradation (telecommunications)Environmental engineeringOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

. Aside from fluoride, PFOA was degraded to a range of short-chain perfluoroalkyl acids and, to a minor extent, at least 20 other fluorinated transformation products. PFOA degradation mechanisms were proposed, including decarboxylation, hydroxylation, hydrogenation reduction, and defluorination reactions. Real water matrices (groundwater, tap water, wastewater effluent, and surface water) showed moderate impact on treatment outcomes, demonstrating the robustness of the treatment process. The study demonstrated an environmentally friendly nonthermal plasma technology for effective PFOA degradation.

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 score0.032
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.007
GPT teacher head0.288
Teacher spread0.281 · 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