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Record W2017687709 · doi:10.2166/wst.2010.799

Arsenic oxidation by UV radiation combined with hydrogen peroxide

2010· article· en· W2017687709 on OpenAlex
Sabrina Sorlini, Francesca Gialdini, Mihaela I. Stefan

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

VenueWater Science & Technology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsTrojan Technologies (Canada)
Fundersnot available
KeywordsHydrogen peroxideArsenicEnvironmental chemistryChemistryPhotochemistryRadiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Arsenic is a widespread contaminant in the environment around the world. The most abundant species of arsenic in groundwater are arsenite [As(III)] and arsenate [As(V)]. Several arsenic removal processes can reach good removal yields only if arsenic is present as As(V). For this reason it is often necessary to proceed with a preliminary oxidation of As(III) to As(V) prior to the removal technology. Several studies have focused on arsenic oxidation with conventional reagents and advanced oxidation processes. In the present study the arsenic oxidation was evaluated using hydrogen peroxide, UV radiation and their combination in distilled and in real groundwater samples. Hydrogen peroxide and UV radiation alone are not effective at the arsenic oxidation. Good arsenic oxidation yields can be reached in presence of hydrogen peroxide combined with a high UV radiation dose (2,000 mJ/cm(2)). The quantum efficiencies for As(III) oxidation were calculated for both the UV photolysis and the UV/H(2)O(2) processes.

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 score0.020
Threshold uncertainty score0.665

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.002
GPT teacher head0.185
Teacher spread0.183 · 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