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Record W4307451423 · doi:10.26434/chemrxiv-2022-sgb2d

Photocatalytic treatment of organoselenium in synthetic mine-impacted effluents

2022· preprint· en· W4307451423 on OpenAlexaff
Jeffrey N. Martin, Tia Chai, Timothy Michael Carter Leshuk, Zachary T. Young, Frank Gu

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSeleniumPhotocatalysisBioaccumulationDaphnia magnaEnvironmental chemistrySelenateChemistryHazardous wasteToxicityCatalysisBiologyOrganic chemistryEcology

Abstract

fetched live from OpenAlex

Biological selenium reduction processes are commonly employed as the best available technology (BAT) for selenium removal, however, as a by-product they produce trace amounts of organoselenium compounds with orders of magnitude greater bioaccumulation potential and toxicity. Here we assessed buoyant photocatalysts (BPCs) as a potential passive advanced oxidation process (P-AOP) for organoselenium treatment. Using a synthetic mine-impacted water solution, spiked with selenomethionine (96 µg/L) as a representative organoselenium compound, photocatalysis with BPCs fully eliminated selenomethionine to <0.01 µg/L with conversion to selenite and selenate. A theoretical reaction pathway was inferred, and a kinetics model developed to describe the treatment trends and intermediates. Given the known toxic responses of Lepomis macrochirus and Daphnia magna to organoselenium, it was estimated that photocatalysis could effectively eliminate organoselenium acute toxicity within a UV dose of 8 kJ/L (1-2 days solar equivalent exposure), by transformation of selenomethionine to less hazardous oxidized Se species. Solar photocatalysis may therefore be a promising passive treatment technology for selenium-impacted mine water management.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.036
GPT teacher head0.294
Teacher spread0.258 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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