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Record W4413881109 · doi:10.1080/00958972.2025.2549040

Sustainable hydrogen production and antibiotic degradation using SeO <sub>2</sub> /Se nanocomposite synthesized via co-precipitation

2025· article· en· W4413881109 on OpenAlexaff
Abdelghani Serouti, Abderrhmane Bouafia, Chaima Salmi, Imen Kir, Khansaa Al‐Essa, Salah Eddine Laouini, Hamdi Ali Mohammed, Fahad A. Alharthi, Abdullah Al Souwaileh, Tomasz Trzepiecińsk

Bibliographic record

VenueJournal of Coordination Chemistry · 2025
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsChemistryNanocompositeDegradation (telecommunications)Hydrogen productionPrecipitationHydrogenChemical engineeringNuclear chemistryOrganic chemistryMeteorology

Abstract

fetched live from OpenAlex

This study reports the synthesis, characterization, and photocatalytic performance of SeO2/Se nanocomposites (NC) for sustainable hydrogen production and antibiotic degradation. The nanocomposites were synthesized via a co-precipitation method, yielding stable structures with a bandgap energy of 2.34 eV and an absorption peak at 315 nm. Structural and morphological analyses confirmed a crystalline phase with an average crystallite size of 20.45 ± 1.23 nm and irregular particle morphology averaging 34 nm. Photocatalytic evaluation demonstrated a hydrogen production rate of 654 µmol/g under optimal conditions (50 mg catalyst, pH 10) under simulated sunlight. In parallel, the nanocomposites achieved 99.9% degradation of amoxicillin within 120 min, following pseudo-first-order kinetics with a rate constant of 0.02249 min−1. Reactive oxygen species were identified as the primary drivers of the photocatalytic degradation process. These results highlight the dual functionality of SeO2/Se NC, offering a promising route for simultaneous clean energy production and environmental remediation. The findings contribute to the development of multifunctional photocatalytic materials for sustainable energy and water purification technologies.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.274
Teacher spread0.262 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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