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Record W4285731724 · doi:10.3390/ma15144970

Photocatalytic Activity of Silicon Nanowires Decorated with PbS Nanoparticles Deposited by Pulsed Laser Deposition for Efficient Wastewater Treatment

2022· article· en· W4285731724 on OpenAlexaff
Faisal K. Algethami, Khaled Trabelsi, Anouar Hajjaji, Mohamed Ben Rabha, Lotfi Khezami, Mohamed R. Elamin, B. Bessaı̈s, My Alı El Khakani

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPhotocatalysisMaterials scienceNanocompositeNanotechnologyNanoparticleNanowireChemical engineeringSiliconEtching (microfabrication)CatalysisLayer (electronics)Organic chemistryMetallurgyChemistry

Abstract

fetched live from OpenAlex

The present work aims to study the photocatalytic properties of nanohybrids composed of silicon nanowires (SiNWs) decorated with PbS nanoparticles (NPs). The elaborated material was intended to be utilized in wastewater treatment. The SiNWs were elaborated from the Metal Assisted Chemical Etching route (MACE), while the PbS NPs were deposited at room temperature onto SiNWs using the pulsed laser deposition (PLD) technique. The influence of decorating SiNWs (having different lengths) with PbS-NPs on their structural, morphological, optoelectronic, and photocatalytic properties was scrutinized. PbS/SiNWs nanohybrids exhibited enhanced photocatalytic degradation towards Black Amido (BA) dye for 20 µm SiNWs length and 0.2% of BA volume concentration. These optimized conditions may insinuate that this nanocomposite-like structure is a promising efficient photocatalytic systems contender, cost-effective, and recyclable for organic compound purification from wastewaters.

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 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 score0.004
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

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