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Room Temperature Surface Bio-Sulfurisation via Natural Sativum Annilin and Bioengineering of Nanostructured CuS/Cu 2S

2023· article· en· W4367057504 on OpenAlexaff
G. G. Welegergs, N. Numan, Simiso Dube, Z.Y. Nuru, Nandipha L. Botha, Karen J. Cloete, Shohreh Azizi, I.G. Madiba, Mahmood Akbari, M.G. Tsegay, H.G. Gebretinsae, Ch. Mtshali, Z Khumalo, Fabian I. Ezema, Alain Krief, A. Gibaud, M. Henini, Mathapelo P. Seopela, Mohamed Chaker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversity of South AfricaAbdus Salam International Centre for Theoretical PhysicsUnited Nations Educational, Scientific and Cultural OrganizationOrganization for Women in Science for the Developing WorldNational Research FoundationRoyal Society
KeywordsCopperOrganosulfur compoundsMaterials scienceAdsorptionAllium sativumNanotechnologyChemical engineeringNanostructureMetallurgyChemistryOrganic chemistrySulfur

Abstract

fetched live from OpenAlex

In this contribution, we report, for the first time, on the surface bio-sulfurisation of metallic surfaces at room temperature via natural sativum annilin . More precisely, this bio-sulfurisation is validated on bioengineered nanostructured Cu 2-X S surfaces using natural organosulfur compounds emitted from Sativum allium L. as efficient sulfurisation chemical agents. It is validated that virgin copper surfaces can be sulfurised at room temperature without adding any extra chemical or physical processes. In addition to the validation of the green sulfurisation process of the copper surface, the bioengineered Cu 2-X S exhibited a multiscale 1-D tubular morphology with Cu 2-X S nanotubules and nanocones. Such a nanostructured Cu 2-X S surface exhibited an excessive optical selectivity, a superhydrophobicity response in addition to a remarkable site selective mercury adsorption.

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 score0.021
Threshold uncertainty score0.440

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 head0.010
GPT teacher head0.214
Teacher spread0.205 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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