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Record W4206448168 · doi:10.3390/molecules27020458

Biogenic Sulfur-Based Chalcogenide Nanocrystals: Methods of Fabrication, Mechanistic Aspects, and Bio-Applications

2022· review· en· W4206448168 on OpenAlex
Oscar P. Yanchatuña Aguayo, Lynda Mouheb, Katherine Villota Revelo, Paola A. Vásquez-Ucho, Prasad Pawar, Ashiqur Rahman, Clayton Jeffryes, Thibault Terencio, Si Amar Dahoumane

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

VenueMolecules · 2022
Typereview
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsNanotechnologyChalcogenideNanomaterialsBiomoleculeSulfurBiochemical engineeringNanoparticleMaterials scienceChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Bio-nanotechnology has emerged as an efficient and competitive methodology for the production of added-value nanomaterials (NMs). This review article gathers knowledge gleaned from the literature regarding the biosynthesis of sulfur-based chalcogenide nanoparticles (S-NPs), such as CdS, ZnS and PbS NPs, using various biological resources, namely bacteria, fungi including yeast, algae, plant extracts, single biomolecules, and viruses. In addition, this work sheds light onto the hypothetical mechanistic aspects, and discusses the impact of varying the experimental parameters, such as the employed bio-entity, time, pH, and biomass concentration, on the obtained S-NPs and, consequently, on their properties. Furthermore, various bio-applications of these NMs are described. Finally, key elements regarding the whole process are summed up and some hints are provided to overcome encountered bottlenecks towards the improved and scalable production of biogenic S-NPs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.324
Teacher spread0.272 · 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