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Record W4366976525 · doi:10.1088/1361-6528/acd00a

Probe sonication-assisted rapid synthesis of highly fluorescent sulfur quantum dots

2023· article· en· W4366976525 on OpenAlexafffund
Sachin Kadian, Narendra Chaulagain, Naveen Joshi, Kazi M. Alam, Kai Cui, Karthik Shankar, Gaurav Manik, Roger J. Narayan

Bibliographic record

VenueNanotechnology · 2023
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersScience and Engineering Research BoardNational Research Council CanadaIndian Institute of Technology RoorkeeUniversity of Alberta
KeywordsMaterials scienceNanomaterialsNanotechnologySonicationFluorescenceQuantum dotPhotoluminescenceAqueous solutionSulfurChemical engineeringOptoelectronicsOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

A new type of heavy-metal free single-element nanomaterial, called sulfur quantum dots (SQDs), has gained significant attention due to its advantages over traditional semiconductor QDs for several biomedical and optoelectronic applications. A straightforward and rapid synthesis approach for preparing highly fluorescent SQDs is needed to utilize this nanomaterial for technological applications. Until now, only a few synthesis approaches have been reported; however, these approaches are associated with long reaction times and low quantum yields (QY). Herein, we propose a novel optimized strategy to synthesize SQDs using a mix of probe sonication and heating, which reduces the reaction time usually needed from 125 h to a mere 15 min. The investigation employs cavitation and vibration effects of high energy acoustic waves to break down the bulk sulfur into nano-sized particles in the presence of highly alkaline medium and oleic acid. In contrast to previous reports, the obtained SQDs exhibited excellent aqueous solubility, desirable photostability, and a relatively high photoluminescence QY up to 10.4% without the need of any post-treatment. Additionally, the as-synthesized SQDs show excitation-dependent emission and excellent stability in different pH (2-12) and temperature (20 °C-80 °C) environments. Hence, this strategy opens a new pathway for rapid synthesis of SQDs and may facilitate the use of these materials for biomedical and optoelectronic applications.

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 categoriesInsufficient 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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.026
GPT teacher head0.246
Teacher spread0.220 · 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

Citations29
Published2023
Admission routes2
Has abstractyes

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