ArticleNanoscale2022
Designing the ultrasonic treatment of nanoparticle-dispersions
Article in Nanoscale, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Application ofPolymers · 2026Article
- Evaluation of the biological activities of Bacillus cereus-synthesized titanium dioxide nanoparticles.Applied microbiology and biotechnology · 2026Article
- Characterization and Antimicrobial Assessment of Cadmium Sulfide Nanoparticles.International journal of molecular sciences · 2025Article
- Hybrid Extracellular Vesicles With Combined Functional Properties From Mesenchymal Stem Cells and Astrocytes for Targeted Neurodegenerative Disease Applications.Journal of extracellular vesicles · 2025Article
- Immunocyte membrane-derived biomimetic nano-drug delivery system: a pioneering platform for tumour immunotherapy.Acta pharmacologica Sinica · 2024Review
- Correct labelling? A full analytical pathway for silica and titania particles in food products.Current research in food science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasonication is a widely used and standardized method to redisperse nanopowders in liquids and to homogenize nanoparticle dispersions. One goal of sonication is to disrupt agglomerates without changing the intrinsic physicochemical properties of the primary particles. The outcome of sonication, however, is most of the time uncertain, and quantitative models have been beyond reach. The magnitude of this problem is considerable owing to fact that the efficiency of sonication is not only dependent on the parameters of the actual device, but also on the physicochemical properties such as of the particle dispersion itself. As a consequence, sonication suffers from poor reproducibility. To tackle this problem, we propose to involve machine learning. By focusing on four nanoparticle types in aqueous dispersions, we combine supervised machine learning and dynamic light scattering to analyze the aggregate size after sonication, and demonstrate the potential to improve considerably the design and reproducibility of sonication experiments.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.