ArticleSensors (Basel, Switzerland)2020
An Artificial Neural Network Assisted Dynamic Light Scattering Procedure for Assessing Living Cells Size in Suspension.
Article in Sensors (Basel, Switzerland), 2020. 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
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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.
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Who cites it
6 citing papers in PubMed.
- Comparative Synthesis of Silver Nanoparticles: Evaluation of Chemical Reduction Procedures, AFM and DLS Size Analysis.Materials (Basel, Switzerland) · 2023Article
- Profiling Particles of Sahara Dust Settled on the Ground by a Simplified Dynamic Light Scattering Procedure and Sedimentation.International journal of environmental research and public health · 2023Article
- Ag Nanoparticles for Biomedical Applications-Synthesis and Characterization-A Review.International journal of molecular sciences · 2022Review
- On the Possibility of Designing an Advanced Sensor with Particle Sizing Using Dynamic Light Scattering Time Series Spectral Entropy and Artificial Neural Network.Sensors (Basel, Switzerland) · 2022Article
- An Advanced Sensor for Particles in Gases Using Dynamic Light Scattering in Air as Solvent.Sensors (Basel, Switzerland) · 2021Article
- Synthesis and Characterization of Amorphous Iron Oxide Nanoparticles by the Sonochemical Method and Their Application for the Remediation of Heavy Metals from Wastewater.Nanomaterials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Dynamic light scattering (DLS) is an essential technique used for assessing the size of the particles in suspension, covering the range from nanometers to microns. Although it has been very well established for quite some time, improvement can still be brought in simplifying the experimental setup and in employing an easier to use data processing procedure for the acquired time-series. A DLS time series processing procedure based on an artificial neural network is presented with details regarding the design, training procedure and error analysis, working over an extended particle size range. The procedure proved to be much faster regarding time-series processing and easier to use than fitting a function to the experimental data using a minimization algorithm. Results of monitoring the long-time variation of the size of the
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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.