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Precipitate Formation and Particle Size Control

Precipitate Formation and Particle Size Control

In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...

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Related Experiment Video

Updated: Sep 11, 2025

Improving Reproducibility to Meet Minimal Information for Studies of Extracellular Vesicles 2018 Guidelines in Nanoparticle Tracking Analysis

Improving Reproducibility to Meet Minimal Information for Studies of Extracellular Vesicles 2018 Guidelines in Nanoparticle Tracking Analysis

Published on: November 17, 2021

Data-driven optimization of nanoparticle size using the prediction reliability enhancing parameter (PREP).

Seyed Saeid Tayebi¹, Nate Dowdall¹, Todd Hoare¹

¹Department of Chemical Engineering, McMaster University, 1280 Main St. W., Hamilton, Ontario, Canada L8S 4L7. hoaretr@mcmaster.ca.

|August 14, 2025

Summary

This summary is machine-generated.

Achieving desired nanoparticle size is challenging and costly. This study introduces the Prediction Reliability Enhancing Parameter (PREP) to precisely control nanoparticle size, significantly reducing experimental iterations for biomaterial optimization.

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Last Updated: Sep 11, 2025

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Area of Science:

Biomaterials Science
Nanotechnology
Chemical Engineering

Background:

Nanoparticle size critically influences biodistribution, cellular uptake, and therapeutic efficacy.
Current experimental methods for nanoparticle size control are often time-consuming and costly due to numerous iterations.
Precise control over particle size and distribution is essential for effective therapeutic applications.

Purpose of the Study:

To address the challenge of achieving precise nanoparticle size control.
To implement and evaluate the Prediction Reliability Enhancing Parameter (PREP) for streamlining nanoparticle design.
To demonstrate the efficacy of PREP in reducing experimental iterations for targeted nanoparticle properties.

Main Methods:

Applied the data-driven modeling approach, Prediction Reliability Enhancing Parameter (PREP).
Utilized PREP to predict and control particle sizes of two distinct nanoparticle types: thermoresponsive microgels and polyelectrolyte complexes.
Fabricated microgels via precipitation polymerization and polyelectrolyte complexes via charge-driven self-assembly.

Main Results:

PREP enabled efficient and precise particle size control for both nanoparticle types.
Target nanoparticle sizes and properties were achieved in only two experimental iterations for each case.
Demonstrated significant reduction in experimental workload for nanoparticle fabrication.

Conclusions:

PREP effectively reduces the number of experimental iterations required for nanoparticle size control.
This data-driven approach streamlines experimental workflows in biomaterials optimization.
PREP offers a promising strategy for efficient development of nanoparticles with desired characteristics.