ArticleCrystal growth & design2010
A Stochastic Model for Nucleation Kinetics Determination in Droplet-Based Microfluidic Systems.
Article in Crystal growth & design, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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Who cites it
16 citing papers in PubMed, 142 citations in OpenAlex.
- An Integrated Experimental and Modeling Approach for Crystallization of Complex Biotherapeutics.Crystal growth & design · 2025Article
- Spontaneous Emergence of Homochiral Suspensions from Racemic Solutions via Stochastic Nucleation.Journal of the American Chemical Society · 2025Article
- Solubility and Metastable Zone Width Measurement of NaIndustrial & engineering chemistry research · 2025Article
- Developing a Vial-Scale Methodology for the Measurement of Nucleation Kinetics Using Evaporative Crystallization: A Case Study with Sodium Chloride.Crystal growth & design · 2025Article
- Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.Chemical science · 2024Article
- In-drop thermal cycling of microcrystal assembly for senescence control (MASC) with minimal variation in efficacy.Advanced functional materials · 2023Article
- Thermodynamics Explains How Solution Composition Affects the Kinetics of Stochastic Ice Nucleation.The journal of physical chemistry letters · 2023Article
- Conceptual Validation of Stochastic and Deterministic Methods To Estimate Crystal Nucleation Rates.Crystal growth & design · 2023Article
- Microfluidic Platform with Serpentine Geometry Providing Chaotic Mixing in Induction Time Experiments.Crystal growth & design · 2022Article
- Comparison of the Nucleation Kinetics Obtained from the Cumulative Distributions of the Metastable Zone Width and Induction Time Data.Molecules (Basel, Switzerland) · 2022Article
- Droplet-Based Evaporative System for the Estimation of Protein Crystallization Kinetics.Crystal growth & design · 2021Article
- Real-time monitoring of crystallization from solution by using an interdigitated array electrode sensor.Nanoscale horizons · 2021Article
- Crystallization of a nonreplicating rotavirus vaccine candidate.Biotechnology and bioengineering · 2021Article
- Article
- Fabrication of X-ray compatible microfluidic platforms for protein crystallization.Sensors and actuators. B, Chemical · 2012Article
- Determination of the phase diagram for soluble and membrane proteins.The journal of physical chemistry. B · 2010Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
The measured induction times in droplet-based microfluidic systems are stochastic and are not described by the deterministic population balances or moment equations commonly used to model the crystallization of amino acids, proteins, and active pharmaceutical ingredients. A stochastic model in the form of a Master equation is formulated for crystal nucleation in droplet-based microfluidic systems for any form of nucleation rate expression under conditions of time-varying supersaturation. An analytical solution is provided to describe the (1) time evolution of the probability of crystal nucleation, (2) the average number of crystals that will form at time t for a large number of droplets, (3) the induction time distribution, and (4) the mean, most likely, and median induction times. These expressions are used to develop methods for determining the nucleation kinetics. Nucleation kinetics are determined from induction times measured for paracetamol and lysozyme at high supersaturation in an evaporation-based high-throughput crystallization platform, which give low prediction errors when the nucleation kinetics were used to predict induction times for other experimental conditions. The proposed stochastic model is relevant to homogeneous and heterogeneous crystal nucleation in a wide range of droplet-based and microfluidic crystallization platforms.
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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.