Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Non-equilibrium in the Cell

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automated synthesis of InSb quantum dots with improved batch-to-batch reproducibility via kinetically matched co-reduction.

Nature communications·2026

Same author

Leveling Up Upconverting Nanoparticles with Machine Learning.

Accounts of chemical research·2026

Same author

Synthetic Control over the Electron-Beam Stability of Upconverting Nanoparticles.

Nano letters·2026

Same author

Photon Avalanching Nanoparticles.

Accounts of chemical research·2026

Same author

CRAGE-RB-PI-seq reveals transcriptional dynamics of plant-associated bacteria during root colonization.

Nature communications·2026

Same author

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs.

Nature computational science·2025

Same journal

Intrinsic Superconducting Gap in Bilayer KCa<sub>2</sub>Fe<sub>4</sub>As<sub>4</sub>F<sub>2</sub> and Decoupled Monolayer FeAs.

Nano letters·2026

Same journal

Programmable Hydrogen-Assisted Chemical Vapor Deposition Growth and Bipolar Transport in Two-Dimensional MoO<sub>2</sub> Nanoflakes.

Nano letters·2026

Same journal

A Curvature-Modulated Strategy for Single-Atom Catalysts toward Reciprocal Regulation in Li-S Batteries.

Nano letters·2026

Same journal

Vacuum Pyrolysis Engineered CoSb/C Scaffold for Sodium Metal Anodes with Sodiophilic and Superionic Interphase.

Nano letters·2026

Same journal

Hexagonal SiGe Quantum Dots in Nanowires.

Nano letters·2026

Same journal

Monolithic Axial InGaAs Quantum Dot Emitters in GaAs-Based Nanowires via Sb-Mediated Facet Engineering.

Nano letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Millifluidics for Chemical Synthesis and Time-resolved Mechanistic Studies

Millifluidics for Chemical Synthesis and Time-resolved Mechanistic Studies

Published on: November 27, 2013

Automated Nanocrystal Synthesis: Lessons from 25 Years of Robots, Microfluidics, and Machine Learning.

¹The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.

|December 23, 2025

Summary

This summary is machine-generated.

Automated synthesis techniques, including microfluidic reactors and robotic workflows, have advanced colloidal nanocrystal production over 25 years. These methods improve reproducibility and enable complex nanoparticle synthesis, accelerating materials discovery.

Keywords:

Nanocrystals automation machine learning microfluidics nanoparticles

More Related Videos

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

Published on: May 10, 2018

Related Experiment Videos

Last Updated: Jun 23, 2026

Millifluidics for Chemical Synthesis and Time-resolved Mechanistic Studies

Millifluidics for Chemical Synthesis and Time-resolved Mechanistic Studies

Published on: November 27, 2013

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

A Modular Microfluidic Technology for Systematic Studies of Colloidal Semiconductor Nanocrystals

Published on: May 10, 2018

Area of Science:

Materials Science
Chemical Engineering
Nanotechnology

Background:

Colloidal nanocrystal synthesis has historically faced challenges in reproducibility and scalability.
The demand for high-quality nanoparticles with complex structures necessitates advanced synthesis methods.

Purpose of the Study:

To review the evolution of automated synthesis techniques for colloidal nanocrystals.
To discuss the impact of automation on nanocrystal research and discovery.
To provide insights for future advancements in automated nanomaterial synthesis.

Main Methods:

Review of microfluidic reactors and robotic workflows in nanocrystal synthesis.
Analysis of data generation capabilities of automated systems for model validation and machine learning.
Discussion of challenges, limitations, and lessons learned in implementing automated synthesis.

Main Results:

Automation enhances reproducibility, facilitates rapid screening, and optimizes material properties.
Automated systems generate robust datasets crucial for physical model validation and chemical mechanism support.
Modern tools enable the synthesis of complex heterostructure nanoparticles and support autonomous experimentation.

Conclusions:

Automated synthesis is pivotal for accelerating the discovery of novel nanocrystals.
Machine learning-guided tools are increasingly important for directing future research.
Continued development of automated and ML-guided systems will drive innovation in nanotechnology.