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Related Experiment Video

Updated: Jun 10, 2026

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
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Simultaneous optimization of assembly time and yield in programmable self-assembly.

Maximilian C Hübl1, Carl P Goodrich1

  • 1Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria.

The Journal of Chemical Physics
|February 24, 2026
PubMed
Summary
This summary is machine-generated.

Optimizing binding energies and particle concentrations in self-assembly significantly enhances assembly speed and quality. This approach accelerates slow, nondeterministic systems, offering a powerful tool for programmable matter design.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Rational design of self-assembly requires understanding equilibrium and kinetics.
  • Assembly kinetics, particularly in semi-addressable systems, present a significant theoretical challenge.
  • Nondeterministic binding and off-target structures impede efficient self-assembly.

Purpose of the Study:

  • To investigate sculpting assembly outcome and kinetics via binding energies and particle concentrations.
  • To optimize the trade-off between assembly speed and quality in programmable self-assembly.
  • To address challenges in semi-addressable self-assembly systems.

Main Methods:

  • Formulating self-assembly as a complex reaction network.
  • Calculating and optimizing binding energies and particle concentrations.
  • Analyzing the trade-off between assembly speed and yield.

Main Results:

  • Parameter optimization can accelerate self-assembly by orders of magnitude without reducing yield.
  • Nondeterministic systems show the largest speedups, sometimes exceeding fully addressable designs.
  • Simultaneous optimization of kinetics and yield is achievable.

Conclusions:

  • Binding energies and particle concentrations offer an underexplored design space for self-assembly.
  • Semi-addressable self-assembly designs can be faster, cheaper, and yield better results.
  • Parameter optimization is crucial for programmable self-assembly, providing tools for kinetics and yield enhancement.