Droplet Precise Self-Splitting on Patterned Adhesive Surfaces for Simultaneous Multidetection.

Area of Science:

• Microfluidics and surface science
• Analytical chemistry and biochemistry

Background:

• Precise microdroplet manipulation is crucial for high-throughput screening, combinatorial chemistry, and complex analyte recognition.
• Existing methods often face challenges in achieving independent microdroplet control and simultaneous detection.

Purpose of the Study:

• To develop a novel droplet self-splitting strategy for predictable microdroplet division and deposition.
• To enable independent manipulation and simultaneous multidetection of microdroplets without sample exchange.

Main Methods:

• Utilized a patterned adhesive surface to induce anisotropic liquid recoiling of impacting droplets.
• Investigated the influence of droplet Weber number and low-adhesive stripe width on self-splitting behavior.
• Developed a quantitative criterion to assess droplet self-splitting capability.

Main Results:

• Achieved predictable self-splitting of droplets into distinct microdroplets at preset locations.
• Demonstrated no observable matter exchange between the self-split microdroplets, allowing independent manipulation.
• Established a quantitative criterion for droplet self-splitting, correlating it with Weber number and stripe width.

Conclusions:

• The developed droplet self-splitting strategy offers precise control over microdroplet separation and localization.
• This technique facilitates simultaneous arrayed reactions and multiplexed analyte detection from a single sample droplet.
• The findings have significant implications for advancing high-throughput screening and analytical methodologies.