Beating Poisson encapsulation statistics using close-packed ordering

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Entry by Leon Furchtgott, APP 225 Fall 2010.

"Beating Poisson encapsulation statistics using close-packed ordering" Adam R. Abate, Chia-Hung Chen, Jeremy J. Agresti and David A. Weitz, Lab Chip 9 2628-2631 (2009).


This paper describes a new method for efficiently loading drops with discrete objects. The authors use deformable particles that are close packed to insert a controllable number of particles into each drop.


Drops loaded with discrete objects are used extensively in microfluidics experiments. Each drop serves as a picoliter vessel within which reactions can be performed, so using drops in microfluidic devices can be a key component for many high-throughput scientific applications. However all such applications require an efficient way of loading objects into individual drops. This so far has been quite difficult to achieve. Typically people dilute the materials and then encapsulate them into the drops at random. But this is very inefficient, since by Poissonian statistics a large number of drops will be completely empty.

In response to this inefficiency, several groups have attempted to find more efficient encapsulation methods such as using lasers to guide particles into drops or using inertial ordering of particles prior to encapsulation. However these methods are still difficult to use, slow, or not robust.


The authors developed a simple, robust method to load a controllable number of particles into every drop. The authors increase the volume fraction of the particles to the point that they are close-packed and naturally order into a regular spacing, providing a periodic flow of particles. By matching the periodicity of the drop formation to the particle flow, the authors achieve near perfect loading of a prescribed number of particles in each drop. By using slightly deformable particles, they avoid clogging, providing a robust, simple method for controlling particle loading in drops.

Fig. 1. (a) Schematic of encapsulation device. (b) Photomicrograph of particle encapsulation. The particles are injected at high volume fraction, causing them to order. Water is added in the first junction to space the particles prior to encapsulation. Oil is added in the second junction to form drops and encapsulate the particles. The scale bar denotes 50 µm.
Fig. 2. (a) Photomicrograph of close-packed gel particles flowing into encapsulation junction. The evenly spaced particles flow at a periodic rate into the encapsulation junction. The scale bar denotes 100 µm. (b) Average grayscale intensity in the box demarcated; each spike corresponds to a particle moving through the box. (c) Power spectrum of the intensity time trace, with a sharp peak at the average frequency of 1.5 kHz. The frequency as a function of time is plotted inset.
Fig. 3. Encapsulation of close-packed particles. Photomicrograph of encapsulation utilizing close-packed ordering and droplet triggering (a), close-packed ordering (b), and in which particles are disordered and encapsulated inefficiently (c). The scale bars denote 75 µm. (d) Probability distributions of the number of particles per drop.


These results are quite important to studies of collagen. They suggest that many previous measurements of the shape and size of collagen pores performed with CRM should be revisited with CFM. In fact, studies of other biopolymers should be careful of using CRM. Previous studies of fibrin using CRM coated the fibers with 5-nm gold beads. Perhaps this coating enhanced the reflectivity so that even fibers at high angles could be detected. The authors point to the need to develop tools to reverse or account for the anisotropy found in CRM data.

Relation to Soft Matter

This paper gives insight into a more experimental area of soft-matter physics than what we covered in our discussions of polymers. In particular it shows the great sensitivity of results about biopolymers to the imaging technique used and the dangers in using the wrong imaging technique for looking at polymers.