Abstract
Microfluidic chips are now being increasingly used
for fast and cost-effective implementation of biochemical protocols.
Sample preparation involves dilution and mixing of fluids in
certain ratios, which are needed for most of the protocols. On a
digital microfluidic biochip (DMFB), these tasks are usually automated
as a sequence of droplet mix-split steps. In the most widely
used (1:1) mix-split operation for DMFBs, two equal-volume
droplets are mixed followed by a split operation, which, ideally,
should produce two daughter-droplets of equal volume (balanced
splitting). However, because of uncertain variabilities in fluidic
operations, the outcome of droplet-split operations often becomes
erroneous, i.e., they may cause unbalanced splitting. As a result,
the concentration factor (CF) of each constituent fluid in the
mixture may become erroneous during sample preparation. All
traditional approaches aimed to recover from such errors deploy
on-chip sensors to detect possible volumetric imbalance, and
adopt either checkpointing-based rollback or roll-forward techniques.
Most of them suffer from significant overhead in terms
of assay-completion time, reactant-cost, and uncertainties in
termination due to randomly occurring split-errors. In this paper,
we propose a new approach to accurate dilution preparation on a
DMFB that is oblivious to volumetric split-errors. It does not need
any sensor and can handle multiple split-errors, deterministically.
The proposed method is customized for each target-CF based on
the criticality of split-errors in each mix-split step. Simulation
experiments on various test-cases demonstrate the effectiveness
of the proposed method.
Original language | English |
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Article number | 8429111 |
Number of pages | 14 |
Journal | IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD) |
DOIs | |
Publication status | Published - 2018 |
Fields of science
- 102 Computer Sciences
- 202 Electrical Engineering, Electronics, Information Engineering
JKU Focus areas
- Computation in Informatics and Mathematics
- Mechatronics and Information Processing
- Nano-, Bio- and Polymer-Systems: From Structure to Function