Abstract
Droplet microfluidics enables compartmentalized single-cell analysis, but conventional single-nozzle systems cannot efficiently process millions of cells needed to identify rare populations. This bottleneck is particularly important in antibody discovery, which requires deep B cell sampling while preserving the native pairing of antibody heavy and light chains. Parallelization increases throughput, but cumulative pressure losses cause flow nonuniformity across large networks. Here, we establish an exact hydrodynamic model for ladder-geometry networks and demonstrate that the maximum flow deviation scales quadratically with the number of parallel units [
O
(
N
2
)] rather than linearly [
O
(
N
)]. Computational fluid dynamics simulations for representative network geometries validate this scaling law. We extend it to hierarchical (
A
×
B
) networks and implement the resulting rules in a computational tool for forward evaluation and inverse channel design. Guided by this framework, we develop 100- and 360-generator chips that increase droplet generation rates by up to 53.8-fold while maintaining stable dripping, low droplet-size variation (CV ≤ 13.8%), and low mean cell occupancy (λ ≤ 0.02). Using a two-stage workflow, we construct natively paired antibody libraries from 1 million primary human B cells, achieving a 14-fold reduction in expensive reagent consumption relative to conventional methods. We further validate the encapsulation of 10 million cells within 40 min, increasing cell-processing and repertoire-sampling capacity by approximately one order of magnitude, crucial for the deep mining of immune repertoires. By overcoming fundamental scaling bottlenecks, this platform establishes a highly scalable, cost-efficient paradigm for massive-scale antibody library construction and broad microfluidic applications.