Dynamic Manipulation of Droplets on Liquid-Infused Surfaces Using Photoresponsive Surfactant.

Fast and programmable transport of droplets on a substrate is desirable in microfluidic, thermal, biomedical, and energy devices. Photoresponsive surfactants are promising candidates to manipulate droplet motion due to their ability to modify interfacial tension and generate "photo-Marangoni" flow under light stimuli. Previous works have demonstrated photo-Marangoni droplet migration in liquid media; however, migration on other substrates, including solid and liquid-infused surfaces (LIS), remains an outstanding challenge. Moreover, models of photo-Marangoni migration are still needed to identify optimal photoswitches and assess the feasibility of new applications. In this work, we demonstrate 2D droplet motion on liquid surfaces and on LIS, as well as rectilinear motion in solid capillary tubes. We synthesize photoswitches based on spiropyran and merocyanine, capable of tension changes of up to 5.5 mN/m across time scales as short as 1.7 s. A millimeter-sized droplet migrates at up to 5.5 mm/s on a liquid, and 0.25 mm/s on LIS. We observe an optimal droplet size for fast migration, which we explain by developing a scaling model. The model also predicts that faster migration is enabled by surfactants that maximize the ratio between the tension change and the photoswitching time. To better understand migration on LIS, we visualize the droplet flow using tracer particles, and we develop corresponding numerical simulations, finding reasonable agreement. The methods and insights demonstrated in this study enable advances for manipulation of droplets for microfluidic, thermal and water harvesting devices.

CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning.

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.