Controlling fluid flow is a fundamental problem with applications from biomedicine to environmental engineering. Contemporary solutions combine electromechanical sensors, valves, and pumps; however, these are expensive and difficult to maintain. We report an autonomous flow control principle inspired by vascular transport in plants. Combining experiments on real and biomimetic tissues, we show that networks of cells linked by nonlinear valves permit the physical programming of a nearly arbitrary pressure drop versus flow rate relation. The nonlinearity is a consequence of fluid-structure interactions that allow a flexible element to selectively block the valve aperture. We report four applications: parallel connections that function as (i) a nonlinear flow controller, (ii) a constant flow controller, (iii) a reverse Ohm flow controller, and a serial connection that acts as (iv) a fluidic on-off switch.

Pulsating flows are common in many industrial, scientific, and natural fluidic systems. However, because the oscillatory flow component disturbs, e.g., optical measurements, deposition, or industrial processes, it is rarely desired. Moreover, in physiological conditions, pulsation control is desired. We explore the effect of using a plant-inspired nonlinear resistor to smooth the output of a peristaltic pump. Incorporating a 3D printed millifluidic biomimetic device reduces the oscillation amplitudes by 3 orders of magnitude, from 100% to 0.1% of the output flow rate. This represents a tenfold improvement relative to a purely linear resistive-capacitive approach. The observed flow kinetics compare well to a predictive model of peristaltic transport, allowing the further development of optimized fluid-handling systems driven by pulsatile flow. Applications to particle tracking and jetting are considered.

Plant metabolites comprise a wide range of extremely important chemicals. In many cases, like savory spices, they combine distinctive functional properties - deterrence against herbivory - with an unmistakable flavor. Others have remarkable therapeutic qualities, for instance, the malaria drug artemisinin, or mechanical properties, e.g., natural rubber. We present a breakthrough in plant metabolite extraction technology. Using a neural network, we teach a computer how to recognize metabolite-rich cells of the herbal plant rosemary (Rosmarinus officinalis) and automatically extract the chemicals using a microrobot while leaving the rest of the plant undisturbed. Our approach obviates the need for chemical and mechanical separation and enables the extraction of plant metabolites that currently lack proper methods for efficient biomass use. Computer code required to train the neural network, identify regions of interest, and control the micromanipulator is available as part of the supplemental material.