Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks [1][2][3][4] . Nonenzymatic networks could in principle support neuromorphic architectures, and seminal proof-of-principles have been reported 5,6 . However, leakages, as well as issues with sensitivity, speed, nonlinearities and preparation, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network (e.g. nonlinear partitioning of a concentration space) remain elusive. Here we introduce DNA-encoded enzymatic neurons with tunable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of the neurons to compute various majority functions on 10 bits. We then compose neurons into a twolayer network, and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.Synthetic DNA has emerged as a versatile polymer to store and process information at the molecular scale. It has powered a rich library of computational molecular devices ranging from logic circuits 7-10 to self-assembling automata 11 . Departing from the biological model of computation, most DNA computing devices imitate the Boolean paradigm of electronics. However, their computing power has fallen short of the exponential growth of Moore's law: their size has been plateauing at ~5-10 logic gates for a decade 8 . In parallel, various groups have started looking at the brain, rather than the CPU, as an inspiration for computing with molecules 2,12,3-6 . This is because neuronal and chemical networks share striking similarities: massively parallel and recurrent architectures, analog and asynchronous operation, fault-tolerant and redundant computations (Figure S12)In 2018, Lopez et al. reported a DNA-based linear classifier 6 that performs all of its computations with a nonenzymatic mechanism: toehold-mediated strand displacement 13 . Using similar DNA-only mechanisms on many more inputs and taking inspiration from competitive neural networks 2,3 , Cherry and Qian reported in a tour de force a DNA classifier for the MNIST database 5 . Together, these molecular classifiers showcased the benefits of neuromorphic networks over Boolean circuits: massive parallelism, handling of analog inputs, and tolerance to corrupted patterns. However, these nonenzymatic classifiers had limited decision margins, i.e. they could not discriminate between two similar inputs belonging to different classes. They also suffered from leaks that made the compositio...

Many microfluidic devices use macroscopic pressure differentials to overcome viscous friction and generate flows in microchannels. In this work, we investigate how the chemical and geometric properties of the channel walls can drive a net flow by exploiting the autophoretic slip flows induced along active walls by local concentration gradients of a solute species. We show that chemical patterning of the wall is not required to generate and control a net flux within the channel, rather channel geometry alone is sufficient. Using numerical simulations, we determine how geometric characteristics of the wall influence channel flow rate, and confirm our results analytically in the asymptotic limit of lubrication theory.

Organic field‐effect transistors (OFETs) can be potentially employed to monitor cell activities for healthcare and medical treatment because of their attractive properties such as ease of use, flexibility, and low‐cost manufacturing processes. Although current OFET‐based sensors are suitable for point‐of‐care testing, the establishment of real‐time monitoring methods is in high demand for continuous monitoring of health conditions and/or biological cell activities. In this regard, we herein propose a microfluidic platform integrated with an extended‐gate‐type OFET for real‐time glucose monitoring. The mechanism of glucose detection depends on the artificial receptor phenylboronic acid and its boronate esterification. After optimization of the microfluidics for the OFET‐based sensor, the sensor was used to monitor glucose consumption and release in a model of pseudo‐liver cells. Random increases or decreases in the glucose concentration were reproducibly monitored.