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In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects. 

Natural systems, synthetic materials, and devices almost always feature interphases that control the flow of mass and energy or stabilize interfaces between incompatible materials. With technologies transitioning to non-planar and 3D mesoscale architectures, novel deposition methods for realizing ultrathin coatings and interphases are required. Polymer networks are of particular interest for their tunable chemical and physical properties combined with their structural integrity. Here, the electrodeposition of polymer networks (EPoN) is introduced as a general approach to uniformly coat non-planar conductive materials. Conceptually, EPoN utilizes electrochemically activated crosslinkers as polymer end groups to confine their network formation exclusively to the material surface upon charge transfer, yielding a passivating and self-limiting growth of conformal and uniform coatings with tunable submicron thickness on conductive materials. EPoN is found to result in thin functional films of various polymer backbones and side group chemistries as demonstrated for poly(ether) and poly(acrylamide) based polymers as solid electrolyte and thermally responsive interphases, respectively. 

We introduce the polymer analysis and discovery array (PANDA), an automated system for high-throughput electrodeposition and functional characterization of polymer films. The PANDA is a custom, modular, and low-cost system based on a CNC gantry that we have modified to include a syringe pump, potentiostat, and camera with a telecentric lens. This system can perform fluid handling, electrochemistry, and transmission optical measurements on samples in custom 96-well plates that feature transparent and conducting bottoms. We begin by validating this platform through a series of control fluid handling and electrochemistry experiments to quantify the repeatability, lack of cross-contamination, and accuracy of the system. As a proof-of-concept experimental campaign to study the functional properties of a model polymer film, we optimize the electrochromic switching of electrodeposited poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) films. In particular, we explore the monomer concentration, deposition time, and deposition voltage using an array of experiments selected by Latin hypercube sampling. Subsequently, we run an active learning campaign based upon Bayesian optimization to find the processing conditions that lead to the highest electrochromic switching of PEDOT:PSS. This self-driving lab integrates optical and electrochemical characterization to constitute a novel, automated approach for studying functional polymer films. 

Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process‐property relationships. In this work, a self‐driving lab is utilized that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, an underlying self‐stabilizing characteristic of VTP layer thickness is discovered as an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable‐driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing. 

Photoactuated pens have emerged as promising tools for expedient, mask-free, and versatile nanomanufacturing. However, the challenge of effectively controlling individual pens in large arrays for high-throughput patterning has been a significant hurdle. In this study, we introduce novel generations of photoactuated pens and explore the impact of pen architecture on photoactuation efficiency and crosstalk through simulations and experiments. By introducing a thermal insulating layer and incorporating an air ap in the architecture design, we have achieved the separation of pens into independent units. This new design allowed for improved control over the actuation behavior of individual pens, markedly reducing the influence of neighboring pens. The results of our research suggest novel applications of photoactive composite films as advanced actuators across diverse fields, including lithography, adaptive optics, and soft robotics. 

Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions at which it is possible to detect sub-micron defects at high-throughput. Extrapolating from the signal to background observed for detecting 440 nm wide lines and 620 nm diameter circles, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 70 nm in radius. We further explore this method by using it to characterize a nominally insulating phenol film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given this assay’s inherent parallelizability and scalability, it is expected to have a major impact on the automated discovery of multifunctional films.