While additive manufacturing (AM) has facilitated the production of complex structures, it has also highlighted the immense challenge inherent in identifying the optimum AM structure for a given application. Numerical methods are important tools for optimization, but experiment remains the gold standard for studying nonlinear, but critical, mechanical properties such as toughness. To address the vastness of AM design space and the need for experiment, we develop a Bayesian experimental autonomous researcher (BEAR) that combines Bayesian optimization and high-throughput automated experimentation. In addition to rapidly performing experiments, the BEAR leverages iterative experimentation by selecting experiments based on all available results. Using the BEAR, we explore the toughness of a parametric family of structures and observe an almost 60-fold reduction in the number of experiments needed to identify high-performing structures relative to a grid-based search. These results show the value of machine learning in experimental fields where data are sparse.

The ability to reliably manipulate small quantities of liquids is the backbone of high-throughput chemistry, but the continual drive for miniaturization necessitates creativity in how nanoscale samples of liquids are handled. Here, we describe a closed-loop method for patterning liquid samples on pL to sub-fL scales using scanning probe lithography. Specifically, we employ tipless scanning probes and identify liquid properties that enable probe−sample transport that is readily tuned using probe withdrawal speed. Subsequently, we introduce a novel twoharmonic inertial sensing scheme for tracking the mass of liquid on the probe. Finally, this is combined with a fluid mechanicsbased iterative control scheme that selects printing conditions to meet a target feature mass to enable closed-loop patterning with better than 1% accuracy and ∼4% precision in terms of mass. Taken together, these advances address a pervasive issue in scanning probe lithography, namely, real-time closed-loop control over patterning, and position scanning probe lithography of liquids as a candidate for the robust nanoscale manipulation of liquids for advanced high-throughput chemistry.

The atomic force microscope (AFM) is widely used in a wide range of applications due to its high scanning resolution and diverse scanning modes. In many applications, there is a need for accurate and precise measurement of the vibrational resonance frequency of a cantilever. These frequency shifts can be related to changes in mass of the cantilever arising from, e.g., loss of fluid due to a nanolithography operation. A common method of measuring resonance frequency examines the power spectral density of the free random motion of the cantilever, commonly known as a thermal. While the thermal is capable of reasonable measurement resolution and speed, some applications are sensitive to changes in the resonance frequency of the cantilever, which are small, rapid, or both, and the performance of the thermal does not offer sufficient resolution in frequency or in time. In this work, we describe a method based on a narrow-range frequency sweep to measure the resonance frequency of a vibrational mode of an AFM cantilever and demonstrate it by monitoring the evaporation of glycerol from a cantilever. It can be seamlessly integrated into many commercial AFMs without additional hardware modifications and adapts to cantilevers with a wide range of resonance frequencies. Furthermore, this method can rapidly detect small changes in resonance frequency (with our experiments showing a resolution of ∼0.1 Hz for cantilever resonances ranging from 70 kHz to 300 kHz) at a rate far faster than with a thermal. These attributes are particularly beneficial for techniques such as dip-pen nanolithography.

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