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ABSTRACT Despite their high power density, microsupercapacitors (MSCs) are impractical for many energy storage applications due to their limited energy density. Their energy density can be increased by shaping the electrodes into 3D structures with high specific surface area (SSA). Direct printing of nanoporous 3D electrodes is a promising approach for achieving high SSA. However, conventional nanoscale 3D printing is too slow due to point‐by‐point processing. Here, we have employed the projection two‐photon lithography technique to fabricate nanoporous 3D electrodes via a rapid layer‐by‐layer mechanism. The 3D MSC electrodes are engineered as an array of nanoporous polymeric micropillars that are printed with customizable spacing and count over a 0.25 cm²area. After printing, these micropillars are conformally coated with titanium nitride to form conductive 3D electrodes, which exhibit a specific capacitance of 361 μF/cm². This is two orders of magnitude higher than the capacitance of the flat surface and exceeds the capacitance of both traditional bare electrodes, such as single‐wall carbon nanotubes (< 100 μF/cm²), and electrodes produced by photo‐polymerization 3D printing (˜200 μF/cm²). As our work demonstrates that high energy density 3D electrodes can be rapidly fabricated, it significantly expands the utility of MSCs as miniaturized energy storage devices. 

Abstract Two-photon lithography (TPL) is an attractive technique for nanoscale additive manufacturing of functional three-dimensional (3D) structures due to its ability to print subdiffraction features with light. Despite its advantages, it has not been widely adopted due to its slow point-by-point writing mechanism. Projection TPL (P-TPL) is a high-throughput variant that overcomes this limitation by enabling the printing of entire two-dimensional (2D) layers at once. However, printing the desired 3D structures is challenging due to the lack of fast and accurate process models. Here, we present a fast and accurate physics-based model of P-TPL to predict the printed geometry and the degree of curing. Our model implements a finite difference method (FDM) enabled by operator splitting to solve the reaction–diffusion rate equations that govern photopolymerization. When compared with finite element simulations, our model is at least a hundred times faster and its predictions lie within 5% of the predictions of the finite element simulations. This rapid modeling capability enabled performing high-fidelity simulations of printing of arbitrarily complex 3D structures for the first time. We demonstrate how these 3D simulations can predict those aspects of the 3D printing behavior that cannot be captured by simulating the printing of individual 2D layers. Thus, our models provide a resource-efficient and knowledge-based predictive capability that can significantly reduce the need for guesswork-based iterations during process planning and optimization. 

Abstract Two-photon lithography (TPL) is a photopolymerization-based additive manufacturing technique capable of fabricating complex 3D structures with submicron features. Projection TPL (P-TPL) is a specific implementation that leverages projection-based parallelization to increase the rate of printing by three orders of magnitude. However, a practical limitation of P-TPL is the high shrinkage of the printed microstructures that is caused by the relatively low degree of polymerization in the as-printed parts. Unlike traditional stereolithography (SLA) methods and conventional TPL, most of the polymerization in P-TPL occurs through dark reactions while the light source is off, thereby resulting in a lower degree of polymerization. In this study, we empirically investigated the parameters of the P-TPL process that affect shrinkage. We observed that the shrinkage reduces with an increase in the duration of laser exposure and with a reduction of layer spacing. To broaden the design space, we explored a photochemical post-processing technique that involves further curing the printed structures using UV light while submerging them in a solution of a photoinitiator. With this post-processing, we were able to reduce the areal shrinkage from more than 45% to 1% without limiting the geometric design space. This shows that P-TPL can achieve high dimensional accuracy while taking advantage of the high throughput when compared to conventional serial TPL. Furthermore, P-TPL has a higher resolution when compared to the conventional SLA prints at a similar shrinkage rate. 

Nuclear fusion is receiving tremendous global interest due to its promise as a source of clean and abundant energy. Although scientific breakeven was recently demonstrated via inertial confinement fusion, economic breakeven has not yet been achieved in any form of fusion. A key barrier for economic viability is the high cost of fabricating the fuel containers (i.e., the targets). Here, we present a quantitative framework and apply it to generate a target manufacturing technology development roadmap to enable economically viable inertial fusion energy. We examine the impact of our recent work in nanoscale additive manufacturing (i.e., 3D printing) and identify the next steps toward economically viable fusion energy. Our analysis has implications for manufacturing technology developers, fusion power plant designers, funding agencies, and policy makers. It demonstrates that economic target manufacturing cannot be achieved by merely increasing the industrial capacity; instead, novel affordable manufacturing technologies must be developed. 

Two-photon lithography (TPL) is a laser-based additive manufacturing technique that enables the printing of arbitrarily complex cm-scale polymeric 3D structures with sub-micron features. Although various approaches have been investigated to enable the printing of fine features in TPL, it is still challenging to achieve rapid sub-100 nm 3D printing. A key limitation is that the physical phenomena that govern the theoretical and practical limits of the minimum feature size are not well known. Here, we investigate these limits in the projection TPL (P-PTL) process, which is a high-throughput variant of TPL, wherein entire 2D layers are printed at once. We quantify the effects of the projected feature size, optical power, exposure time, and photoinitiator concentration on the printed feature size through finite element modeling of photopolymerization. Simulations are performed rapidly over a vast parameter set exceeding 10,000 combinations through a dynamic programming scheme, which is implemented on high-performance computing resources. We demonstrate that there is no physics-based limit to the minimum feature sizes achievable with a precise and well-calibrated P-TPL system, despite the discrete nature of illumination. However, the practically achievable minimum feature size is limited by the increased sensitivity of the degree of polymer conversion to the processing parameters in the sub-100 nm regime. The insights generated here can serve as a roadmap towards fast, precise, and predictable sub-100 nm 3D printing. 

Abstract Two-photon lithography (TPL) is a direct laser writing process that enables the fabrication of cm-scale complex three-dimensional polymeric structures with submicrometer resolution. In contrast to the slow and serial writing scheme of conventional TPL, projection TPL (P-TPL) enables rapid printing of entire layers at once. However, process prediction remains a significant challenge in P-TPL due to the lack of computationally efficient models. In this work, we present machine learning-based surrogate models to predict the outcomes of P-TPL to >98% of the accuracy of a physics-based reaction-diffusion finite element simulation. A classification neural network was trained using data generated from the physics-based simulations. This enabled us to achieve computationally efficient and accurate prediction of whether a set of printing conditions will result in precise and controllable polymerization and the desired printing versus no printing or runaway polymerization. We interrogate this surrogate model to investigate the parameter regimes that are promising for successful printing. We predict combinations of photoresist reaction rate constants that are necessary to print for a given set of processing conditions, thereby generating a set of printability maps. The surrogate models reduced the computational time that is required to generate these maps from more than 10 months to less than a second. Thus, these models can enable rapid and informed selection of photoresists and printing parameters during process control and optimization. 

{"Abstract":["Data description \nThis dataset presents the raw and augmented data that were used to train the machine learning (ML) models for classification of printing outcome in projection two-photon lithography (P-TPL). P-TPL is an additive manufacturing technique for the fabrication of cm-scale complex 3D structures with features smaller than 200 nm. The P-TPL process is further described in this article: \u201cSaha, S. K., Wang, D., Nguyen, V. H., Chang, Y., Oakdale, J. S., and Chen, S.-C., 2019, "Scalable submicrometer additive manufacturing," Science, 366(6461), pp. 105-109.\u201d This specific dataset refers to the case wherein a set of five line features were projected and the printing outcome was classified into three classes: \u2018no printing\u2019, \u2018printing\u2019, \u2018overprinting\u2019. \n \nEach datapoint comprises a set of ten inputs (i.e., attributes) and one output (i.e., target) corresponding to these inputs. The inputs are: optical power (P), polymerization rate constant at the beginning of polymer conversion (kp-0), radical quenching rate constant (kq), termination rate constant at the beginning of polymer conversion (kt-0), number of optical pulses, (N), kp exponential function shape parameter (A), kt exponential function shape parameter (B), quantum yield of photoinitiator (QY), initial photoinitiator concentration (PIo), and the threshold degree of conversion (DOCth). The output variable is \u2018Class\u2019 which can take these three values: -1 for the class \u2018no printing\u2019, 0 for the class \u2018printing\u2019, and 1 for the class \u2018overprinting\u2019. \n\nThe raw data (i.e., the non-augmented data) refers to the data generated from finite element simulations of P-TPL. The augmented data was obtained from the raw data by (1) changing the DOCth and re-processing a solved finite element model or (2) by applying physics-based prior process knowledge. For example, it is known that if a given set of parameters failed to print, then decreasing the parameters that are positively correlated with printing (e.g. kp-0, power), while keeping the other parameters constant would also lead to no printing. Here, positive correlation means that individually increasing the input parameter will lead to an increase in the amount of printing. Similarly, increasing the parameters that are negatively correlated with printing (e.g. kq, kt-0), while keeping the other parameters constant would also lead to no printing. The converse is true for those datapoints that resulted in overprinting. \n\nThe 'Raw.csv' file contains the datapoints generated from finite element simulations, the 'Augmented.csv' file contains the datapoints generated via augmentation, and the 'Combined.csv' file contains the datapoints from both files. The ML models were trained on the combined dataset that included both raw and augmented data."]}