Faculty Research and Data

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/41971

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    Modeling, Machine Learning, and Additive Printing for the Solar Cell Grid
    (2023-05-23) Nakano-Baker, Oliver
    The front electrode grid of a solar cell solves a simple problem: it must move current from the solar cell area to a sink. In doing so, it must minimize the resistive power losses incurred in transit while minimizing the size of the shadow it casts on the underlying photovoltaic material. The optimal geometry to achieve this may become complex depending on the material and scaling properties of the system. It may share morphological aspects with the natural systems that solve similar problems, such as leaves, veins, and waterways. The underpinnings of solar cell grid optimization are a set of well-known equations describing the principal sources of shadowing and resistive power loss. We challenged two assumptions that have been long entrenched in the field: first, that colinear line arrays are strictly optimal when designing solar cell grids. In fact, there is a class of isotropic grids that can outperform linear arrays in certain conditions. Second, that constant grid line height describes most grid applications. Instead, in the era of additive and printed electronics, many grids may be subject to virtuous scaling - the property that larger wires become more efficient conductors. Under virtuous scaling, complex ramified electrode structures become optimal. A dynamic graph approach was developed to model these electrode patterns, and a locally greedy approach to optimize them realized novel electrode patterns with ~1\% performance over standard linear grids. The continuous width variation inherent in these patterns presented a control challenge for an additive process attempting to print them. An electrohydrodynamic (EHD) inkjet printing approach was proposed; the powerful but difficult-to-control additive technique would be combined with machine learning (ML) model-based control. Rapid serial experiments on the EHD produced a large dataset of training samples. ML models were trained on this data to predict the jetting and deposited feature characteristics of the EHD as a function of the input waveform. Transfer learning was demonstrated between EHD task datasets using a performance-gated mixture-of-experts ensemble, and zero-shot models were trained for use in a model-based control algorithm. Model-based control of the EHD, paired with a single proportional tuning adjustment, was able to achieve ~$5 \mu m$ error when printing lines and ~$1 \mu m$ error when printing dots, both over at least one decade of feature sizes. This capability enabled EHD printing of micron-scale ramified solar cell electrodes with ML-produced recipes. These steps demonstrate a complete journey from concept to prototype, in which computational tools model, optimize, and ultimately manufacture a novel engineered electrode structure to improve solar cell performance.
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    iOBPdb – A Database for Experimentally Determined Functional Characterization of Odorant Binding Proteins
    (2023) Shukla, Shalabh; Nakano-Baker, Oliver; Godin, Dennis; MacKenzie, Devin; Sarikaya, Mehmet
    Odorant binding proteins (OBPs) are extra-cellular proteins which solubilize and transport volatile organic compounds (VOCs). Thousands of OBPs have been identified through genome sequencing and hundreds have been characterized by fluorescence ligand binding assays in individual studies. There is a limited understanding of the comparative structure-function relations of OBPs, primarily due to a lack of a centralized database that relates OBP binding affinity and structure. Combining 215 functional studies containing 381 unique OBPs from 91 insect species we present a database, iOBPdb: https://iobpdb.herokuapp.com, of OBP binding affinities for 620 unique VOC targets. This initial database provides powerful search and associative capabilities for retrieving and analyzing OBP-VOC binding interaction data. We present our results in a variety of phylogenetic representations as well as providing the binding profiles of OBP groups to VOC functional moieties. Potential applications include development of molecular probes for biosensors, novel bioassays and drugs, targeted pesticides which inhibit VOC / OBP interactions, and understanding odor sensing and perception in the brain.
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    iOBPdb – A Database for Experimentally Determined Functional Characterization of Odorant Binding Proteins
    (2023) Shukla, Shalabh; Nakano-Baker, Oliver; Godin, Dennis; MacKenzie, Devin; Sarikaya, Mehmet
    Odorant binding proteins (OBPs) are extra-cellular proteins which solubilize and transport volatile organic compounds (VOCs). Thousands of OBPs have been identified through genome sequencing and hundreds have been characterized by fluorescence ligand binding assays in individual studies. There is a limited understanding of the comparative structure-function relations of OBPs, primarily due to a lack of a centralized database that relates OBP binding affinity and structure. Combining 215 functional studies containing 381 unique OBPs from 91 insect species we present a database, iOBPdb: https://iobpdb.herokuapp.com, of OBP binding affinities for 620 unique VOC targets. This initial database provides powerful search and associative capabilities for retrieving and analyzing OBP-VOC binding interaction data. We present our results in a variety of phylogenetic representations as well as providing the binding profiles of OBP groups to VOC functional moieties. Potential applications include development of molecular probes for biosensors, novel bioassays and drugs, targeted pesticides which inhibit VOC / OBP interactions, and understanding odor sensing and perception in the brain.
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    EHDprint dataset ver2
    (2023-02-13) Nakano-Baker, Oliver; MacKenzie, Devin
    Advances in high resolution electrohydrodynamic inkjet (EHD) printing of functional materials are poised to enable additive and scalable large-area manufacturing for optical metasurfaces and flexible electronics. This would provide a fully additive, scalable pathway for the fabrication of flexible optoelectronics that rivals and even exceeds the performance of conventional subtractive electronics processing technologies. EHD printing moves additive printing of functional materials from the domain of conventional printing resolutions of >20 microns down to single micron and even submicron regimes that exceed the patterning resolution for large area lithography used in thin film silicon on glass processing for displays. There are, however, improvements in the technique that are required to make this a manufacturable technology of use to a broader range of materials and applications spanning electronics, photonic and optical metasurfaces, and interconnects. Process monitoring, optimization of new inks, and design for EHD are challenging due to the extremely small, high velocity femtoliter droplet sizes that are 1000X smaller than conventional inkjet droplets and high droplet velocities and are beyond the limits of conventional droplet and imaging approaches. To achieve high throughput and reproducibility in the EHD inkjet’s complex process space, proven in-situ laser diffraction, in-situ imaging and characterization strategies will be used to optimize, train, and deploy machine learning models specialized in prediction and control of complex dynamical systems. By providing reproducible feature control at the micron and even submicron scale, this ML-guided manufacturing approach will make deployment of large-area photonic, sensing and high resolution flexible printed circuit and photonic technologies with features sizes approaching the wavelength of light feasible at scale. With this collaboration between the University of Washington, Iowa State University, and small businesses, we form an interdisciplinary team with members in different backgrounds and expertise in printed electronics, metasurface design, materials, printing hardware and applications. We expect the project to have a disruptive impact on advanced ultralight optical materials, additive manufacture of micron-scale interconnects and functional devices, and data-driven process control. The proposed work directly addresses Part E. of the FlexTech/SEM RFP, specifically advancing the development of optical metamaterials and AI for process optimization. The work, as a sustainable, additive manufacturing alternative to subtractive semiconductor processing, addresses Part C. Lastly, this project also addresses a key scope area for Part D – Heterogeneous packaging as it applies to FHE- Heterogeneous Integration, specifically in that it provides an additive processing with fine lines and pads with pads separation of <10um. The industrial collaborators provide a pathway for manufacturing and product commercialization of this work.
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    iOBPdb – A Database for Experimentally Determined Functional Characterization of Odorant Binding Proteins
    (bioRxiv, 2022-08-24) Shukla, Shalabh; Nakano-Baker, Oliver; Godin, Dennis; MacKenzie, Devin; Sarikaya, Mehmet
    Odorant binding proteins (OBPs) are extra-cellular proteins which solubilize and transport volatile organic compounds (VOCs). Thousands of OBPs have been identified through genome sequencing and hundreds have been characterized by fluorescence ligand binding assays in individual studies. There is a limited understanding of the comparative structure-function relations of OBPs, primarily due to a lack of a centralized database that relates OBP binding affinity and structure. Combining 215 functional studies containing 381 unique OBPs from 91 insect species we present a database, iOBPdb: https://iobpdb.herokuapp.com, of OBP binding affinities for 620 unique VOC targets. This initial database provides powerful search and associative capabilities for retrieving and analyzing OBP-VOC binding interaction data. We present our results in a variety of phylogenetic representations as well as providing the binding profiles of OBP groups to VOC functional moieties. Potential applications include development of molecular probes for biosensors, novel bioassays and drugs, targeted pesticides which inhibit VOC / OBP interactions, and understanding odor sensing and perception in the brain.
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    EHDprint dataset ver1
    (2022-03-30) Nakano-Baker, Oliver
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    Electrohydrodynamic Inkjet High Speed Dataset
    (2021-10-10) Nakano-Baker, Oliver
    This dataset is a collection of post-processed high-speed videos of ink jet formation in an Electrohydrodynamic Inkjet Printer. Image analysis was used to extract the z-axis profile of an ink column's diameter during jet formation and collapse. An EHDIJ uses electric signal applied at the printer tip to drive droplet formation and attract ink to the grounded substrate. The electrical and fluid behavior at the tip of an EHDIJ printer is dynamic, transient, and not fully understood. Manufacturing outcomes of EHD printing are closely tied to the interplay of tip, electrical signal, substrate, and ink properties. Understanding these droplet dynamics is key to producing high-resolution and consistent output from the EHD printer. Advances in this technology could enable EHD additive printed patterns with features sizes far smaller (micron and below) than other current additive technologies, possibly enabling novel energy, electronics, optical, and biomedical applications.
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