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DOE OSTI2025

From TPL assessment to design optimization: Wave energy converter control co-design applied to the RM3

The Reference Model (RM) project developed six marine energy converter concepts using a sequential design methodology, which, while widely adopted in the industry, often overlooks interactions between system components, resulting in suboptimal designs. One such example is the Reference Model 3 (RM3), a two-body point absorber wave energy converter (WEC). An assessment using the Technology Performance Level (TPL) revealed that RM3’s low power-to-cost ratio, partly due to expensive steel construction, limits its techno-economic performance. Here, this study aims to redesign RM3 by reducing its scale and employing control co-design to integrate WEC and Power Take-Off (PTO) dynamics, constraints, and cost considerations within an optimization framework. We demonstrate the limitations of RM3’s current PTO design and explore the benefits of scaling down to enhance techno-economic viability by lowering material costs. Using WecOptTool, we conduct a parameter sweep over gear ratios and spring stiffnesses for various Commercial Off-The-Shelf generators in irregular wave conditions. Our findings emphasize the importance of aligning PTO components with WEC dynamics, showing that control co-design and strategic scaling can improve RM3’s power-to-cost ratio. This study presents a transferable example of applied control co-design for other WECs, supporting early-stage developers in their design decisions.

Off the shelf components↗

DOE OSTI2022

Collaborative TPL Assessment of Wave Energy Converters and Farms

The Technology Performance Level (TPL) assessment is a holistic methodology to assess a wave energy converter (WEC) technology’s ability to achieve [continental grid] market competitiveness and acceptability via criteria-based consideration of key cost, performance, environmental, safety, and societal drivers. The TPL assessment can be applied at all technology development stages and associated technology readiness levels (TRLs).

Technology Performance Level↗

DOE OSTI2022

WaveSPARC: TPL Assessment - Guiding Technology Development Trajectories to Successful Outcomes in Less Time, at Less Overall Cost, and With Less Encountered Risk

marine renewable energy↗

DOE OSTI2022

M379A Mutant Tyrosine Phenol–lyase from Citrobacter freundii Has Altered Conformational Dynamics

The M379A mutant of Citrobacter freundii tyrosine phenol-lyase (TPL) has been prepared. M379A TPL is a robust catalyst to prepare a number of tyrosines substituted at the 3-position with bulky groups that cannot be made with wild type TPL. The three dimensional structures of M379A TPL complexed with L-methionine and 3-bromo-DL-phenylalanine have been determined by X-ray crystallography. Methionine is bound as a quinonoid complex in a closed active site in 3 of 4 chains of homotetrameric M379A TPL. M379A TPL reacts with L-methionine about 8-fold slower than wild type TPL. The temperature dependence shows that the slower reaction is due to less positive activation entropy. The structure of the M379A TPL complex of 3-bromo-DL-phenylalanine has a quinonoid complex in two subunits, with an open active site conformation. The effects of the M379A mutation on TPL suggest that the mutant enzyme has altered the conformational dynamics of the active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Automated detection of part quality during two-photon lithography via deep learning

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Understanding the Uncertainty in the Technical Performance Level Assessment for Wave Energy

In recent years, the design and development of wave energy converters (WECs) has been explored with intense interest, with highly varying design concepts emerging globally across both research enterprises and industry. The design space for WECs is vast - many concepts ranging in functionality, control systems, power development systems, materials, and scale have been ideated and prototyped, but WEC technology has yet to converge. One critical element of the technology trajectory that governs the speed of adoption is the performance of a WEC concept. In analogous but more-established industries (such as aerospace, and environmentally sustainable electronics design), performance assessment is a quantitative method, based on historical data, that is used as an iterative tool to improve the design of these systems early on in the design process. Though more nascent than these approaches, in wave energy R&D, WEC performance has been assessed using the Technology Performance Level (TPL) assessment, which provides designers with a quantitative score, situating a grid-scale WEC concept on a scale from 1-9 (1 being the lowest performance, and 9 being the highest, trending with the oft-used Technology Readiness Level, or TRL). The TPL assessment is designed to be used during design iteration, when a WEC concept is fully ideated, to enable designers to consider potential means of improving the downstream performance of the concept. One concern that may be slowing the adoption of TPL among WEC developers is the inherent uncertainty in the assessment, and how uncertainty in the individual questions asked as part of the assessment may contribute to perceived inaccuracies in the final score. In this work, we explore the uncertainty present in the assessment and quantify this uncertainty using both traditional mathematical operations and a Monte Carlo simulation. Results imply areas of improvement of the TPL assessment, where reducing uncertainty will be most helpful to end users, enabling both TPL practitioners and users to understand with more accuracy those design elements that can be improved to impact device performance most substantively.

techno-economic analysis↗

DOE OSTI2023

Plasmonic nanorod probes’ journey inside plant cells for in vivo SERS sensing and multimodal imaging

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Cupil-Garcia, Vanessa↗

DOE OSTI2022

Adapting the Technology Performance Level Integrated Assessment Framework to Low-TRL Technologies Within the Carbon Capture, Utilization, and Storage Industry, Part I

With the urgent need to mitigate climate change and rising global temperatures, technological solutions that reduce atmospheric CO 2 are an increasingly important part of the global solution. As a result, the nascent carbon capture, utilization, and storage (CCUS) industry is rapidly growing with a plethora of new technologies in many different sectors. There is a need to holistically evaluate these new technologies in a standardized and consistent manner to determine which technologies will be the most successful and competitive in the global marketplace to achieve decarbonization targets. Life cycle assessment (LCA) and techno-economic assessment (TEA) have been employed as rigorous methodologies for quantitatively measuring a technology's environmental impacts and techno-economic performance, respectively. However, these metrics evaluate a technology's performance in only three dimensions and do not directly incorporate stakeholder needs and values. In addition, technology developers frequently encounter trade-offs during design that increase one metric at the expense of the other. The technology performance level (TPL) combined indicator provides a comprehensive and holistic assessment of an emerging technology's potential, which is described by its techno-economic performance, environmental impacts, social impacts, safety considerations, market/deployability opportunities, use integration impacts, and general risks. TPL incorporates TEA and LCA outputs and quantifies the trade-offs between them directly using stakeholder feedback and requirements. In this article, the TPL methodology is being adapted from the marine energy domain to the CCUS domain. Adapted metrics and definitions, a stakeholder analysis, and a detailed foundation-based application of the systems engineering approach to CCUS are presented. The TPL assessment framework is couched within the internationally standardized LCA framework to improve technical rigor and acceptance. It is demonstrated how stakeholder needs and values can be directly incorporated, how LCA and TEA metrics can be balanced, and how other dimensions (listed earlier) can be integrated into a single metric that measures a technology's potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

SCEPTRE 2.1 Quick Start Guide

This report provides a summary of notes for building and running the Sandia Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) code. SCEPTRE is a general purpose C++ code for solving the Boltzmann transport equation in serial or parallel using unstructured spatial finite elements, multigroup energy treatment, and a variety of angular treatments including discrete ordinates and spherical harmonics. Either the first-order form of the Boltzmann equation or one of the second-order forms may be solved. SCEPTRE requires a small number of open-source Third Party Libraries (TPL) to be available, and example scripts for building these TPL's are provided. The TPL's needed by SCEPTRE are Trilinos, boost, and netcdf. SCEPTRE uses an autoconf build system, and a sample configure script is provided. Running the SCEPTRE code requires that the user provide a spatial finite-elements mesh in Exodus format and a cross section library in a format that will be described. SCEPTRE uses an xml-based input, and several examples will be provided.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

SCEPTRE 2.3 Quick Start Guide

This report provides a summary of notes for building and running the Sandia Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) code. SCEPTRE is a general- purpose C++ code for solving the linear Boltzmann transport equation in serial or parallel using unstructured spatial finite elements, multigroup energy treatment, and a variety of angular treatments including discrete ordinates (Sn) and spherical harmonics (Pn). Either the first-order form of the Boltzmann equation or one of the second-order forms may be solved. SCEPTRE requires a small number of open-source Third Party Libraries (TPL) to be available, and example scripts for building these TPL are provided. The TPL needed by SCEPTRE are Trilinos, Boost, and Netcdf. SCEPTRE uses an autotools build system, and a sample configure script is provided. Running the SCEPTRE code requires that the user provide a spatial finite-elements mesh in Exodus format and a cross section library in a format that will be described. SCEPTRE uses an xml-based input, and several examples will be provided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies: Preprint

The advantage of using Technology Performance Level (TPL) in conjunction with Technology Readiness Level (TRL) assessments in guiding technology development trajectories to successful outcomes in less time, at less overall cost, and with less encountered risk has been well articulated in the literature. In partnership with industry and international collaborators, a TPL assessment methodology for grid-connected applications has been developed through the application of the systems engineering approach. Metrics under seven different categories have been developed, weighted based on their relative relevance, and combined to yield a composite score. The methodology has been implemented in a spreadsheet tool plus a web application specifically aimed at assessing early stage (TRL 1-3) concepts. The target use cases are (a) technology developers improving their design, to find fatal flaws early, to get feedback on current design, to identify areas of improvement that will yield the highest return on investment, (b) reviewers assessing technologies in competitions or for making funding decisions, (c) investor or project developer doing due diligence, (d) policy makers landscaping the technology domain for formulating R&D strategy. The methodology and the tools are undergoing continuous improvement based on the experience and lessons learnt from applying it to internal and external marine energy technology development projects. The methodology is also being adapted for assessing WECs servicing markets outside the continental grid - broadly categorized as Powering the Blue Economy (PBE) applications. Such applications have vastly different functional requirements entailing a modification of the methodology to account for their higher risk tolerance, reduced price sensitivities, lower power needs, different permitting protocols, etc. This paper presents the latest status of the TPL assessment methodology and tools, describes its adaptation to select PBE markets, and explores its extension to other domains where it could provide a comprehensive and holistic measure of a nascent or disruptive technology's technoeconomic performance potential.

metrics↗

DOE OSTI2021

Developing Technology Performance Level Assessments for Early-Stage Wave Energy Converter Technologies

metrics↗

DOE OSTI2026

Phosphoserine Charge State Drives Ion Condensation and Spatial Polyamine Presentation in Multirepeat Silaffin

Diatom silaffins direct silica biomineralization through heavily post-translationally modified repeat domains, yet how these modifications reshape the multirepeat conformational ensemble remains unknown. We report all-atom MD simulations of a 195-residue construct spanning repeats R1–R7 of Sil1p from Cylindrotheca fusiformis, carrying the full complement of native PTMs: phosphoserine (pSer), long-chain polyamines (LCPA), dimethyllysine (MLY), and trimethylhydroxylysine phosphate (TPL). We simulated three variants (Native, P1/singly deprotonated phosphate, and P2/doubly deprotonated phosphate) at two NaCl concentrations in triplicate for 500 ns each. All systems disorder from the AlphaFold 3 starting structure. Phosphate charge state, not ionic strength, is the dominant control of ensemble compaction and ion organization. Doubly deprotonated phosphate organizes an extensive Na + condensation shell (∼100 ions, 20% of box Na + ) and a heterogeneous bridging network that integrates both pSer and TPL phosphate groups. The resulting ensemble is compact with LCPA side chains exhibiting above-median solvent accessibility in 84% of simulation frames in P2 at 300 mM. This is higher than any other condition we simulated and indicates that polyamine groups are preferentially surface-presented in the most compact, ion-organized state. A charge-neutralization control confirms that this compact state is a structured intermediate maintained by the bridging network, not a simple collapsed globule. Here, this repeat-scale spatial organization is not captured by single-repeat peptide studies. Understanding the dynamics, mechanism, and spatial organization of PTM-rich silaffin at the repeat scale is a step closer to hierarchical biomimetic materials beyond simple silica morphologies.

Amines↗

DOE OSTI2025

3D nanolithography with metalens arrays and spatially adaptive illumination

The growing demand for advanced materials, miniaturized devices and integrated microsystems calls for the reliable fabrication of complex, multiscale, three-dimensional (3D) architectures, a need increasingly addressed through light-based and laser-based processes. However, owing to the field-of-view (FOV) limitations of conventional imaging optics, existing 3D laser nanofabrication techniques face fundamental challenges in throughput, proximity error and stitching defects on the path to scaling. Here, in this study, we present a scalable 3D nanofabrication platform that uses a metalens-generated focal spot array to parallelize two-photon lithography (TPL) beyond centimetre-scale write field areas. Metalenses are ideally suited for producing submicron-scale focal spots for high-throughput nanolithography, as they uniquely feature large numerical apertures (NAs), immersion media compatibility and large-scale manufacturability. We experimentally demonstrate a printing system that uses a 12-cm 2 metalens array to produce more than 120,000 cooperative focal spots, corresponding to a throughput exceeding 10 8 voxels s −1 . By programmatically patterning the focal spot array using a spatial light modulator (SLM), an adaptive parallel printing strategy is developed for precise greyscale linewidth modulation and choreographed printing of semiperiodic and fully aperiodic 3D geometries. We demonstrate parallel printing of replicated microstructures (>50 M microparticles per day), centimetre-scale 3D architectures with feature sizes down to 113 nm, and photonic and mechanical metamaterials. This work demonstrates the potential of 3D nanolithography towards wafer-scale production, showing how TPL could be used at scale for applications in microelectronics, biomedicine, quantum technology and high-energy laser targets.

Materials science↗

DOE OSTI2026

Suppressing proximity effects during rapid serial two-photon lithography through tuning of reaction–diffusion kinetics

The ability of two-photon lithography (TPL) to print deterministic nanoporous 3D structures is highly valuable for many applications. However, it is challenging to print such structures rapidly due to the proximity effects that cause closely spaced features to enlarge and merge. A key challenge is the limited understanding of the origins and spatiotemporal dynamics of the long-range proximity effects that extend beyond the optical focal spot. Here, we empirically investigate these proximity effects in serial TPL using custom-made acrylate-based photoresists. We demonstrate that the complex spatiotemporal dynamics of the proximity effects can be explained through the kinetics of the reaction–diffusion photopolymerization mechanisms that underlie the curing process. Specifically, we show that the proximity effects arise due to comparable timescales of reaction and diffusion of oxygen. Furthermore, we demonstrate that long-range proximity effects can be suppressed by introducing phenolic inhibitors, which reduce oxygen consumption through non-inhibiting side reactions and promote faster termination of curing reactions. These insights enabled improving the linewidths from >400 to 260 nm during printing of nanoporous 3D woodpiles at scanning speeds of 50 mm s −1 . Thus, the knowledge generated here can be applied to deterministically tune the proximity effects and enable rapid printing of high-resolution nanoporous 3D structures.

36 MATERIALS SCIENCE↗

DOE OSTI2020

A MEMS dynamic mechanical analyzer for in situ viscoelastic characterization of 3D printed nanostructures

Cellular metamaterial structures with sub-micron features have shown the ability to become excellent energy absorbing materials for impact mitigation due to the enhanced mechanical properties of materials at the nanoscale. However, in order to optimize the design of these energy absorbing metamaterial structures we need to be able to measure the dynamic properties of the sub-micron features such as storage and loss moduli and the loss factor. Therefore, at scale testing is required to capture the scale, temperature, and strain rate dependent material properties of these nanoscale materials. This study presents the design, fabrication, and calibration of a MEMS-based dynamic mechanical analyzer (DMA) that can be directly integrated with the two photon lithography (TPL) process commonly used to fabricate metamaterial structures with nanoscale features. The MEMS-based DMA consists of a chevron style thermal actuator used to generate a tensile load on the structure and two differential capacitive sensors on each side of the structure used to measure load and displacement. This design demonstrated 1.5 ± 0.75 nm displacement resolution and 104 ± 52 nN load resolution, respectively. Dynamic mechanical analysis was successfully conducted on a single nanowire feature printed between the load and displacement stages of the MEMS device with testing frequencies ranging between 0.01–10 Hz and testing temperatures ranging between 22 °C–47 °C. These initial tests on an exemplar TPL part demonstrate that the printed nanowire behaves as a viscoelastic material wherein the transition from glassy to viscous behavior has already set in at the room temperature.

42 ENGINEERING↗

DOE OSTI2022

ODI notebook additive_manufacturing_video_2022

Additive Manufacturing, video dataset Two-photon lithography (TPL) is a widely used 3D nanoprinting technique that uses laser light to create objects. Challenges to large-scale adoption of this additive manufacturing method include identifying light dosage parameters and monitoring during fabrication. A research team from LLNL, Iowa State University, and Georgia Tech is applying machine learning models to tackle these challenges-i.e., accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. Funded by LLNL's Laboratory Directed Research and Development Program, the project team has curated a video dataset of TPL processes for parameters such as light dosages, photo-curable resins, and structures. Both raw and labeled versions of the datasets are available on the links in the Open Data Initiative page. The code uses the labeled dataset. Notebook compiled by Nisha Mulakken (mulakken1@llnl.gov) for LLNL Open Data Initiative, Summer 2022. Original code provided by research team. Publications: X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. "Automated detection of part quality during two-photon lithography via deep learning." Additive Manufacturing 36, December 2020: doi.org/10.1016/j.addma.2020.101444 X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. "wo Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures." Data in Brief 32, October 2020. doi.org/10.1016/j.dib.2020.106119.

Mulakken, NishaJ↗

DOE OSTI2022

Testing and Expertise for Marine Energy (TEAMER) Program Support - Numerical Modeling Assistance for iProTech's "PIP" WEC Device: Cooperative Research and Development Final Report, CRADA Number CRD-20-17303

Develop a time-domain, 3 degree of freedom numerical model of IProTech's PIP device, including power take-off (PTO), that can be used to develop control algorithms and establish a baseline technology performance level (TPL). This is important to IProTech because having a good numerical model allows IProTech to optimize design and control algorithms on an ongoing basis. A baseline TPL enables the PIP technology to be benchmarked against other wave energy converter (WEC) concepts, providing (i) justification (or not) for investment in wave tank model testing, (ii) identifying optimum design parameters and control algorithms for the test model, (iii) specifying test conditions and (iv) interpreting wave tank test results. An accredited WEC-Sim model of the PIP device is an essential tool for further development of the technology. 1) The development of numerical models of the PIP concept will enable iProTech to evaluate the potential performance of this concept, and to investigate how sensitive the device is to different variables within the system (e.g. hydraulic components, geometry dimensions, etc.) 2) The numerical models were developed with WEC-Sim and PTO-Sim -- the de-facto industry standard WEC numerical modeling tools. A detailed model of the system's hydraulic circuit was developed with PTO-Sim. However, WEC-Sim is based on linear hydrodynamics and has several limitations; validation against high fidelity models/physical scale models would help to build confidence in the results.

16 TIDAL AND WAVE POWER↗