SEARCH · Search NASA

Results for “optimization platform”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,135 records · Page 63

All sources NASA NTRS DOE OSTI

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

DOE OSTI2026

Host-Directed, Bioelectronic Immunomodulation for Protection Against Emerging Pathogens

Acute care of patients with severe infections often relies on systemic administration of pharmaceuticals and monitoring of complex physiological symptoms to identify immune system dysfunction, which can lead to increased mortality. Furthermore, determining disease-specific treatment plans often leads to a delay in patient care. To address this, we proposed an immune modulation system that electrically detects and responds to a patient’s immune system status, creating an agnostic means of treating illness and infection. Two pieces of hardware were developed for this task: a minimally-invasive sensor and a vagus nerve stimulator. Stimulation of the vagus nerve is known to modulate the immune system. The sensor is a microfabricated, silicon-based microneedle array capable of interfacing with interstitial fluid to detect small molecules such as inflammatory proteins (cytokines) and pharmaceuticals (vancomycin). Process optimization to manufacture the needles refined the silicon etch process, creating needle patches long enough to penetrate skin and reach interstitial fluid. The needles were tested for mechanical strength and stability, and did not shatter when inserted into skin models. The needles are coated with a thin film metal, turning them into electrodes for electrochemical sensing of our target molecules. We hybridized aptamers to the surface of the electrode to act as the sensing layer and were able to detect changes in the conformation of the aptamer electrochemically in the presence of the target molecule. The stimulator was a cuff electrode that encircled the vagus nerve. Rodent studies were conducted in which rodents were exposed to an inflammatory event and vagus nerve stimulation (VNS) was applied. It was demonstrated that optimized electrical stimulation of the vagus nerve created measurably different levels of cytokines in blood samples, and certain cytokines released during the inflammatory event were either upregulated or downregulated. In sum, this project successfully developed new platforms and technologies that can, with further development, enable better temporal insight into biomarker changes in the body, letting healthcare providers know of possible immune system dysfunction before they are detected physiologically. We also demonstrated the value of VNS and its possible use in treating immune system response to inflammation and illness.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Data Needs for Modeling Low-Temperature Non-Equilibrium Plasmas: The LXCat Project, History, Perspectives and a Tutorial

Technologies based on non-equilibrium, low-temperature plasmas are ubiquitous in today’s society. Plasma modeling plays an essential role in their understanding, development and optimization. An accurate description of electron and ion collisions with neutrals and their transport is required to correctly describe plasma properties as a function of external parameters. LXCat is an open-access, web-based platform for storing, exchanging and manipulating data needed for modeling the electron and ion components of non-equilibrium, low-temperature plasmas. The data types supported by LXCat are electron- and ion-scattering cross-sections with neutrals (total and differential), interaction potentials, oscillator strengths, and electron- and ion-swarm/transport parameters. Online tools allow users to identify and compare the data through plotting routines, and use the data to generate swarm parameters and reaction rates with the integrated electron Boltzmann solver. In this review, the historical evolution of the project and some perspectives on its future are discussed together with a tutorial review for using data from LXCat.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1974

A study to identify and compare airborne systems for in-situ measurements of launch vehicle effluents

An in-situ system for monitoring the concentration of HCl, CO, CO2, and Al2O3 in the cloud of reaction products that form as a result of a launch of solid propellant launch vehicle is studied. A wide array of instrumentation and platforms are reviewed to yield the recommended system. An airborne system suited to monitoring pollution concentrations over urban areas for the purpose of calibrating remote sensors is then selected using a similar methodology to yield the optimal configuration.

Thomas, T. J.↗

NASA NTRS1980

Study of thermal management for space platform applications

Techniques for the management of the thermal energy of large space platforms using many hundreds of kilowatts over a 10 year life span were evaluated. Concepts for heat rejection, heat transport within the vehicle, and interfacing were analyzed and compared. The heat rejection systems were parametrically weight optimized over conditions for heat pipe and pumped fluid approaches. Two approaches to achieve reliability were compared for: performance, weight, volume, projected area, reliability, cost, and operational characteristics. Technology needs are assessed and technology advancement recommendations are made.

Oren, J. A.↗

NASA NTRS1989

A new adaptive control approach for aerospace vehicles with parameter uncertainties

A new stochastic adaptive control structure is developed for the problem of combined parameter estimation and control of aerospace vehicles with changing parameters. Parameter uncertainties are modeled as first-order Gauss-Markov processes, and are introduced to the system dynamics through a small parameter. It is assumed that an accurate inertial measurement unit gives perfect measurements of the state variables. Since the stochastic system is assumed to be Gauss-Markov, the density function of the parameters given these measurements is conditionally Gaussian. Based on this conditionally Gaussian density, the problem of minimizing a quadratic cost over an infinite time horizon can be set up within the framework of stochastic optimal control theory. The optimal feedback control law is derived from a straightforward expansion of the Hamilton-Jacobi-Bellman equation, based on the LQG solution. The resulting nonlinear controller is applied to the pitch axis control of a space platform with uncertain moments of inertia and is shown to produce marked improvement over a fixed controller.

Hahn, Yungsun↗

NASA NTRS1989

The space station tethered elevator system

The optimized conceptual engineering design of a space station tethered elevator is presented. The elevator is an unmanned mobile structure which operates on a ten kilometer tether spanning the distance between the Space Station and a tethered platform. Elevator capabilities include providing access to residual gravity levels, remote servicing, and transportation to any point along a tether. The potential uses, parameters, and evolution of the spacecraft design are discussed. Engineering development of the tethered elevator is the result of work conducted in the following areas: structural configurations; robotics, drive mechanisms; and power generation and transmission systems. The structural configuration of the elevator is presented. The structure supports, houses, and protects all systems on board the elevator. The implementation of robotics on board the elevator is discussed. Elevator robotics allow for the deployment, retrieval, and manipulation of tethered objects. Robotic manipulators also aid in hooking the elevator on a tether. Critical to the operation of the tethered elevator is the design of its drive mechanisms, which are discussed. Two drivers, located internal to the elevator, propel the vehicle along a tether. These modular components consist of endless toothed belts, shunt-wound motors, regenerative power braking, and computer controlled linear actuators. The designs of self-sufficient power generation and transmission systems are reviewed. Thorough research indicates all components of the elevator will operate under power provided by fuel cells. The fuel cell systems will power the vehicle at seven kilowatts continuously and twelve kilowatts maximally. A set of secondary fuel cells provides redundancy in the unlikely event of a primary system failure. Power storage exists in the form of Nickel-Hydrogen batteries capable of powering the elevator under maximum loads.

Anderson, Loren A.↗

NASA NTRS2019

Implementation of Human System Integration Workshop at NASA for Human Spaceflight

The human is a key element in the complex system of systems underlying space exploration missions. As a critical system, its operating bands and requirements need to be characterized and integrated with other systems. Optimal integration of the human system with hardware and software elements has an impact on multiple aspects of mission execution, including human health and performance, risk mitigation, effective design and functionality, enhanced safety, and reduced lifecycle costs. The field of Human Systems Integration (HSI) represents an interdisciplinary and comprehensive cross-cutting approach encompassing technical and management processes for integrating human as a system consideration and objective within and across all other system components and multiple domains. In addition to human activities, HSI covers training, operations and support dimensions. Moreover, HSI is an essential enabler to systems engineering practice, emphasizing human system aspects toward optimizing fully integrated system of systems performance while systematically infusing the needs of all users during the earliest stages of development. Consistent with the National Space Exploration Campaign, NASA is developing the Gateway, a lunar orbiting platform that will serve as astronaut habitat, support transit to deep space, validate new technologies and systems, and function as a science laboratory and communications hub, among other uses. It is an essential element of a phase that will extend human exploration into deep space through evolvable infrastructure and advanced technology, supporting assembly and logistics of other exploration architecture elements. In an effort to explore the current status and forward plan of HSI implementation in the mission (system of systems) lifecycle, the HSI Employee Resource Group conducted an HSI workshop using the Gateway Program as a case study. It revealed how different organizations at the Johnson Space Center incorporate HSI in their processes in preparation for the development and operation of the Gateway. The workshop focused on HSI methodology for implementation of the six NASA HSI domains: Human Factors Engineering, Operations Resources, Habitability and Environment, Maintainability and Supportability, Safety, and Training. Results from the workshop are reported on this paper, as well as some historical background of HSI at NASA, and the success in using an Employee Resource Group to promote technical knowledge. The authors hope that this information can be used to disseminate best practices for translational applications to other space exploration systems.

Silva-Martinez, Jackelynne↗

NASA NTRS2019

Implementation of Human System Integration Workshop at NASA for Human Spaceflight

Silva-Martinez, Jackelynne↗

DOE OSTI2020

Scalable Heterogeneous Execution of a Coupled-Cluster Model with Perturbative Triples

The CCSD(T) coupled-cluster model with perturbative triples is considered a gold standard for computational modeling of the correlated behavior of electrons in molecular systems. A fundamental constraint is the relatively small global-memory capacity in GPUs compared to the main-memory capacity on host nodes, necessitating relatively smaller tile sizes for high-dimensional tensor contractions in NWChem's GPU-accelerated implementation of the CCSD(T) method. A coordinated redesign is described to address this limitation and associated data movement overheads, including a novel fused GPU kernel for a set of tensor contractions, along with inter-node communication optimization and data caching. The new implementation of GPU-accelerated CCSD(T) improves overall performance by 3.4x. Finally, we discuss the trade-offs in using this fused algorithm on current and future supercomputing platforms.

Kim, Jinsung↗

DOE OSTI2026

Benchmarking quantum trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo

The phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) method is a stochastic imaginary-time projection technique for computing ground-state properties of strongly correlated quantum systems, with accuracy that depends critically on the choice of trial wavefunction. Here, we investigate ph-AFQMC with trial states prepared using parameterized quantum circuits. In this work, we present a comprehensive benchmarking study of quantum trial wavefunctions spanning unitary coupled-cluster, Hamiltonian-informed, Jastrow-inspired, and adaptively constructed ansatze. The benchmarking evaluates accuracy, expressibility, and scalability of these ansatze within the QC-AFQMC framework. We test these ansatze on linear hydrogen chains under bond stretching and find that several ansatz families produce chemically accurate ph-AFQMC energies across the dissociation curve. We have performed simulations using the CUDA-Q quantum development platform on the GPU partition of the Perlmutter supercomputer. When comparing ansatze at similar numbers of variational parameters, we find that different ansatz families yield comparable ph-AFQMC results despite exhibiting substantially different variational energies, optimization costs, and circuit depths. Our results indicate that the variational energy of an ansatz is not always a reliable indicator of its quality for ph-AFQMC and reveal instances of over-parameterization. In the strongly correlated regime, trial wavefunctions obtained from adaptive ansatze, exemplified here by ADAPT-VQE with the UCCSD operator pool, can outperform their fixed-ansatz counterparts (UCCSD) in terms of projected energies while using substantially more compact circuits, providing a flexible route to optimize quantum resources within the ph-AFQMC framework.

Rofougaran, Rod [LBNL, Berkeley; Columbia U.; PNL,↗

DOE OSTI2017

Wireless Chemical Sensor Systems (Optimization of Field-Structured Chemiresistors for in situ Quantification of Propellant Stabilizer Concentration)

This project is a Cooperative Research and Development Agreement (CRADA) with International Electronic Machines Corp. (IEM, Troy, NY), who is contracted through the U.S. Naval Air Systems Command (NAVAIR) (under SBIR N111-010) to develop an in situ wireless chemical sensor system called the Stabilizer Level Accurate Measurement System (SLAMS). The SLAMS device is specified to include a miniature, very thin, self-contained chemical-sensor node to detect and track the level of stabilizer present in various PADs and CADs. IEM is focused on the wireless sensor platform, node communication, and electronics; whereas Sandia National Laboratories’ (SNL’s) efforts are focused on the chemical sensor development and integration. The sensor technology being employed is known as field-structured chemiresistors (FSCRs), which was invented by James Martin, et al. (SNL) and refined by Martin and Read. These sensors are small, require very little power, and have tunable sensitivity and selectivity. In the following, we discuss SNL’s efforts to optimize the FSCRs for the demanding chemical and environmental conditions that will be encountered in this application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Intermediate Scale Test Platform Conceptual Design and Hybrid Resin Performance Evaluation Recommendations

Recent laboratory evaluations demonstrated that further testing of alternative hybrid resins is warranted for their potential use as part of the 200 West Area (200W) pump-and-treat (P&T) remedy. 200W P&T has been treating technetium (Tc) since 2012 and uranium (U) since 2015 via two separate ion exchange (IX) treatment units: a single treatment train (i.e., a three-vessel system) for the removal of uranium utilizing a gel strong base anion (SBA) exchange resin, Dowex 21K; and two parallel treatment trains for the removal technetium using a gel SBA resin, Purolite® A532E. While these resins have been removing these contaminants effectively, new hybrid resins can provide additional benefits of further optimizing the IX treatment unit capacities and addressing additional treatment needs (e.g., Cr(VI)) by removing multiple contaminants simultaneously and/or through specific design configurations. Some of these resins also demonstrated some level of removal for I-129 which would be significant for expansion of the P&T into the 200 East Area. This document provides design recommendations for an intermediate scale test platform (ISTP) to evaluate alternative hybrid IX resin performance under conditions approaching field scale and using 200W P&T process groundwater. Results from the ISTP will provide the technical basis required to inform alternative IX resin selection in future 200W P&T facility operations where the treatment of multiple contaminants of concern may be required.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1997

Gradient-Based Aerodynamic Shape Optimization Using ADI Method for Large-Scale Problems

A gradient-based shape optimization methodology, that is intended for practical three-dimensional aerodynamic applications, has been developed. It is based on the quasi-analytical sensitivities. The flow analysis is rendered by a fully implicit, finite volume formulation of the Euler equations.The aerodynamic sensitivity equation is solved using the alternating-direction-implicit (ADI) algorithm for memory efficiency. A flexible wing geometry model, that is based on surface parameterization and platform schedules, is utilized. The present methodology and its components have been tested via several comparisons. Initially, the flow analysis for for a wing is compared with those obtained using an unfactored, preconditioned conjugate gradient approach (PCG), and an extensively validated CFD code. Then, the sensitivities computed with the present method have been compared with those obtained using the finite-difference and the PCG approaches. Effects of grid refinement and convergence tolerance on the analysis and shape optimization have been explored. Finally the new procedure has been demonstrated in the design of a cranked arrow wing at Mach 2.4. Despite the expected increase in the computational time, the results indicate that shape optimization, which require large numbers of grid points can be resolved with a gradient-based approach.

Pandya, Mohagna J.↗

DOE OSTI2025

Modification and analysis of context-specific genome-scale metabolic models: methane-utilizing microbial chassis as a case study

ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.

Kulyashov, M. A.↗

DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗

DOE OSTI2022

Optimization of WAAM Process to Produce AUSC Components with Increased Service Life

Additive manufacturing has the potential to revolutionize industrial hardware and unlock efficiency gains through the fabrication of geometries and architectures not possible by conventional processing. Wire Arc Additive manufacturing (WAAM) process is a class of directed energy deposition process enabling higher build rate and allowing custom wire feedstock allowing spatial variation of microstructure. The larger spot size and lower speed creates a larger melt pool, reducing residual stress and often time creating directional/columnar microstructure for Ni-superalloy. The use of flexible platform provides freedom in deposition strategy, which can accommodate complex substrates, including feature addition onto existing structures, non-flat layers, and repair methods. However, the certification of the final component needs to match the strength requirement. Hence, the quality of the component produced is stringently monitored to avoid buildup of residual stress, cracks, porosity and to reduce detrimental segregated phases commonly observed during alloy solidification. Simulation plays a huge role in predicting the melt pool dimension and can be used to optimize the process parameter. Similar development is also required to perform physics-based modeling of microstructural development in WAAM that can predict the microstructural features during solidification and can be used to optimize the process more effectively. To move toward this goal, Raytheon Technologies Research Center together with Siemens worked to create a set of computational tools to control the process parameters, enable on-line measurements and acquisition with feedback to the optimized process parameters, and eventually track material evolution through each step of the additive process. Computational fluid dynamics is used for accurate prediction and calibration of the thermal field during WAAM process. and phase field models for microstructure evolution as a function of processing parameters to establish a connection between additive parameters and the final microstructure. Here we report cellular automata (CA) model development to predict the dendritic microstructure evolution with surface and bulk nuclei for single track and multiple layers. The CA model was developed to account for secondary element addition and predict segregation, local melting, and latent heat release as well as prediction of Euler angles from orientation information and validated against experiments. This framework was utilized to tailor spatially-varying composition in a part by appropriately controlling the microstructure evolution during the additive process. Functionally graded Haynes 282 alloy with high Cr content at the surface was tested for oxidation and mechanical properties. An advanced physics-based reaction-diffusion model predicting the simultaneous creation of chromium oxide and alumina is developed and validated to extend life expectancy of the WAAM manufactured high temperature part. A machine-learning data-driven framework establishing the process-structure relationship from a dataset of real microstructure images and corresponding process history data has been developed and implemented in the NX Siemens design system. The digital twin configuration along with the tool path generation enabled prediction of WAAM component buildup time and the techno-economic analysis provided a favorable option for all 4 cases with 15-40% cost reduction.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2017

Zn 2+ -Inducible Expression Platform for Synechococcus sp. Strain PCC 7002 Based on the smtA Promoter/Operator and smtB Repressor

Synechococcus sp. strain PCC 7002 has been gaining significance as both a model system for photosynthesis research and for industrial applications. Until recently, the genetic toolbox for this model cyanobacterium was rather limited and relied primarily on tools that only allowed constitutive gene expression. This work describes a two-plasmid, Zn 2+ -inducible expression platform that is coupled with a zurA mutation, providing enhanced Zn 2+ uptake. The control elements are based on the metal homeostasis system of a class II metallothionein gene ( smtA 7942 ) and its cognate SmtB 7942 repressor from Synechococcus elongatus strain PCC 7942. Under optimal induction conditions, yellow fluorescent protein (YFP) levels were about half of those obtained with the strong, constitutive phycocyanin ( cpcBA 6803 ) promoter of Synechocystis sp. strain PCC 6803. This metal-inducible expression system in Synechococcus sp. strain PCC 7002 allowed the titratable gene expression of YFP that was up to 19-fold greater than the background level. This system was utilized successfully to control the expression of the Drosophila melanogaster β-carotene 15,15'-dioxygenase, NinaB, which is toxic when constitutively expressed from a strong promoter in Synechococcus sp. strain PCC 7002. Together, these properties establish this metal-inducible system as an additional useful tool that is capable of controlling gene expression for applications ranging from basic research to synthetic biology in Synechococcus sp. strain PCC 7002. IMPORTANCE This is the first metal-responsive expression system in cyanobacteria, to our knowledge, that does not exhibit low sensitivity for induction, which is one of the major hurdles for utilizing this class of genetic tools. In addition, high levels of expression can be generated that approximate those of established constitutive systems, with the added advantage of titratable control. Together, these properties establish this Zn 2+ -inducible system, which is based on the smtA 7942 operator/promoter and smtB 7942 repressor, as a versatile gene expression platform that expands the genetic toolbox of Synechococcus sp. strain PCC 7002.

59 BASIC BIOLOGICAL SCIENCES↗