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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.

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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Gravitational waves from global cosmic strings and cosmic archaeology

Global cosmic strings are predicted in many motivated extensions to the Standard Model of particle physics, with close connections to axion dark matter physics. Recent studies suggest that, although subdominant relative to Goldstone emission, gravitational wave (GW) signals from global strings can be detectable with current and planned GW detectors such as LIGO, LISA, DECIGO/BBO, ET/CE and AEDGE/AION, as well as pulsar timing arrays such as PPTA, NANOGrav and SKA. This work is an extensive, updated study on GWs from a global cosmic string network, taking into account of the most recent developments related to the subject. The main analysis is based on the analytical Velocity-dependent One-Scale (VOS) model calibrated with recent simulation results, which provides a generic protocol for such calculations with details given. We also demonstrate how the GW signal can be influenced with variations to the baseline model: this includes considering the uncertainties of model parameters and the potential deviation from the conventional VOS model prediction (i.e. the scaling behavior) as suggested by some of the recent simulation results. Furthermore, we investigated in detail the effect of a non-standard cosmology (e.g. early matter domination or kination) or new particle species on the GW signals from global strings. We demonstrate that the frequency spectrum of GW background from global cosmic strings can be used to probe the cosmic history prior to the Big Bang nucleosynthesis (BBN) (i.e. the primordial dark age) up to a temperature of T ~ 10 8 GeV.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Surface Energy Modulated Self‐Assembly of Percolative Gallium Nanodroplet Films for Stretchable Electronics

Advancements in soft robotics and wearable technologies have led to an increased need for flexible and stretchable electronic systems. Liquid metals like Gallium (Ga) and its alloys offer high potential in the field of stretchable electronics and photonics due to their inherent high conductivity and malleability. However, their high surface tension and native oxide formation hinder uniform wetting and continuous film formation to get conducting films. Here, we report a fabrication strategy to realize electrically conductive Ga thin films with effective thicknesses around 100 nm on polymeric and elastomeric substrates. By enhancing the surface energy of the elastomer, we create uniform Ga nanodroplets capable of sustaining stretchability up to 200% strain. Our devices exhibit electronic conduction primarily through tunneling and display strain tunable piezoresistive characteristics, which are explained using Kirchhoff's resistive network and Simmon's analytical tunneling current model. To showcase the device's capability in fields of stretchable electronics, we demonstrate several functional prototypes, including flexible devices integrated with Light Emitting Diodes (LEDs), curvature and pressure sensors, and soft robotic grippers capable of sensing object curvature. The demonstrated devices highlight the potential of this approach for scalable integration of stretchable electronics in soft robotic and wearable platforms.

42 ENGINEERING↗
DOE OSTI2025

Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy

Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.

2D materials↗
DOE OSTI2021

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Furthermore, our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Overall, our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Considerations and recent advances in nanoscale interfaces with neuronal and cardiac networks

Nanoscale interfaces with biological tissue, principally made with nanowires (NWs), are envisioned as minimally destructive to the tissue and as scalable tools to directly transduce the electrochemical activity of a neuron at its finest resolution. This review lays the foundations for understanding the material and device considerations required to interrogate neuronal activity at the nanoscale. We first discuss the electrochemical nanoelectrode-neuron interfaces and then present new results concerning the electrochemical impedance and charge injection capacities of millimeter, micrometer, and nanometer scale wires with Pt, PEDOT:PSS, Si, Ti, ITO, IrO x , Ag, and AgCl materials. Using established circuit models for NW-neuron interfaces, we discuss the impact of having multiple NWs interfacing with a single neuron on the amplitude and temporal characteristics of the recorded potentials. We review state of the art advances in nanoelectrode-neuron interfaces, the standard control experiments to investigate their electrophysiological behavior, and present recent high fidelity recordings of intracellular potentials obtained with ultrasharp NWs developed in our laboratory that naturally permeate neuronal cell bodies. Recordings from arrays and individually addressable electrically shorted NWs are presented, and the long-term stability of intracellular recording is discussed and put in the context of established techniques. Finally, a perspective on future research directions and applications is presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Understanding Strain and Failure of a Knot in Polyethylene Using Molecular Dynamics with Machine-Learned Potentials

A neural network potential (NNP) has been developed by fitting to ab initio electronic structure data on hydrocarbons and is used to study failure of linear and knotted polyethylene (PE) chains. A linear PE chain must be highly strained before breaking as the stress is equally distributed across the chain. In contrast, the stress in a PE chain with a 31 or overhand knot, accumulates at the knot’s entrance/exit. We find the strain energy is greatest when the bond length and angle are strained simultaneously, and that the knot weakens the chain by increasing the variance of the C–C–C angle, thereby allowing rupture at lower bond strains. Here, we extend our analysis to both 51 and 52 knots and find that both break at the entrance/exit of a loop. Notably, molecular scale PE knots exhibit many of the same characteristics as knots in a macroscopic rope, with stick–slip phenomena upon tightening and similar points of failure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Punzi-loss: a non-differentiable metric approximation for sensitivity optimisation in the search for new particles

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems

Here, we propose new symplectic networks (SympNets) for identifying Hamiltonian systems from data based on a composition of linear, activation and gradient modules. In particular, we define two classes of SympNets: the LA-SympNets composed of linear and activation modules, and the G-SympNets composed of gradient modules. Correspondingly, we prove two new universal approximation theorems that demonstrate that SympNets can approximate arbitrary symplectic maps based on appropriate activation functions. We then perform several experiments including the pendulum, double pendulum and three-body problems to investigate the expressivity and the generalization ability of SympNets. The simulation results show that even very small size SympNets can generalize well, and are able to handle both separable and non-separable Hamiltonian systems with data points resulting from short or long time steps. In all the test cases, SympNets outperform the baseline models, and are much faster in training and prediction. We also develop an extended version of SympNets to learn the dynamics from irregularly sampled data. This extended version of SympNets can be thought of as a universal model representing the solution to an arbitrary Hamiltonian system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Sub-melt nanosecond pulsed-laser induced densification and strain-field relaxation in single-crystal diamond

Dislocations and polishing-induced defect networks in synthetic diamond introduce local strain fields and broaden Raman features, limiting performance in optical, thermal, and electronic applications. Laser annealing is emerging as a promising approach to repair surface and near-surface defects in diamond without entering the melt regime, yet surface densification, defect-state modification, and associated structural changes have not been well quantified. In this work, we show that sub-melt nanosecond pulsed-laser annealing (PLA) induces near-surface densification and defect-mediated strain relaxation in single-crystal Chemical Vapor Deposition (CVD) diamond. Single- and two-pulse PLA were applied, and structural evolution was quantified using co-registered ISO 25,178 white-light interferometry, depth-resolved Raman spectroscopy, and cross-sectional STEM with geometric phase analysis (GPA). Across a 5 × 6 grid (n = 30), responsive regions exhibit large reductions in local slope (Sdq 45–65%), developed area (Sdr 60–90%), height spread (Sp, Sz 30–65%), void volume (Vv 57–60%), and roughness amplitude (Sa, Sq 48–57%), consistent with densification of ∼4–6.5 nm. Raman profiling shows narrowing of the diamond line and improved spectral uniformity to depths of ∼2–3 μm. Given that the Raman probing depth significantly exceeds the densified layer thickness, this response is interpreted as consistent with long-range strain-field redistribution originating from the near-surface region. STEM-GPA strain maps further support this interpretation, showing smoother strain fields, suppressed hotspots, and redistribution of localized strain concentrations following PLA. These results are consistent with defect-mediated strain relaxation and densification-driven modification of the near-surface energy state. The approach provides a scalable pathway for improving near-surface structural quality in diamond relevant to electronic, photonic, and quantum applications.

Areal surface metrology (ISO 25,178)↗
DOE OSTI2026

Minimization of Disorder as a Key Design Principle for Natural Sizes of Light Harvesting 2 Complexes

The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure–function relationship and can be utilized for the design of artificial light harvesting systems. To this end, we conducted comprehensive computational investigation of the dynamical and statistical nature of electronic excited states of pigment molecules in a natural LH2 complex with 9-fold symmetry and its two non-natural in silico analogues with 6- and 12-fold symmetries. To ensure reliable and efficient all-atomistic molecular dynamics simulations, we combined a well established interpolation approach for the construction of the potential energy surface with a neural network machine learning approach. Outcomes of these calculations clarify that non-natural forms of LH2-type complexes have significantly larger quasistatic disorder than those for the natural one. In addition, non-natural systems have more disruptions of the hydrogen bonding, underscoring its crucial role for reducing the disorder. On the other hand, local environmental dynamics are relatively insensitive to the structural changes although there is moderate enhancement in the anharmonic or interatomic components for the synthetic ones. These findings based on all-atomistic simulations provide direct computational evidence that the structure and sizes of natural LH2 complexes are designed to minimize the energetic disorder. We analyze quantitative implications of these for the energy transferring capability of the LH2 complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Intricate short-range order in GeSn alloys revealed by atomistic simulations with highly accurate and efficient machine-learning potentials

GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topological quantum devices. However, understanding their intricate structures using density functional theory (DFT) calculations is hindered by spatiotemporal constraints. To overcome this limitation, we develop highly accurate and efficient machine-learning interatomic potentials based on a neuroevolution potential approach with farthest point sampling on a comprehensive DFT data set. The application of the developed machine-learning potential in large-scale atomistic simulations bridges the spatiotemporal gap between modeling and advanced characterizations, and facilitates the discovery of structural intricacies in GeSn alloys. Through extensive statistical sampling, we identify a type of short-range order (SRO) that is distinguished by both its structural signature and electronic band gap from the SRO structure previously predicted. Modeling based on a large simulation cell reveals the coexistence of nano SRO domains with various degrees of ordering, demonstrating a complex spatial heterogeneity of SRO structure. Finally, our study not only reinforces the significance of fine-level structural information in alloys, but it also constitutes an effective framework for exploring SRO in a broad range of complex alloys based on highly accurate and effective machine-learning potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Laser Network US Experiments at the University of Texas at Austin

Under Subcontract B638654, The University of Texas at Austin (UT Austin) Center for High Energy Density Science supported three Laser Network US experiments and conducted upgrade planning for the Texas Petawatt Laser Facility. For the experiment “Investigation of the physics of MeV X-ray and particle generation using Compound Parabolic Concentrators” led by Dr. Andrew Mackinnon of Lawrence Livermore National Laboratory, UT Austin provided postdoctoral associate support in the person of Dr. Ganesh Tiwari. For the experiments “Megatesla magnetic fields and enhanced gamma-ray beam generation” led by Hans Rinderknecht of LLE, University of Rochester (experiment) and Alexey Arefiev of UC San Diego (theory) and “High resolution imaging of resonately accelerated electrons under tight focusing” led by Deepak Kumar of ELI-Beamlines, Czech Republic, UT Austin provided the Texas Petawatt Laser staff and a prorated share of the materials and supplies approximately equal to the average of those required for one 5-week experimental campaign. Summaries of these three experiments are provided in this report. Two staff members were hired—Dr. Sandra Bruce at 40% and Dr. Erhard Gaul at 50% time—to support Texas Petawatt Laser maintenance and operations and to plan for the Texas Relativistic Intensity Ultrafast Multi-beam Plasma Facility (TRIUMPF).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Axion domain walls, small instantons, and non-invertible symmetry breaking

Non-invertible global symmetry often predicts degeneracy in axion potentials and carries important information about the global form of the gauge group. When these symmetries are spontaneously broken they can lead to the formation of stable axion domain wall networks which support topological degrees of freedom on their worldvolume. Such non-invertible symmetries can be broken by embedding into appropriate larger UV gauge groups where small instanton contributions lift the vacuum degeneracy, and provide a possible solution to the domain wall problem. We explain these ideas in simple illustrative examples and then apply them to the Standard Model, whose gauge algebra and matter content are consistent with several possible global structures. Each possible global structure leads to different selection rules on the axion couplings, and various UV completions of the Standard Model lead to more specific relations. As a proof of principle, we also present an example of a UV embedding of the Standard Model which can solve the axion domain wall problem. The formation and annihilation of the long-lived axion domain walls can lead to observables, such as gravitational wave signals. Observing such signals, in combination with the axion coupling measurements, can provide valuable insight into the global structure of the Standard Model, as well as its UV completion.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Mesoscale Modeling of Hydrogels Under Frictional Shear Stress

Hydrogels are three-dimensional networks of hydrophilic polymers often used as a simplified model of hydrated biological materials, from cartilaginous joints to the ocular tear film. However, the lubrication mechanisms of hydrogels remain poorly understood, partly due to their complex polymeric structure, which creates blurred interfaces during sliding that are challenging to study experimentally. In this study, we employ dissipative particle dynamics (DPD) to investigate the frictional behavior of a polymeric hydrogel network sliding against a solid wall in an explicit viscous solvent. This computational approach enables us to model hydrodynamic interactions and mesoscale polymer dynamics, capturing key aspects of hydrogel friction. Our simulations reveal that hydrogel friction is governed by the interplay between polymer relaxation and viscous shear, characterized by the Weissenberg number (Wi). At low Wi, friction coefficient remain nearly constant, dominated by polymer relaxation. However, at higher Wi, friction is dominated by viscous drag within a near-wall solvent layer, leading to a linear increase in friction coefficient with Wi. Furthermore, our results demonstrate an inverse relationship between the friction coefficient and the applied normal load, consistent with experimental observations. This work provides new insights into the fundamental tribological properties of hydrogels, shedding light on the micromechanics of hydrogel friction. Improving our understanding of hydrogel structure and dynamics under friction advances our knowledge of the mechanisms regulating biological lubrication in health and disease.

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the structure sensitivity of and CO coverage effects on formic acid decomposition on Pd surfaces

Using density functional theory calculations, the Pd-catalyzed vapor-phase formic acid decomposition was studied, with a focus on the structure sensitivity and CO coverage effects. A comprehensive reaction network was developed on both the (111) and (100) facets of Pd, at CO coverages of 0 and 5/9 monolayer (ML). Pd(100) was determined to be more reactive than Pd(111) at both CO coverages. The introduction of 5/9 ML CO decreased the activity of both facets significantly, due to destabilization of the surface intermediates and transition states on the CO-decorated surfaces. Three reaction pathways were explored on the clean surfaces: the formate (HCOO) pathway, the carboxyl (COOH) pathway leading to the formation of CO 2 , and the COOH pathway leading to the formation of CO (COOH→CO). Based on the DFT-derived energetics alone, it appears that all three pathways contribute to the reaction on clean Pd, whereas the presence of 5/9 ML of CO inhibits the HCOO pathway on both facets and favors the COOH→CO pathway on the (111) facet, but the COOH→CO 2 one on the (100) facet. Moreover, at high CO coverages, alternative spectator CO-assisted adsorbate decomposition pathways were discovered, which could potentially play a role in formic acid decomposition on Pd catalysts under realistic reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2007

NASA Tech Briefs, September 2007

Topics covered include; Rapid Fabrication of Carbide Matrix/Carbon Fiber Composites; Coating Thermoelectric Devices To Suppress Sublimation; Ultrahigh-Temperature Ceramics; Improved C/SiC Ceramic Composites Made Using PIP; Coating Carbon Fibers With Platinum; Two-Band, Low-Loss Microwave Window; MCM Polarimetric Radiometers for Planar Arrays; Aperture-Coupled Thin-Membrane L-Band Antenna; WGM-Based Photonic Local Oscillators and Modulators; Focal-Plane Arrays of Quantum-Dot Infrared Photodetectors; Laser Range and Bearing Finder With No Moving Parts; Microrectenna: A Terahertz Antenna and Rectifier on a Chip; Miniature L-Band Radar Transceiver; Robotic Vision-Based Localization in an Urban Environment; Programs for Testing an SSME-Monitoring System; Cathodoluminescent Source of Intense White Light; Displaying and Analyzing Antenna Radiation Patterns; Payload Operations Support Team Tools; Space-Shuttle Emulator Software; Soft Real-Time PID Control on a VME Computer; Analyzing Radio-Frequency Coverage for the ISS; Nanorod-Based Fast-Response Pressure-Sensitive Paints; Capacitors Would Help Protect Against Hypervelocity Impacts; Diaphragm Pump With Resonant Piezoelectric Drive; Improved Quick-Release Pin Mechanism; Designing Rolling-Element Bearings; Reverse-Tangent Injection in a Centrifugal Compressor; Inertial Measurements for Aero-assisted Navigation (IMAN); Analysis of Complex Valve and Feed Systems; Improved Path Planning Onboard the Mars Exploration Rovers; Robust, Flexible Motion Control for the Mars Explorer Rovers; Solar Sail Spaceflight Simulation; Fluorine-Based DRIE of Fused Silica; Mechanical Alloying for Making Thermoelectric Compounds; Process for High-Rate Fabrication of Alumina Nanotemplates; Electroform/Plasma-Spray Laminates for X-Ray Optics; An Automated Flying-Insect Detection System; Calligraphic Poling of Ferroelectric Material; Blackbody Cavity for Calibrations at 200 to 273 K; KML Super Overlay to WMS Translator; High-Performance Tiled WMS and KML Web Server; Modeling of Radiative Transfer in Protostellar Disks; Composite Pulse Tube; Photometric Calibration of Consumer Video Cameras; Criterion for Identifying Vortices in High- Pressure Flows; Amplified Thermionic Cooling Using Arrays of Nanowires; Delamination-Indicating Thermal Barrier Coatings; Preventing Raman Lasing in High-Q WGM Resonators; Procedures for Tuning a Multiresonator Photonic Filter; Robust Mapping of Incoherent Fiber-Optic Bundles; Extended-Range Ultrarefractive 1D Photonic Crystal Prisms; Rapid Analysis of Mass Distribution of Radiation Shielding; Modeling Magnetic Properties in EZTB; Deep Space Network Antenna Logic Controller; Modeling Carbon and Hydrocarbon Molecular Structures in EZTB; BigView Image Viewing on Tiled Displays; and Imaging Sensor Flight and Test Equipment Software.

Source record↗
DOE OSTI2022

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light‐driven metabolism

SUMMARY Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome‐scale metabolic models capture species‐specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non‐photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non‐radiative energy dissipation processes, cross‐compartment electron shuttling, and non‐photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico . Overall, we present a methodology for incorporating a common, informative data type into computational models of light‐driven metabolism and show its utilization within the design–build–test–learn cycle for engineering of photosynthetic organisms.

59 BASIC BIOLOGICAL SCIENCES↗