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Combinatorial Al 1− x Sc x N library decouples composition, crystal structure, and ferroelectric properties. The local chemical bonding is the key factor to control ferroelectric properties rather than extended crystal structure.
Synthetic biology generates vast combinatorial designs, yet high-throughput analytical methods to screen them are poorly matched to interrogate this search space. We address this challenge by developing a biosensor-driven, growth-coupled selection strategy in Pseudomonas putida for isoprenol, a potential aviation fuel precursor. We found and characterized a noncanonical signaling pathway, revealing a functional and physical complex between a hybrid histidine kinase and an alcohol dehydrogenase, whose activity is tuned by heterodimerization. Leveraging this biosensor in a pooled CRISPRi library selection, we identified key host limitations. Iterative combinatorial strain engineering derived from these hits yielded a 36-fold titer increase to ~900 milligrams per liter. Integrated omics analysis revealed that metabolic rewiring toward amino acid catabolism was crucial for this improvement. This observation was found to be beneficial by technoeconomic analysis. Our modular workflow provides a powerful strategy for optimizing complex heterologous pathways and uncovering emergent host biology.
A thin film combinatorial synthesis approach was utilized to develop a compositional and structural library for the OsRuWCo alloy system. The survey of this alloy space produced 24 unique compositions and their corresponding crystal structures, which represent a phase map where amorphous and single-phase hexagonal close-packed alloys were identified. Based on the identification of a promising candidate film region, a new high entropy alloy based on OsRuWCo, but with non-equiatomic composition, was synthesized in bulk form. The alloy exhibited a single-phase hexagonal close-packed structure in the as-cast state. Three derivatives from this system were also developed by considering the heat of mixing, atomic size, and binary solubility. These new alloys are based on OsRuWCoIr and OsRuWCoFe, both of which exhibit single-phase hexagonal close-packed as-cast structures, as determined by X-ray diffraction and electron microscopy. Additionally, this large compositional space was utilized to evaluate conventional parameters that describe high entropy alloys (ΔS mix , ΔH mix , Ω, δ, and VEC), and these were found to elucidate the criteria for solid solution formation in this system. Additionally, trends illustrating the evolution from amorphous to crystalline phases are demonstrated and discussed. It is concluded that in this particular case, the dominant factors promoting crystallinity are thermodynamic or electronic, rather than geometric.
A high‐throughput ion beam sputtering system is used to synthesize compositional gradient superlattice‐like (SLL) thin film libraries of Ge–Sb–Te alloys over the entire phase diagram. Here, the optical properties and structural evolution of the Ge–Sb–Te combinatorial SLL thin film are investigated. A systematic screening over the annealing temperature, annealing time, and modulation period has elucidated the critical factors that affect the stability of the metastable phase and optical properties. It is found that amorphous stability and optical constant are highly dependent on the modulation period and chemical composition of the thin film. This data‐driven approach offers new perspectives for accelerating the development of new materials with excellent optical and amorphous stability and for exploring their mechanisms, by greatly expanding the dataset of Ge–Sb–Te alloys with SLL structures through high‐throughput experiments.
Sputter-deposited, nanocrystalline Cu-Ag thin films produced across a broad compositional and deposition-parameter space were evaluated to unravel the process-structure-property relationships important for creating hard, conductive electrical contacts and coatings. Combinatorial deposition involving pulsed direct current magnetron sputtering of elemental targets enabled swift examination of nearly the full range of alloy compositions and a relevant portion of deposition atomistics. Several high-throughput characterization modalities were employed to evaluate the chemistry, structure, and properties of the films. The resultant hardness, modulus, film density, crystal texture, and resistivity were analyzed in terms of key deposition characteristics (incident atom kinetic energy and incidence angle) predicted by binary-collision, kinematic Monte Carlo simulations. The study revealed improved hardness, parabolic resistivity dependence on composition, and compositional and process dependencies of film tarnishing. The results are discussed in the context of variations in microstructure and film density. Transmission electron microscopy and X-ray diffraction demonstrate several forms of compositional variation including solute segregation to grain boundaries as well as periodic, intragranular compositional modulations. Annealing of a Cu-rich alloy film exhibiting grain boundary segregation showed that this as-deposited, compositional variation is not stable above 100 °C. Finally, the Cu-Ag system is shown to have potential for hard, conductive, tarnish-resistant and room temperature-stable nanocrystalline thin films across the composition space.
Activating inert substrates is a challenge in nature and synthetic chemistry, but essential for creating functionally active molecules. In this work, we used a combinatorial optimization approach to assemble cytochrome P450 monooxygenases (CYPs) and reductases (CPRs) to achieve a target product profile. By creating 110 CYP-CPR pairs and iteratively screening different pairing libraries, we demonstrated a framework for establishing a CYP network that catalyzes six oxidation reactions at three different positions of a chemical scaffold. Target product titer was improved by remodeling endoplasmic reticulum (ER) size and spatially controlling the CYPs’ configuration on the ER. Out of 47 potential products that could be synthesized, 86% of the products synthesized by the optimized network was our target compound quillaic acid (QA), the aglycone backbone of many pharmaceutically important saponins, and fermentation achieved QA titer 2.23 g/L.
Resolving the local structure motifs that characterize phase evolution as a function of composition is a key challenge in structure characterization of complex materials. Here, in this study, we combine first-principles simulations and x-ray absorption near-edge structures (XANES) analysis to gain insights into the structure evolution revealed by measurements across a combinatorial zinc titanate thin film, which was grown with smoothly varying composition over a wide range of the Ti:Zn ratio. Specifically, we propose a cluster blind-signal-separation (cBSS) method for XANES spectral analysis based on a library of the structures and spectra of representative local motifs. In addition to motifs from zinc titanate crystals, two types of Ti-defect models constructed in this study are key to the understanding of the structure characteristics in the Zn-rich region. The cBSS method makes use of both spectral clustering of the simulated site-XANES spectra library and the BSS procedure to construct high-fidelity spectral basis functions from an experimental spectral sequence. The method provides a rigorous measure of the spectral sensitivity and basis completeness. The results of the XANES analysis are corroborated with other experimental modalities, including x-ray diffraction and spectroscopic ellipsometry, to validate the cBSS method. The calculated motif weights resulting from fitting the XANES spectra with the cBSS basis probe the atomic structure characteristics of both crystalline and amorphous phases as a function of the Ti/Zn composition. The insights of the local structure motif evolution are pivotal to the understanding of the nonmonotonic trend in the optical gap, which may lead to potential applications through tuning the optical properties of zinc titanate. The workflow of the XANES spectral analysis developed in this work can be generalized to construct the structure-property relationship in a broad material space.
Coupling plasmonic and functional materials provides a promising way to generate multifunctional structures. However, finding plasmonic nanomaterials and elucidating the roles of various geometric and dielectric configurations are tedious. This work describes a combinatorial approach to rapidly exploring and identifying plasmonic heteronanomaterials. Symmetry-broken noble/non-noble metal particle heterojunctions (~100 nanometers) were synthesized on multiwindow silicon chips with silicon nitride membranes. The metal types and the interface locations were controlled to establish a nanoparticle library, where the particle morphology and scattering color can be rapidly screened. By correlating structural data with near- and far-field single-particle spectroscopy data, we found that certain low-energy plasmonic modes could be supported across the heterointerface, while others are localized. Furthermore, we found a series of triangular heteronanoplates stabilized by epitaxial Moiré superlattices, which show strong plasmonic responses despite largely comprising a lossy metal (~70 atomic %). These architectures can become the basis for multifunctional and cost-effective plasmonic devices.
The Joint BioEnergy Institute (JBEI) researchers were co-inventors of the technology that will be used on this project and have developed a more current version of the chip and controller. JBEI will also assist with the design of the pathways and implementation of pathways on the chip. LanzaTech has developed novel gas fermentation technology that captures and utilizes greenhouse gases for production of fuels and chemicals. In contrast to traditional fermentation that uses sugars as substrate (and releases CO2 as a byproduct), gas fermentation utilizes C1 substrates carbon monoxide (CO) or CO2. This enables a diverse range of feedstock options including waste gases from industrial sources (e.g., steel mills and processing plants) or syngas generated from any biomass resource (e.g., agricultural waste, municipal solid waste, or organic industrial waste). Biomass is then gasified, allowing for maximum yields and complete carbon utilization including the recalcitrant lignocellulosic fraction that cannot be utilized in traditional sugar fermentation. To maximize the value that can be added to the array of gas resources that the LanzaTech process can use as an input, LanzaTech has pioneered genetic modification of acetogens and developed a comprehensive set of genetic tools to perform routine strain modification, including genome editing tools as CRISPR/Cas9 and libraries of validated genetic parts as promoters and terminators. Using this platform, production of over 50 new molecules have been demonstrated directly from gas. For a few selected molecules production rates and yields have been optimized and surpass production of native producers and engineered E. coli or yeast strains, but a higher throughput approach for combinatorial optimization of pathways is required to further advance synthesis of additional products in parallel.
This work analyzes the rules governing the growth of the numbers of vertices, edges and faces in all possible periodic tessellations of the 2D Euclidean space, and encodes those rules in several types of polynomial growth functions. These encodings map the geometric, combinatorial and topological properties of the tessellations into sets of integer coefficients. Several general statements about these encodings are given with rigorous mathematical proof. The variation of the growth functions is represented graphically and analyzed in orphic diagrams, so named because of their similarity to orphic art. Several examples of 3D space groups are included, to emphasize the complexity of the growth functions in higher dimensions. A freely available Python library is presented to facilitate the discovery of the growth functions and the generation of orphic diagrams.
Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. Here, the standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.
With the support of DoE’s EERE office, our team has established a unique tool-chest of capabilities, including theoretical modeling (Lawrence Livermore National Laboratory: LLNL), state-of-the-art synthesis (Hawaii Natural Energy Institute: HNEI, Stanford, and the National Renewable Energy Laboratory: NREL) and advanced materials and interfaces characterization (University of Nevada, Las Vegas: UNLV, and Lawrence Berkeley National Laboratory: LBNL), to accelerate the development of high efficiency and durable chalcopyrite materials for advanced photoelectrochemical (PEC) water splitting. Using this synergistic approach, we have successfully created new wide bandgap chalcopyrite photocathodes generating over 10 mA/cm 2 , developed innovative strategies to protect them from corrosion, and engineered novel integration methods to circumvent thin film materials mechanical, chemical and thermal incompatibility. In Task 1 “Modeling and synthesis of chalcopyrite photocathodes”, we expanded our library of wide bandgap chalcopyrites for PEC water splitting. With support from LLNL’s “Computational Materials Diagnostics and Optimization of PEC Devices”, LBNL’s “photophysical” and NREL’s “I-III-VI Compound Semiconductors for Water-Splitting” nodes, we investigated two new chalcopyrite candidates for PEC water splitting: Cu(In,Al)Se 2 and Cu(In,B)Se 2 . We also further developed ordered vacancy compounds, such as CuGa 3 Se 5 , with unprecedented durability during PEC waters splitting in acidic solutions. In Task 2 “Interfaces engineering for enhanced efficiency and durability”, we addressed both the non-ideal band-edge positions of chalcopyrites with respect to water redox potentials, as well as their chemical instability under PEC water splitting, with a buried-junctions approach. With help from NREL’s “High-Throughput Experimental Thin Film Combinatorial Capabilities” and “Corrosion Analysis of Materials” nodes, we engineered environmentally friendly n-type buffers, including Mn x Zn 1-x O, to adjust the chalcopyrite band-edge positions and achieved photovoltages as high as 925 mV. Also, we integrated non-precious catalytic-protecting layers, such as WO 3 , to enhance the water splitting long-term stability of chalcopyrite absorbers. Finally, in Task 3 “Hybrid photoelectrode device integration”, we proposed an innovative method to bond wide bandgap photocathodes onto narrow bandgap PV drivers at room temperature using conductive polymers. Our semi-monolithic approach addressed fundamental processing incompatibility issues, as both the photocathode and the PV driver are processed separately. Proof-of-concept whole-chalcopyrite tandems were obtained by consecutive exfoliation and transfer of fully integrated 1.85 eV CuGa 3 Se 5 and 1.13 eV CuInGaSe 2 stacks from their Mo/SLG substrates onto a new single FTO host substrate.
Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) have the potential to demonstrate significant run-time performance benefits over current state-of-the-art solutions. Using existing methods for characterizing classical optimization algorithms, we analyze solution quality obtained by solving Max-Cut problems using a quantum annealing device and gate-model quantum simulators and devices. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through effective visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. The framework is an enhancement to the existing open-source QED-C Application-Oriented Benchmark suite and can connect to the open-source analysis libraries. The suite can be executed on various quantum simulators and quantum hardware systems.
The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. In conclusion, we numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.