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DOE OSTI2024

First-principles and cluster expansion study of the effect of magnetism on short-range order in Fe–Ni–Cr austenitic stainless steels

Short-range order (SRO), the regular and predictable arrangement of atoms over short distances, alters the mechanical properties of technologically relevant structural materials such as medium/high entropy alloys and austenitic stainless steels. In this study, we present a generalized spin cluster expansion (CE) model and show that magnetism is a primary factor influencing the level of SRO present in austenitic Fe-Ni-Cr alloys. The spin CE consists of a chemical cluster expansion combined with an Ising model for Fe-Ni-Cr austenitic alloys. It explicitly accounts for local magnetic exchange interactions, thereby capturing the effects of finite temperature magnetism on SRO. Model parameters are obtained by fitting to a first-principles data set comprising both chemically and magnetically diverse FCC configurations. The magnitude of the magnetic exchange interactions are found to be comparable to the chemical interactions. Compared to a conventional implicit magnetism CE built from only magnetic ground state configurations, the spin CE shows improved performance on several experimental benchmarks over a broad spectrum of compositions, particularly at higher temperatures due to the explicit treatment of magnetic disorder. We find that SRO is strongly influenced by alloy Cr content, since Cr atoms prefer to align antiferromagnetically with nearest neighbors but become magnetically frustrated with increasing Cr concentration. Using the spin CE, we predict that increasing the Cr concentration in typical austenitic stainless steels promotes the formation of SRO and increases order-disorder transition temperatures. Furthermore, this study underscores the significance of considering magnetic interactions explicitly when exploring the thermodynamic properties of complex transition metal alloys. It also highlights guidelines for customizing SRO through adjustments of alloy composition.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Phase diagram of magnetic shape memory alloy Ni 50 Mn $50–x$ In $x$ , 0 < $x$ , 25 from first principles, via spin cluster expansion and phonon vibrational entropies

The metamagnetic shape memory Heusler alloy Ni 50 Mn $50–x$ In $x$ exhibits a rich phase diagram featuring competing magnetic states, coupled magnetic–structural phase transitions, and strong compositional sensitivity. Existing first-principles approaches struggletocapturetheintertwinedchemical, magnetic, andvibrationaleffectsinthesealloys, necessitating a more integrated modeling framework. We develop a spin cluster expansion (spin-CE) framework augmented by a quasi-harmonic phonon model to capture both configurational (chemical and magnetic) and vibrational contributions to the free energy of Ni 50 Mn $50–x$ In $x$ over the full compositional range 0 ≤ x ≤25. The spin-CE includes both chemical clusters and composition-dependent Ising spin interactions, with parameters fit to a first-principles density functional theory (DFT) dataset. Using this approach, we predict the complete magnetostructural phase diagram and transformation temperatures of Ni 50 Mn $50–x$ In $x$ across the composition space. We find that vibrational entropy alone is insufficient to reproduce the martensitic transformation in the magnetic shape memory alloy regime, highlighting the essential role of magnetism. Incorporating both magnetic and vibrational contributions allows us to reproduce all experimentally known phases, including the disappearance of the stable martensite phase at a critical In concentration and the Curie temperature of the austenite phase. The method also captures the transition with increasing In in martensite from antiferromagnetic to ferromagnetic order and predicts re-entrant ferromagnetism, though the latter occurs at higher In content than reported experimentally. We discuss possible sources of this discrepancy and highlight the broader applicability of the method to other magnetostructurally complex materials, where it may offer mechanistic insight and predictive design capabilities.

Cluster expansion↗

DOE OSTI2025

Spatio-temporal multivariate cluster evolution analysis for detecting and tracking climate impacts

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. Here, this paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

Anomaly detection↗

DOE OSTI2022

Data on Cu- and Ni-Si-Mn-rich solute clustering in a neutron irradiated austenitic stainless steel

The data presented in this article is supplementary to the research article “Phase instabilities in austenitic steels during particle bombardment at high and low dose rates” (Levine et al.). Needle-shaped samples were prepared with focused ion beam milling from a 304L stainless steel that was irradiated with fast neutrons (E 0.1 MeV) in the BOR-60 reactor at 318 °C to 47.5 dpa. Atom probe tomography (APT) experiments in voltage mode were then conducted on a Cameca LEAP 5000X HR. Atom position, range, and mass spectrum files after reconstruction with Cameca’s IVAS software are included. Cu- and Ni-Si-Mn-rich solute nanoclusters were identified and analyzed using the Open Source Characterization of APT Reconstructions (OSCAR) program. Python code for OSCAR, information on the program’s underlying algorithm, and sample output files are provided. A proximity histogram of a Ni-Si-Mn-rich cluster and a 1D density/solute concentration profile of a Cu-rich cluster are given to demonstrate OSCAR’s analytical functionalities. The provided APT dataset is valuable for benchmarking phase instabilities in neutron-irradiated austenitic stainless steels that occur at high doses. The OSCAR program can be reused to process other APT data sets where solute nanoclustering is of interest.

42 ENGINEERING↗

DOE OSTI2021

Anion photoelectron spectroscopic and relativistic coupled-cluster studies of uranyl dichloride anion, UO 2 Cl 2 –

A joint relativistic coupled-cluster and experimental photoelectron (PE) spectroscopic study of the uranyl dichloride anion, UO 2 Cl 2 – , is reported. Sophisticated electronic-structure calculations predict the photodetachment of UO 2 Cl 2 – to involve a U 5f electron and to be followed by significant geometry relaxation. Therefore, the adiabatic electron affinity (EA a ) of the uranyl dichloride neutral molecule, UO 2 Cl 2 , and the vertical detachment energy (VDE) of its anion, UO 2 Cl 2 – , provide valuable information about its uranium 5f orbital energies. The EA a value was computed to be 3.15 eV. The VDE value was calculated to be 3.55 eV by augmenting the computed EA a with a shift derived from a Franck–Condon simulation using coupled-cluster potential energy surfaces. Here, the VDE, which corresponds to the highest intensity peak in the PE spectrum, was measured to be 3.69 ± 0.20 eV, in good agreement with the computed value. The origin transition in the PE spectrum, whose electron binding energy corresponds to the EA a , was assigned to the feature at 3.2 ± 0.20 eV, consistent with the computed EA a .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Cluster dynamics modeling of Mn-Ni-Si precipitates coupled with radiation-induced segregation in low-Cu reactor pressure vessel steels

Formation of precipitates enhanced or induced by irradiation causes hardening and embrittlement of nuclear structural materials. Post-irradiation microstructure characterization of reactor pressure vessel (RPV) steels has shown that precipitation can be strongly associated with radiation-induced segregation (RIS) of solutes on dislocations. However, RIS and precipitation have not been coupled in previous precipitation modeling of RPV steels. Here in this study, a new hybrid and spatially-dependent precipitation model is developed that couples cluster dynamics with RIS, providing an unique way to account for concurrent evolution of heterogeneous cluster densities as well as solute and point defect concentration profiles. The model is applied to study the segregation of Mn, Ni, and Si on dislocations and heterogeneous nucleation of Mn-Ni-Si rich precipitates (MNSPs) in a low-Cu RPV steel. The result shows that the onset of MNSP nucleation on dislocations occurs at the fluence of 2 x 10 23 n.m -2 . The number density and mean radius can reach ~ 10 24 m -3 and 1-2 nm at high fluence, respectively. These observations suggest that the role of RIS and heterogeneous nucleation can be significant for RPV steels under a high fluence of irradiation. Results of simulations adopting various dose rates and dislocation densities show an increased fraction of MNSPs on dislocations at the condition of low dose rate, high dislocation density, and high fluence.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity

Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗

DOE OSTI2022

Gamma-Ray-Induced Formation of Uranyl Peroxide Cage Clusters

Aqueous solutions of lithium uranyl triperoxide, Li 4 [UO 2 (O 2 ) 3 ] (LiUT), were irradiated with gamma rays at room temperature and found to form the uranyl peroxide cage cluster, Li 24 [(UO 2 )(O 2 )(OH)] 24 (Li–U 24 ). Raman spectroscopy and 18 O labeling were used to identify the Raman-active vibrations of LiUT. With these assignments, the concentration of LiUT was tracked as a function of radiation dose. A discrepancy between monomer removal and cluster formation suggests that the reaction proceeds by the assembly of an intermediate. Non-negative matrix factorization was used to separate Raman spectra into components and resulted in the identification of a unique intermediate species. Much of the conversion appears to be driven by water radiolysis products, particularly the hydroxyl radical. Furthermore, this differs from the 18 O-labeled copper-catalyzed formation of U 24 , which progresses at a steady rate with no observation of intermediates. Li–U 24 in solution decomposes at high radiation doses resulting in a solid insoluble product similar to Na-compreignacite, Na 2 (UO 2 ) 6 O 4 (OH) 6 ·7H 2 O, which contains uranyl oxyhydroxy sheets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Developing Lanthanide-Nitrate Cluster Chemistry toward Rare Earth Separations

Nitrate-decorated hexamers with a [Ln 6 (μ 6 -O)(μ 3 –OH) 8 ] 8+ core have been reported for nearly every lanthanide ion and are used as precursors for the assembly of functional metal–organic frameworks. Yet, few studies have examined the correlation between the solution and solid-state species, and the formation of mixed-metal clusters. Toward this end, a series of homo- and heterometal lanthanide nitrate hexamers was prepared via pH adjustment of aqueous lanthanide nitrate solutions. Examination of the homometallic europium solutions using Small Angle X-ray Scattering and nESI-MS showed that lower order complexes dominate lanthanide speciation in nitrate media. Yet, powder X-ray diffraction data of the precipitated phase confirmed the formation of [Ln 6 (μ 6 -O)(μ 3 -OH) 8 (NO 3 ) 6 (H 2 O) 12 ]·2(NO 3 )·n(H 2 O), Ln 6 , for Ln = Eu and Tb. For heterometal systems, analysis of the solid-state product by ICP–MS showed the selective incorporation of the heavier rare earths into Ln 6 . Selectivity was quantified by calculating an average separation factor, which is defined as the ratio of recovery factors of both metals. Further examination of the luminescence behavior of mixed metal [Tb 6–x Eu x (μ 6 -O)(μ 3 -OH) 8 (NO 3 ) 6 (H 2 O) 12 ]·2(NO 3 )·n(H 2 O), with x = 1.1–3.6, showed that the relative intensities of the peaks at 489 nm (terbium, 5 D 4 → 7 F 6 ) and 690 nm (europium, 5 D 0 → 7 F 4 ) trend with the percent incorporation of europium and terbium into the cluster.

anions↗

DOE OSTI2026

Local Pair Natural Orbital-Based Coupled-Cluster Theory through Full Quadruples (DLPNO–CCSDTQ)

In this work, we implement a local pair natural orbitalbased coupled-cluster method through the full treatment of quadruple excitations (CCSDTQ). The domain-based local pair natural orbital (DLPNO) approach, which has successfully been applied to lower levels of coupled-cluster theory, is utilized in our algorithm, and thus our algorithm is called DLPNO-CCSDTQ. For simplicity in the working equations and in the implementation, we t 1 -dress the twoelectron integrals as well as Fock matrix elements. Our method can recover CCSDTQ-CCSDT and CCSDTQ-CCSDT(Q) energy differences on the order of 0.01−0.05 kcal mol −1 , even at a loose quadruples natural orbital (QNO) occupation number cutoff of 3.33 × 10 −6 . To highlight the capabilities of our code and its potential future applications, we showcase computations that would be intractable with canonical CCSDTQ, such as the benzene dimer, (H 2 O) 17 , and adamantane. With sufficient computing resources, computations up to 15 heavy atoms (40 atoms overall) may be feasible for fully bonded 3D systems.

Cluster chemistry↗

DOE OSTI2020

Flash Formation of I-Rich Clusters during Multistage Halide Segregation Studied in MAPbI 1.5 Br 1.5

Wide band gap mixed halide perovskites such as MAPb(I 1–x Br x ) 3 sparked great research interest because of their outstanding optoelectronic properties, ease of fabrication, and band gap tunability. Their application thus far is however limited by light-induced halide segregation in which microscopic clusters with a high iodide content are formed and act as recombination centers. The key mechanism(s) underlying this halide segregation process are still debated. Here, we present a study on the photoluminescence evolution in MAPb(I 1.5 Br 1.5 ) perovskites with varying microstructures under constant illumination at room temperature and at elevated temperature. Our findings reveal a more complicated picture of the segregation mechanism occurring in three stages instead of two as commonly reported. The process starts with a flash formation of I-rich domains. Following is a rapid blue shift before the gradual and typically observed red shift occurs. The evolution of the three stages is fully reversible in the dark and is also present at elevated temperatures (50 °C). We explain the existence of multiple stages during light-induced halide segregation by natural compositional fluctuations of the halides and the formation of halide clusters with a dynamically changing distribution in I–Br content. The variation in the I–Br ratio depends on the grain size and film heterogeneity. Overall, these findings add further details in the quest of unraveling the underlying segregation mechanism(s), which need to be identified to stabilize halides in wide band gap perovskites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. In conclusion, through this computational material design study, a previously unexplored mechanism in PL formation, solvent–solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

Carbon capture↗

DOE OSTI2022

How a Formate Dehydrogenase Responds to Oxygen: Unexpected O 2 Insensitivity of an Enzyme Harboring Tungstopterin, Selenocysteine, and [4Fe–4S] Clusters

The reversible two-electron interconversion of formate and CO 2 is catalyzed by both nonmetallo- and metallo-formate dehydrogenases (FDHs). The latter group comprises molybdenum- or tungsten-containing enzymes with the metal coordinated by two equivalents of a pyranopterin cofactor, a cysteinyl or selenocysteinyl (Sec) ligand supplied by the polypeptide, and a catalytically essential terminal sulfido ligand. In addition, these biocatalysts incorporate one or more [4Fe–4S] clusters for facilitating long-distance electron transfer. However, an interesting dichotomy arises when attempting to understand how the metallo-FDHs react with O 2 . Whereas existing scholarship portrays these enzymes as being unable to perform in air due to extreme O 2 lability of their metal centers, studies dating as far back as the 1930s emphasize that some of these systems exhibit formate oxidase (FOX) activity, coupling formate oxidation to O 2 reduction. Therefore, to reconcile these conflicting views, we explored context-dependent functional linkages between metallo-FDHs and their cognate electron acceptors within the same organism vis-à-vis catalysis under atmospheric O 2 . Here, we report the discovery and characterization of an O 2 -insensitive FDH2 from the sulfate-reducing bacterium Desulfovibrio vulgaris Hildenborough (DvH) that ligates tungsten, Sec, and four [4Fe–4S] clusters. By advancing a robust expression platform for its recombinant production, we eliminate both the requirement of nitrate or azide during purification and reductive activation with thiols and/or formate prior to catalysis. Because the distinctive spectral signatures of formate-reduced DvH-FDH2 remain invariant under anaerobic and aerobic conditions, we benchmarked the enzyme activity in air, identifying CO 2 as the catalytic product. Full reaction progress curve analysis discloses a high catalytic efficiency when probed with a high-potential artificial electron acceptor. Furthermore, we show that DvH-FDH2 enables near-stoichiometric hydrogen peroxide production without superoxide release to achieve O 2 insensitivity. Notably, simultaneous electron transfer to cytochrome c and O 2 reveals that metal-based electron bifurcation is operational in this system. Taken together, our work proves the co-occurrence of redox bifurcated FDH and FOX activities within a metalloenzyme scaffold. These findings set the stage for uncovering previously unknown O 2 -insensitive flavin-based electron bifurcation mechanisms, as well as for developing authentic formate/air biofuel cells, engineering O 2 -stable FDHs and biohybrid metallocatalysts, and discerning formate bioenergetics of gut microbiota.

13C NMR↗

DOE OSTI2025

Spatial Distribution and Clustering of Glycosaminoglycans in Electrospun Gelatin-Based Scaffolds

The extracellular matrix (ECM) is comprised of components like collagen, elastin, and glycosaminoglycans (GAGs). Electrospun fibrous scaffolds are designed to replicate the form and composition of the native ECM, often requiring blending of various ECM component mimics to enhance cellular responses. However, the spatial distribution of blended components within these fibers remains unclear. This study investigates the spatial distribution of chondroitin sulfate-C (CSC) in electrospun gelatin-based scaffolds. scanning electron microscopy (SEM), attenuated reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Time-of-flight Secondary Ion Mass Spectrometry (ToF-SIMS) were applied for surface and subsurface chemical characterization of the fibrous scaffolds. SEM confirmed a fibrous morphology, while ATR-FTIR and XPS analyses indicated the presence of CSC through the identification of sulfate groups. ToF-SIMS imaging, alongside K-means clustering and Ripley’s K function, revealed a nonuniform CSC distribution with higher concentrations at the top layer of the scaffold. This study demonstrates that CSC presentation at the fiber surface varies with depth and differs from bulk incorporation while reveals nanoscale clustering and spatial heterogeneity at both the surface and subsurface of electrospun gelatin fibers. These findings define an underexplored design consideration with potential to influence cell–scaffold interactions.

Animal derived food↗

DOE OSTI2020

CO 2 Hydrogenation to Formate and Formic Acid by Bimetallic Palladium–Copper Hydride Clusters

Mass spectrometric analysis of the anionic products of interaction between bimetallic palladium-copper tetrahydride anions, PdCuH 4 – , and carbon dioxide, CO 2 , in a reaction cell shows an efficient generation of the PdCuCO 2 H 4 – intermediate and formate/formic acid complexes. Multiple structures of PdCuH 4 – and PdCuCO 2 H 4 – are identified by a synergy between anion photoelectron spectroscopy and quantum chemical calculations. The higher energy PdCuH 4 – isomer is shown to drive the catalytic hydrogenation of CO 2 , emphasizing the importance of accounting for higher energy isomers for cluster catalytic activity. In conclusion, this study represents the first example of CO 2 hydrogenation by bimetallic hydride clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Characterization of Surface Heterogeneity-Induced Convection Using Cluster Analysis

Land-atmosphere interactions and boundary layer processes often control the formation of shallow clouds and subsequently deep convective precipitation over the Southern Great Plains. In this study, we examine the impacts of large-scale advection on the cloud populations and land-atmospheric coupling observed during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign in 2016. In this work, we performed two Large Eddy Simulations (LES) using the Weather Research and Forecasting Model for a day in which the transition from clear-sky to shallow precipitating clouds and isolated deep convection was observed. The control simulation reproduced the overall distribution of cloud populations by using realistic soil conditions with an interactive land model. In the sensitivity simulation where large-scale advection is removed, a strong relationship between the land and boundary layer is found. To study the timing, location, and intensity of convective initiation and the relationship of clouds with land surface properties, a cluster analysis of equivalent potential temperature is performed for the simulation without large-scale advection. That analysis shows that convective clouds first form over regions with higher surface sensible heat flux. Precipitation from those convective clouds likely triggers new updrafts nearby about two hours later through the lifting associated with cold pools. Furthermore, the cluster analysis also shows that in addition to the spatial pattern of soil moisture, land use and soil texture in western Oklahoma also influence the location of convective initiation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Structure of the respiratory MBS complex reveals iron-sulfur cluster catalyzed sulfane sulfur reduction in ancient life

Abstract Modern day aerobic respiration in mitochondria involving complex I converts redox energy into chemical energy and likely evolved from a simple anaerobic system now represented by hydrogen gas-evolving hydrogenase (MBH) where protons are the terminal electron acceptor. Here we present the cryo-EM structure of an early ancestor in the evolution of complex I, the elemental sulfur (S 0 )-reducing reductase MBS. Three highly conserved protein loops linking cytoplasmic and membrane domains enable scalable energy conversion in all three complexes. MBS contains two proton pumps compared to one in MBH and likely conserves twice the energy. The structure also reveals evolutionary adaptations of MBH that enabled S 0 reduction by MBS catalyzed by a site-differentiated iron-sulfur cluster without participation of protons or amino acid residues. This is the simplest mechanism proposed for reduction of inorganic or organic disulfides. It is of fundamental significance in the iron and sulfur-rich volcanic environments of early earth and possibly the origin of life. MBS provides a new perspective on the evolution of modern-day respiratory complexes and of catalysis by biological iron-sulfur clusters.

59 BASIC BIOLOGICAL SCIENCES↗