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DOE OSTI2024

System and method for wave prediction

A method and system for prediction of wave properties include collecting time series data streams from one or more wave measurement devices and processing the data using a wave-prediction algorithm to identify the frequency components of the data and compute wave parameters. The wave-field is propagated in space and time to predict wave height, speed, and velocity at a target location. A sliding window approach is used to continuously update the prediction in real-time.

Previsic, Mirko↗

DOE OSTI2024

System and method for wave prediction

A method and system for prediction of wave properties include collecting time-series data streams from one or more wave measurement devices and processing the data to identify data parameters to establish boundary conditions of a numerical model. The numerical model may be used to compute a predicted wave field of time-series data for a variety of wave properties at a target location.

Previsic, Mirko↗

DOE OSTI2026

The DECam MAGIC Survey $-$ Mapping the Ancient Galaxy in CaHK: Overview and Summary of Early Science

We present the DECam Mapping the Ancient Galaxy in CaHK (MAGIC) survey, a 54-night NOIRLab Survey Program to image $\gtrsim$5,000$\,$deg$^2$ of the southern hemisphere using a metallicity-sensitive narrow-band filter covering the Ca$\,$ii$\,$H&K lines centered at 3955$\,$A. This filter is installed on the Dark Energy Camera (DECam), mounted on the 4-m NSF VÃctor M. Blanco Telescope. The survey reaches typical $10Ï$ depths of $\text{mag}_{\text{CaHK}} \approx 22.5$, 3$-$4$\,$mag deeper than comparable surveys in the southern hemisphere. By combining photometry from this Ca$\,$ii$\,$H&K filter with existing DECam $g,r,i$ broadband photometry from the DECam Local Volume Exploration (DELVE) survey, MAGIC is deriving photometric metallicities for red giant branch stars down to the magnitude limit of usable proper motions from Gaia data release 3 (DR3). MAGIC has already imaged $\sim$3,000$\,$deg$^2$, supplemented by other affiliated observing programs that have used this filter to image star clusters, dwarf galaxies, and stellar streams. We overview MAGIC's survey strategy, describe data processing through the derivation of metallicities and photometric distances, and summarize early science results that have been published with this dataset. In addition, we present several new results, including the confirmation of a distant ($>5\,r_h$) member of the Reticulum II ultra-faint dwarf galaxy, on-sky density maps of low-metallicity stars into the distant Milky Way halo ($\sim150\,$kpc) recovering 13/14 ultra-faint dwarf galaxies in the current footprint, and a validation of our initial targeting of extremely metal-poor stars. Collectively, these results demonstrate that the MAGIC dataset enables cutting-edge studies of the faint, low-metallicity regime of the Milky Way and its substructures.

Chiti, A. [KIPAC, Menlo Park] (ORCID:0000000271556↗

DOE OSTI2024

Streaming Matching and Edge Cover in Practice

Graph algorithms with polynomial space and time requirements often become infeasible for massive graphs with billions of edges or more. State-of-the-art approaches therefore employ approximate serial, parallel, and distributed algorithms to tackle these challenges. However, such approaches require storing the entire graph in memory and thus need access to costly computing resources such as clusters and supercomputers. In this paper, we present practical streaming approaches for solving massive graph problems using limited memory for two prototypical graph problems: maximum weighted matching and minimum weighted edge cover. For matching, we conduct a thorough computational study on two of the semi-streaming algorithms including a recent breakthrough result that achieves a $1/(2+\varepsilon)$-approximation of the weight while using $O( n \log W /\epsilon)$ memory (here $n$ is the number of vertices and $W$ is the maximum edge weight), designed by Paz and Schwartzman [SODA, 2017]. Empirically, we show that the semi-streaming algorithms produce matchings whose weight is close to the best $1/2$-approximate offline algorithm while requiring less time and an order-of-magnitude less memory. For minimum weighted edge cover, we develop three novel semi-streaming algorithms. Two of these algorithms require a single pass through the input graph, require $O(n \log n)$ memory, and provide a 2-approximation guarantee on the objective. We also leverage a relationship between approximate maximum weighted matching and approximate minimum weighted edge cover to develop a two-pass $3/2+\epsilon$-approximate algorithm with the memory requirement of Paz and Schwartzman's semi-streaming matching algorithm. These streaming approaches are compared against the state-of-the-art 3/2-approximate offline algorithm. The semi-streaming matching and the novel edge cover algorithms proposed in this paper can process graphs with several billions of edges in under 30 minutes using 6 GB of memory, which is at least an order of magnitude improvement from the offline (non-streaming) algorithms. For the largest graph, the best alternative offline parallel approximation algorithm (GPA+ROMA) could not finish in three hours even while employing hundreds of processors and 1 TB of memory. We also demonstrate an application of the semi-streaming algorithm by computing a matching using linearly bounded memory on item intersection graphs derived from three machine learning datasets, whereas the existing offline algorithms could not complete on one of these datasets since their memory requirements exceeded 1TB.

Ferdous, S M.↗

DOE OSTI2025

Development of Biological and Electrochemical Technologies for the Clean Extraction of Copper and Critical Materials from Low Grade Ores

As we transition toward renewable energy resources and electrification, there will be an increasing demand for critical materials, including copper. While copper is currently mined in the US, processing capacity is not sufficient, and intermediate mining products are shipped to Asia for further processing. The goal of this research was to develop a transformative hydrometallurgical process for the production of copper from low-grade ores that would eliminate the need for smelting and would increase the domestic processing capacity in the US, The project initially focused on the electrochemical reduction of copper concentrate using vanadium, followed by the biological oxidation to produce a stream compatible with existing solvent extraction and electrowinning operations. We discovered an efficient electrochemical process that could produce copper salts from concentrate without the need for biological oxidation, and this technology has been licensed and spun-off into a start-up company. The microbes involved in the current state-of-the-art bioleaching processes were genetically modified to introduced a number of new traits, including increased sulfur oxidation, salt-tolerance, and binding of other critical metals such as cobalt, molybdenum, rhenium, and the rare earth elements. The project was extended to also explore the electrochemical oxidation of copper concentrate using cerium which would potentially eliminate production of hydrogen sulfide that occurs with the reductive leaching process. This technology was found to have slower kinetics, however could it still be developed as an alternative process for domestic copper production and has been found to be applicable to other critical minerals.

42 ENGINEERING↗

DOE OSTI2025

PFAS Waste Management for Industrial, Water, and Wastewater Treatment Facilities

Per- and polyfluoroalkyl substances (PFAS), including perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), are widely used in industrial and consumer products due to their unique water and oil repelling properties. These slow-degrading and bio-accumulating compounds, often called "forever chemicals," are not typically removed by conventional industrial and municipal water/wastewater (W/WW) treatment. This tip of the month provides comprehensive strategies for managing PFAS-contaminated waste streams. Understanding the various types of PFAS waste streams is the first step in managing them. Primary PFAS waste includes contaminated process W/WW and solid materials directly from the production and processing of PFAS-containing materials. These might come from manufacturing facilities, firefighting training areas, or other industrial operations where PFAS compounds are used or were historically present. Secondary PFAS waste results from treatment and disposal mechanisms of primary PFAS waste streams, including spent activated carbon, used ion exchange resins, membrane reject streams, PFAS-laden backwash water, landfill leachate, incineration residuals, etc. TABLE 1 lists examples of the most common PFAS waste streams in different industrial facilities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids

Understanding aquatic ecosystem metabolism involves the study of two key processes: carbon fixation via primary production and organic C mineralization as total ecosystem respiration (ERtot). In streams and rivers, ERtot includes respiration in the water column (ERwc) and in the sediments (ERsed). While literature surveys suggest that ERsed is often a dominant contributor to ERtot, recent studies indicate that the relative influence of sediment-associated processes versus water column processes can fluctuate along the river continuum. Still, a comprehensive understanding of the factors contributing to these shifts within basins and across stream orders is needed. Here we contribute to this need by measuring ERwc and collecting water samples across 47 sites in the Yakima River basin, Washington, USA. We found that ERwc rates varied throughout the basin during baseflow conditions, ranging from –7.38 to 0.36 g O2 m?3 d?1, and encompassed the range of ERwc literature values. Additionally, by comparing to ERtot estimates for rivers across the contiguous United States, we suggest that the contribution of ERwc rates to reach-scale ERtot rates across the Yakima River was likely highly variable, but we did not test this directly. We observed that temperature, nutrient concentrations (dissolved organic carbon, total dissolved nitrogen), and total suspended solids explained 41% of ERwc variability across the basin. Our findings highlight the potential relevance of water column processes in aquatic ecosystem metabolism, with the Yakima River basin serving as an environmentally diverse river network representative of the larger Columbia River basin that spans much of the Pacific Northwest region of the United States. Our results are generally congruent with previous work, suggesting that the observed variability and suite of associated environmental factors influencing ERwc are potentially transferable across basins.

Laan, Maggi M.↗

DOE OSTI2025

Bioconversion of self-neutralized chemically depolymerized lignin streams into polyhydroxyalkanoates

Lignin is a chemically complex, diverse, and abundant plant polymer mainly composed of aromatic monomers. These aromatic monomers make lignin a potential source of aromatics and a viable substitute for petrochemically-derived aromatics. However, the structural recalcitrance of lignin requires harsh reagents from the chemical process and expensive catalysts for effective depolymerization. This chemical process often results in poor yields of chemical intermediate mixtures of varying bioavailability and/or toxicity. Furthermore, the cost of additional reagents required to separate or detoxify these intermediates makes processing lignin impractical. We report progress towards the use of such chemically depolymerized lignin streams by employing bacterial strains to produce polyhydroxyalkanoates (PHA). PHAs are a group of biodegradable microbial polyesters that have potential as a replacement for petroleum-based plastics. In this study we utilized two distinct lignin streams obtained after chemical depolymerization of lignin under alkaline and acidic pH in the presence of catalysts. We mixed the alkali-treated depolymerized stream with the acid-treated depolymerized stream to create a solution of neutral-pH chemically depolymerized lignin (CDL). We found moderate to substantial growth of both native and non-native PHA producers on the mixture of CDL as well as its aliphatic and aromatic components. PHA was detected by Sudan Black B staining in C. necator H16, P. putida KT2440, and E. coli LSBJ STQKAB grown on mixed CDL as the sole carbon source. For C. necator H16 and E. coli LSBJ STQKAB we found that PHA content was greater when grown on mixed CDL when compared to their preferred carbon source by GC-FID quantification. Our study provided progress towards a cost-competitive, sustainable, and industrially relevant use for lignin.

Winkler, Gordon L. W. [Univ. of Wisconsin, Madison↗

DOE OSTI2025

Frontiers of Ionic Liquids in Carbon Dioxide Separation and Valorization

Ionic liquids (ILs) have emerged as highly tunable sorbents and membranes for gas separation, especially in the purification of CO 2 -containing gas streams such as air, natural gas, biogas, and syngas. Their negligible volatility, high thermal stability, and chemical versatility position them as promising alternatives to conventional amine and alkaline metal derivative-based systems, effectively addressing key challenges such as volatility, stability, and high regeneration energy. Here, this Review explores IL-derived systems for CO 2 -related gas separation across dense, porous, and supported categories. At the dense liquid level, we discuss strategies for tailoring IL properties to optimize CO 2 sorption, focusing on the correlation between IL-CO 2 interaction strength, uptake capacity, and regeneration energy. Key advancements in carbon capture, including amino-functionalized (AILs) and superbase-derived ILs (SILs), are highlighted, along with strategies such as chemical structure engineering, multiple binding site integration, alternative driving force exploration, and stability enhancement. Then, the porous liquids (PLs) scale focuses on the emerging field integrating IL properties with permanent porosity engineering, spanning ultramicropores (<5 Å) to macropores (around 100 nm). These innovations improve gas uptake capacity, accelerate transport kinetics, introduce the gating effect, and enable the coexistence of active sites with antagonistic properties within a single IL medium. At the supported IL scale, the discussion shifts to IL- and ionic pair-modified sorbents and membranes, emphasizing the modulation of cations and anions, confinement effects from porous supports, and the IL–interface interaction to enhance CO 2 separation performance, particularly in diluted gas streams. Beyond separation, this Review highlights IL-based integrated processes for CO 2 capture and conversion into value-added chemicals via thermocatalytic, electrocatalytic, and photocatalytic pathways. At each scale, advanced computational and experimental tools for IL design are also discussed, providing insights into stability enhancement, sorption efficiency, and process integration. The Review concludes by addressing existing challenges and outlining future directions for IL-driven innovations in gas separation technologies.

Qiu, Liqi [Univ. of Tennessee, Knoxville, TN (Unit↗

DOE OSTI2024

Iodine Capture Studies of Silver Mordenite and Novel Alternative Metal Sorbents

Radioiodine is one of the radionuclides of concern when considering reprocessing of used nuclear fuel (UNF). It is expected to be released primarily in the dissolver off-gas (DOG) stream. Sorbents that target I 2 removal from a UNF processing off gas must perform efficiently under elevated temperatures in the presence of water vapor and nitrogen oxide gasses (NOx). Many studies have been conducted that examine the adsorption of iodine species in the presence of NOx gases through reduced silver-exchanged mordenite (Ag 0 Z), a zeolite mineral. This study characterizes the effects of iodine, water vapor, and nitrogen dioxide on the adsorption of I 2 by Ag 0 Z at lower temperatures suitable for comparison with novel Cu- and Bi- bearing sorbents which offer a non-RCRA alternative. When exposed to ‘harsh’ conditions such as I 2 , H 2 O, and NO 2 vapors carried by air, Cu 2 S-polyacrylonitrile (PAN) is more efficient than Ag 0 Z and all other novel sorbents, providing with 11% I capture efficiency. However, under ‘ideal’ conditions in which I 2 is carried by dry air), Ag 0 Z is more efficient than Cu 2 S-PAN and other novel sorbents, providing 74% I capture efficiency. To investigate this result and the overall performance of all sorbents tested, several characterization techniques were employed, including thermal gravimetric analysis, scanning electron microscopy – electron dispersion X-Ray spectroscopy, powder X-ray diffraction, and X-ray photoelectron spectroscopy. These techniques are discussed within this report. This document also includes a design and test plan update from Idaho National Laboratory as the next step in testing the high performing sorbents from thin-bed tests carried out by Oak Ridge National Laboratory.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Reduction of Porosity in Cementitious Waste Forms to Reduce Effective Diffusivity

Although current practices meet regulations for grout waste forms, development of sequestration materials and techniques for contaminants of concern could expand the use of grout waste forms and reduce risk to established processes from future compositional changes. There are contaminants in waste streams that are not permanently and completely sequestered in current grout waste forms under certain conditions. These contaminants include methyl mercury, 129 I, 99 Tc, and nitrate. Each of these species have minor fractions that are not permanently and completely isolated from the environment and therefore potentially contribute to long-term environmental impacts. An approach of interest includes the technique of reducing porosity to result in reduced leachability of all contaminants simultaneously. The current Savannah River Site (SRS) saltstone formulation was used to evaluate the relationship between leachability and porosity reduced waste forms by altering the water-to-binder ratio. Results indicated decreasing the water-to-binder directly reduced porosity and resulted in the waste form’s ability to more effectively immobilize nitrate. This work was funded by the Department of Energy Office of Environmental Management (DOE-EM) Technology Operations Office (TOO) Project # HQ221818.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Functional Verification for Endcap Concentrator ASICs in the High-Granularity Calorimeter Upgrade of CMS

The High-Granularity Calorimeter (HGCAL) of CMS will undergo a major upgrade during Long-Shutdown 3. The Endcap Concentrator (ECON) ASICs represent key elements in the readout chain, processing trigger (ECON-T) and data (ECON-D) streams from the HGCROC to the lpGBT. The ECONs will operate in a radiation environment with a High-Energy Hadron (HEH) flux of $3\cdot10^{6} cm^{-2}s^{-1}$. This contribution describes the Universal Verification Methodology (UVM)-based functional verification of the ECON ASICs focusing on the re-use of existing components to manage the complexity of the verification environment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture↗

DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗

DOE OSTI2025

A co-registered in-situ and ex-situ dataset from wire arc additive manufacturing process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data-centric approach emphasizes leveraging sensor data available throughout the production process to optimize performance. Integration of extensive data analysis provides opportunities for improving precision, reducing waste, and enhancing the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes a comprehensive description of the deposition process, process parameters, welding characteristics and acoustic data collected in-situ, and X-Ray Computed Tomography data of the build.

42 ENGINEERING↗

DOE OSTI2024

A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build. Dataset A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process has arisen under UT-Battelle, LLC’s Prime Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE) to manage and operate the Oak Ridge National Laboratory. UT-Battelle, LLC will not assert any rights under United States law or under the Prime Contract it has in the dataset against any user of the dataset, including any copyrights or patent rights. UT-Battelle, LLC requests that attribution to the dataset is provided as academically appropriate.

42 ENGINEERING↗

DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗

DOE OSTI2025

Integrated CO 2 Capture and Conversion to Formate with a Molecular Platinum Bis(diphosphine) Electrocatalyst

Carbon dioxide is a potentially valuable feedstock for carbon-based fuels or commodities but is only available in dilute streams. Many studies have focused on either the capture and concentration of CO 2 or the reduction of pure CO 2 streams. The direct reduction of sorbent-captured CO 2 in an integrated process would skip the energy-intensive CO 2 concentration and sorbent regeneration step. Herein, we report the electrocatalytic reduction of 1,3-bis(2,6-diisopropylphenyl)imidazolium-2-carboxylate (IPr·CO 2 ), which forms quantitatively from the reaction of sorbent 1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene (IPr) with 10% and 0.04% CO 2 streams, by catalyst [Pt(dmpe) 2 ](PF 6 ) 2 (dmpe = 1,2-bis(dimethylphosphino)ethane) to formate with >70% Faradaic efficiencies. Unexpectedly, experimental studies indicate that the proton source phenol facilitates rapid decarboxylation of IPr·CO 2 to release CO 2 , which is the substrate for reduction. Kinetic studies determined the rate of hydride transfer from a catalytic intermediate [HPt(dmpe) 2 ](PF 6 ) to form the C–H bond in formate to be 0.22 M –1 s –1 . Further details on the mechanism, transition state energy, and structure for hydride transfer to CO 2 , a common step in CO 2 reduction, were explored using computational methods.

Chemistry↗