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DOE OSTI2020

Overview of the SPARC tokamak

The SPARC tokamak is a critical next step towards commercial fusion energy. SPARC is designed as a high-field ( $B_0 = 12.2$ T), compact ( $R_0 = 1.85$ m, $a = 0.57$ m), superconducting, D-T tokamak with the goal of producing fusion gain $Q>2$ from a magnetically confined fusion plasma for the first time. Currently under design, SPARC will continue the high-field path of the Alcator series of tokamaks, utilizing new magnets based on rare earth barium copper oxide high-temperature superconductors to achieve high performance in a compact device. The goal of $Q>2$ is achievable with conservative physics assumptions ( $H_{98,y2} = 0.7$ ) and, with the nominal assumption of $H_{98,y2} = 1$ , SPARC is projected to attain $Q \approx 11$ and $P_{\textrm {fusion}} \approx 140$ MW. SPARC will therefore constitute a unique platform for burning plasma physics research with high density ( $\langle n_{e} \rangle \approx 3 \times 10^{20}\ \textrm {m}^{-3}$ ), high temperature ( $\langle T_e \rangle \approx 7$ keV) and high power density ( $P_{\textrm {fusion}}/V_{\textrm {plasma}} \approx 7\ \textrm {MW}\,\textrm {m}^{-3}$ ) relevant to fusion power plants. SPARC's place in the path to commercial fusion energy, its parameters and the current status of SPARC design work are presented. This work also describes the basis for global performance projections and summarizes some of the physics analysis that is presented in greater detail in the companion articles of this collection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Thermodynamic, Kinetic, and Transport Contributions to Hydrogen Evolution Activity and Electrolyte-Stability Windows for Water-in-Salt Electrolytes

Concentrated water-in-salt electrolytes (WiSEs) are used in aqueous batteries and to control electrochemical reactions for fuel production. The hydrogen evolution reaction is a parasitic reaction at the negative electrode that limits cell voltage in WiSE batteries and leads to self-discharge, and affects selectivity for electrosynthesis. Mitigating and modulating these processes is hampered by a limited fundamental understanding of HER kinetics in WiSEs. Here, in this study, we quantitatively assess how thermodynamics, kinetics, and interface layers control the apparent HER activities in 20 m LiTFSI. When the LiTFSI concentration is increased from 1 to 20 m, an increase in proton activity causes a positive shift in the HER equilibrium potential of 71 mV. The exchange current density, i o , derived from the HER branch for 20 m LiTFSI in 98% purity (0.56 ± 0.05 μA/cm Pt 2 ), however, is 8 times lower than for 20 m LiTFSI in 99.95% (4.7 ± 0.2 μA/cm Pt 2 ) and 32 times lower than for 1 m LiTFSI in 98% purity (18 ± 1 μA/cm Pt 2 ), demonstrating that the WiSE's impurities and concentration are both central in significantly suppressing HER kinetics. The ability and applicability of the reported methods are extended by examining additional WiSEs formulations made of acetates and nitrates.

25 ENERGY STORAGE↗

DOE OSTI2020

A Hybrid Pathway to Biojet Fuel via 2,3-Butanediol

Production of biomass-derived sustainable alternative jet fuels (SAJF) has been considered as an important approach to decarbonize the aviation industry but still possesses various challenges in technology advancement, particularly in achieving high carbon efficiency. Here we report a hybrid pathway to SAJF from 2,3-butanediol (2,3-BDO), integrating biologically converting biomass to 2,3-BDO with catalytically upgrading of 2,3-BDO to jet-range hydrocarbons. This pathway is demonstrated to have a high carbon recovery to liquid hydrocarbons from corn stover (25–28%) (74–82% of the theoretical maximum efficiency). The catalytic conversion steps involve 2,3-BDO to C3+ olefins, oligomerization, and hydrogenation where the first two steps are the focus of this study. Under optimum reaction conditions (523 K, 115 kPa, 1.0 h-1 weight hourly space velocity), 2,3-BDO conversion and C3+ olefin selectivity are >97% and 94–98% during 40 h time on stream, respectively. To demonstrate the adaptability of this technology with bio-derived 2,3-BDO, we also investigated the impact of water and other organic coproducts (acetoin and acetic acid) inherited from the fermentation broth on the catalyst performance and product selectivities. We have shown that the catalyst can handle a significant amount of water in the liquid feed (40 wt% water/60 wt% 2,3-BDO) and maintain catalyst stability for ~40 h. Acetoin can be converted to similar C3+ olefins as 2,3-BDO with complete conversion of acetoin. Co-feeding 10 wt% acetoin with 2,3-BDO is found to have no impact on 2,3-BDO conversion, C3+ olefin selectivity, and catalyst stability. The utilization of organic coproduct like acetoin can help to improve overall carbon conversion efficiency when using real biomass-derived 2,3-BDO. On the other hand, the presence of 10 wt% acetic acid is shown to drastically inhibit methyl ethyl ketone (MEK) hydrogenation and butene oligomerization as revealed by the increased MEK and butene selectivities, implying the importance of separating organic acids when feeding bio-derived 2,3-BDO. The formed C3–C6 olefins from 2,3-BDO are further oligomerized over Amberlyst-36 catalyst to longer-chain hydrocarbons with >70 wt% jet-range hydrocarbons including predominantly iso-olefins/iso-paraffins. The overall carbon efficiency for the jet-range hydrocarbons is 19–22%, exceeding most of the reported biojet pathways, which makes it a promising approach for SAJF production.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Massively parallel, computationally guided design of a proenzyme

Proteins have shown promise as therapeutics and diagnostics, but their effectiveness is limited by our inability to spatially target their activity. To overcome this limitation, we developed a computationally guided method to design inactive proenzymes or zymogens, which are activated through cleavage by a protease. Since proteases are differentially expressed in various tissues and disease states, including cancer, these proenzymes could be targeted to the desired microenvironment. We tested our method on the therapeutically relevant protein carboxypeptidase G2 (CPG2). We designed Pro-CPG2s that are inhibited by 80 to 98% and are partially to fully reactivatable following protease treatment. The developed methodology, with further refinements, could pave the way for routinely designing protease-activated protein-based therapeutics and diagnostics that act in a spatially controlled manner. Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to redesign any chosen enzyme to be similarly stimulus responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for proenzyme design. For a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effectively designed variants shows that they are inhibited by ∼80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98% but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing proenzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the prodomain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of proproteins with precise spatial regulation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

The Atacama Cosmology Telescope: delensed power spectra and parameters

We present ΛCDM cosmological parameter constraints obtained from delensed microwave background power spectra. Lensing maps from a subset of DR4 data from the Atacama Cosmology Telescope (ACT) are used to undo the lensing effect in ACT spectra observed at 150 and 98 GHz. At 150 GHz, we remove the lensing distortion with an effective efficiency of 30% (TT), 30% (EE), 26% (TE) and 20% (BB); this results in detections of the delensing effect at 8.7σ (TT), 5.1σ (EE), 2.6σ (TE), and 2.4σ (BB) significance. The combination of 150 and 98 GHz TT, EE, and TE delensed spectra is well fit by a standard ΛCDM model. We also measure the shift in best-fit parameters when fitting delensed versus lensed spectra; while this shift does not inform our ability to measure cosmological parameters, it does provide a three-way consistency check among the lensing inferred from the best-fit parameters, the lensing in the CMB power spectrum, and the reconstructed lensing map. This shift is predicted to be zero when fitting with the correct model since both lensed and delensed spectra originate from the same region of sky. Furthermore, fitting with a ΛCDM model and marginalizing over foregrounds, we find that the shift in cosmological parameters is consistent with zero. Our results show that gravitational lensing of the microwave background is internally consistent within the framework of the standard cosmological model.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Turbulence driven widening of the near-SOL power width in ASDEX Upgrade H-Mode discharges

Divertor heat flux data from infra red (IR) from various tokamaks in H-Mode regime show that the power width, $\lambda_{q}$, scales approximately like 1.6 $(R/a) ρ_{s,pol}$. However, the IR based scaling comes with the restriction that only discharges were considered with attached divertor conditions. Here we set up a new data base covering low edge density data but also including high density plasmas reaching the H-mode density limit. We use Thomson-scattering to measure the electron temperature decay length which will set the near-SOL power width through parallel heat conduction. We present scaling results for the density, temperature and pressure separatrix fall-off lengths. As the principal result we derive a generalized power width scaling which reads as $\lambda_{q}\propto\,\rho_{s,pol}\cdot(1+2.1\alpha_{t}^{1.7})$ where $\alpha_{t}$ describes a normalized collisionality ($\alpha_{t} = 3\cdot\,10^{-18}\,R\,q^2\,n\,Z_{eff}T^{-2}$). The parameter $\alpha_{t}$ describes the relative importance of the interchange effect on drift-wave turbulence as proposed by Scott and is found to be closely linked to the diamagnetic parameter αd introduced by Rogers, Drake and Zeiler (RDZ). The edge operational space for ASDEX Upgrade displays a boundary qualitatively in agreement with the region labelled as density limit in the edge plasma phase space proposed by RDZ. The data base shows in the limit of low edge densities ($\alpha_{t}\simeq\,0.2$) accurate agreement to the IR based power width scaling. At elevated separatrix densities ($\alpha_{t}\simeq\,1$) the power width is broadened by a factor of up to about three accompanied by a reduction of the $\text{H}_{98,y2}$ confinement factor. Finally, plasmas with higher triangularity show higher values for $\text{H}_{98,y2}$ in the same range of normalized collisionality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Achievements of actively controlled divertor detachment compatible with sustained high confinement core in DIII-D and EAST

The compatibility of efficient divertor detachment with high-performance core plasma is vital to the development of magnetically controlled fusion energy. The joint research on the EAST and DIII-D tokamaks demonstrates successful integration of divertor detachment with excellent core plasma confinement quality, a milestone towards solving the critical plasma–wall-interaction (PWI) issue and core-edge integration for ITER and future reactors. In EAST, actively controlled partial detachment with T et,div ~ 5 eV around the strike point and H 98 > 1 in different H-mode scenarios including the high β P H-mode scenario have been achieved with ITER-like tungsten divertor, by optimizing the detachment access condition and performing detailed experiments for core-edge integration. For active long-pulse detachment feedback control, a 30 s H-mode operation with detachment-control duration being 25 s has been successfully achieved in EAST. DIII-D has achieved actively controlled fully detached divertor with low plasma electron temperature (T et,div ≤ 5 eV across the entire divertor target) and low particle flux (degree of detachment, DoD > 3), simultaneously with very high core performance (β N ~3, β P > 2 and H 98 ~ 1.5) in the high βP scenario being developed for ITER and future reactors. The high-β P high confinement scenario is characterized by an internal transport barrier (ITB) at large radius and a weak edge transport barrier (ETB, or pedestal), which are synergistically self-organized. Both the high-β P scenario and impurity seeding facilitate divertor detachment. The detachment access leads to the reduction of ETB, which facilitates the development of an even stronger ITB at large radius in the high β P scenario. Thus, this strong large radius ITB enables the core confinement improvement during detachment. In conclusion, these significant joint DIII-D and EAST advances on the compatibility of high confinement core and detached divertor show a great potential for achieving a high-performance core plasma suitable for long-pulse operation of fusion reactors with controllable steady-state PWIs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

The Atacama cosmology telescope: flux upper limits from a targeted search for extragalactic transients

ABSTRACT We have performed targeted searches of known extragalactic transient events at millimetre wavelengths using nine seasons (2013–2021) of 98, 150, and 229 GHz Atacama Cosmology Telescope (ACT) observations that mapped ∼40 per cent of the sky for most of the data volume. Our data cover 88 gamma-ray bursts (GRBs), 12 tidal disruption events (TDEs), and 203 other transients, including supernovae (SNe). We stack our ACT observations to increase the signal-to-noise ratio of the maps. In all cases but one, we do not detect these transients in the ACT data. The single candidate detection (event AT2019ppm), seen at ∼5σ significance in our data, appears to be due to active galactic nuclei activity in the host galaxy coincident with a transient alert. For each source in our search we provide flux upper limits. For example, the medians for the 95 per cent confidence upper limits at 98 GHz are 15, 18, and 16 mJy for GRBs, SNe, and TDEs, respectively, in the first month after discovery. The projected sensitivity of future wide-area cosmic microwave background surveys should be sufficient to detect many of these events using the methods described in this paper.

Hervías-Caimapo, Carlos↗

DOE OSTI2024

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗

DOE OSTI2022

Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Marfo, William↗

DOE OSTI2023

Residual stresses in Cu matrix composite surface deposits after laser melt injection

Abstract Tungsten carbide particles reinforced metal matrix composite (MMC) coatings can significantly improve surface wear resistance owing to their increased surface hardness. However, the presence of macro‐ and micro‐residual stresses in MMC coatings can have detrimental effects, such as reducing service life. In this study, neutron diffraction was used to determine the residual stresses in spherical fused tungsten carbide (sFTC) reinforced Cu matrix composite surface deposits after laser melt injection. We also developed a thermo‐mechanical coupled finite element model to predict residual stresses. Our findings reveal that sFTC/Cu composite deposits produced with a preheating temperature of 400°C have low residual stresses, with a maximum tensile residual stress of 98 MPa in the Cu matrix on the top surface. In contrast, the sFTC/bronze (CuAl10Ni5Fe4) composite deposit exhibits very high residual stresses, with a maximum tensile residual stress in the Cu matrix on the top surface reaching 651 MPa. These results provide a better understanding of the magnitudes and distributions of residual stresses in sFTC‐reinforced Cu matrix composite surface deposits manufactured via laser melt injection.

Zhang, Xingxing↗

DOE OSTI2025

Accelerating GNNs on GPU Sparse Tensor Cores through N:M Sparsity-Oriented Graph Reordering

Recent advancements in GPU hardware support have introduced the capability to leverage N:M sparse patterns for substantial performance gains. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is often irregular, not conforming to such sparse patterns. In this paper, we propose a novel graph reordering algorithm, the first of its kind, to reshape irregular graph data into the N:M structured sparse pattern at the tile level, allowing linear-algebra-based graph operations in GNNs to benefit from the N:M sparse hardware. The optimization is lossless, maintaining the accuracy of GNN. It can remove 98-100\% violations of the N:M sparse patterns at the vector level, and increase the proportion of conforming graphs in SuiteSparse collection from 5-9\% to 88.7-93.5\%. On A100 GPUs, the optimization accelerates Sparse Matrix Matrix (SpMM) by up to 43X (2.3X -- 7.5X on average) and speeds up the key graph operations in GNNs on real graphs by as much as 8.6X (3.5X on average).

artificial intelligence, graph neural networks↗

DOE OSTI2021

Demonstration of Electrochemically-Driven CO 2 Separation Using Hydroxide Exchange Membranes

Hydroxide exchange membrane fuel cells (HEMFCs) are a potentially lower-cost hydrogen fuel cell technology; however, ambient levels of CO 2 in air significantly reduce HEMFCs’ performance. In this work, we demonstrate an electrochemically-driven CO 2 separator (EDCS) which can be used to remove ambient levels of CO 2 from air upstream of the HEMFC stack in fuel cell vehicles, protecting it from CO 2 -related performance losses. The EDCS operating window was explored for current density, anode flow, and cathode flow with respect to its impact on CO 2 separation performance. Additionally, gas-phase mass transport was improved by selecting flow fields and gas diffusion layers conducive to the EDCS operating regime. The use of a carbon-ionomer interlayer at the cathode was explored and improved CO 2 removal performance from 77.7% to 98.2% at 20 mA cm −2 . An analytical, 1-D model is used to explain the experimental observations and design improvements. A single-cell, 25 cm 2 EDCS using the aforementioned improved design demonstrated greater than 98% CO 2 removal at a cathode flow rate of 1300 sccm for 100 h with 2.7% hydrogen stack consumption.

Matz, Stephanie↗

DOE OSTI2023

Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87 Sr 0.13 CrO 3 , Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH 4 , two types of natural gas simulants, and CH 4 +NH 3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.

03 NATURAL GAS↗

DOE OSTI2025

Tunturi virus isolates and metagenome-assembled viral genomes provide insights into the virome of Acidobacteriota in Arctic tundra soils

Arctic soils are climate-critical areas, where microorganisms play crucial roles in nutrient cycling processes. Acidobacteriota are phylogenetically and physiologically diverse bacteria that are abundant and active in Arctic tundra soils. Still, surprisingly little is known about acidobacterial viruses in general and those residing in the Arctic in particular. Here, we applied both culture-dependent and -independent methods to study the virome of Acidobacteriota in Arctic soils. Five virus isolates, Tunturi 1–5, were obtained from Arctic tundra soils, Kilpisjärvi, Finland (69°N), using Tunturiibacter spp. strains originating from the same area as hosts. The new virus isolates have tailed particles with podo- (Tunturi 1, 2, 3), sipho- (Tunturi 4), or myovirus-like (Tunturi 5) morphologies. The dsDNA genomes of the viral isolates are 63–98 kbp long, except Tunturi 5, which is a jumbo phage with a 309-kbp genome. Tunturi 1 and Tunturi 2 share 88% overall nucleotide identity, while the other three are not related to one another. For over half of the open reading frames in Tunturi genomes, no functions could be predicted. To further assess the Acidobacteriota-associated viral diversity in Kilpisjärvi soils, bulk metagenomes from the same soils were explored and a total of 1881 viral operational taxonomic units (vOTUs) were bioinformatically predicted. Almost all vOTUs (98%) were assigned to the class Caudoviricetes. For 125 vOTUs, including five (near-)complete ones, Acidobacteriota hosts were predicted. Acidobacteriota-linked vOTUs were abundant across sites, especially in fens. Terriglobia-associated proviruses were observed in Kilpisjärvi soils, being related to proviruses from distant soils and other biomes. Approximately genus- or higher-level similarities were found between the Tunturi viruses, Kilpisjärvi vOTUs, and other soil vOTUs, suggesting some shared groups of Acidobacteriota viruses across soils. This study provides acidobacterial virus isolates as laboratory models for future research and adds insights into the diversity of viral communities associated with Acidobacteriota in tundra soils. Predicted virus-host links and viral gene functions suggest various interactions between viruses and their host microorganisms. Largely unknown sequences in the isolates and metagenome-assembled viral genomes highlight a need for more extensive sampling of Arctic soils to better understand viral functions and contributions to ecosystem-wide cycling processes in the Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Data for "Adsorptive Separation and Recovery of Triacetic Acid Lactone from Fermentation Broth"

Triacetic acid lactone (TAL) can be microbially produced and further chemically upgraded to several high-value chemicals. In this work, several acidic and basic ion-exchange resins and activated charcoal were evaluated for their ability to adsorb microbially produced TAL. Activated charcoal and a weak base resin, Dowex 66, showed similar TAL adsorption capacity of 0.18 ± 0.002 g/g. At 15% w/v activated charcoal, about 98% of TAL present in fermentation broth could be adsorbed. Further, ethanol washing allowed recovery of 72% of adsorbed TAL. A biorefinery producing TAL from sucrose was designed, simulated, and evaluated (through technoeconomic analysis) under uncertainty, for an estimated TAL minimum product selling price (MPSP) of $4.27/kg [$3.71−4.94/kg; 5th-95th percentiles] for the current state of technology and $2.83/kg [$2.46–3.29/kg] following potential near-term improvements to fermentation. Thus, this work provides an adsorptive process to recover microbially produced TAL that can be chemically upgraded to several industrial products.

Bioproducts↗

DOE OSTI2026

Data for 3-Hydroxypropionic Acid Recovery from Fermentation Broth through Novel Downstream Processing: Technoeconomic Analysis

This study develops and validates a simplified, fully solvent-free downstream processing (DSP) strategy for high-purity recovery of 3-hydroxypropionic acid (3-HP) from real fermentation broth containing 62.3 g/L of 3-HP. Optimized activated carbon treatment achieved 98% color removal, while Amberlite IRA-67 was operated at pH 4.5 and 30 °C to minimize product loss. This is the first integrated demonstration of a fully solvent-free DSP enabling recovery of bio-based 3-HP as both a solid sodium salt and a concentrated aqueous solution, supported by techno-economic analysis. At lab scale, the process achieved 77.3% recovery of sodium 3-HP with 83.2% (w/w) purity and produced a 30% (w/v) aqueous solution. Techno-economic analysis yielded minimum selling prices of $0.551/kg for the solution and $0.892/kg for the salt, both below target thresholds for cost-competitive bio-acrylic acid production. Overall, these results demonstrate an efficient, scalable, and economically viable industrial pathway for 3-HP recovery.

Bioproducts↗

DOE OSTI2026

Leafweb: Leaf Gas Exchange and Pulse-Amplitude Modulated Fluorometry for C4 Species, June 2026 Release

This dataset contains leaf gas exchange and Pulse-Amplitude Modulated (PAM) fluorometry for 98 C4 species. The C4 photosynthetic pathway employs specialized CO2 concentration mechanisms and Kranz anatomy to enrich CO2 concentration around Rubisco, the enzyme that catalyzes carbon fixation in the Calvin-Benson cycle to suppress photorespiration and increase the use efficiencies of light, nitrogen, and water as compared to the C3 photosynthetic pathways. Large-scale C4 photosynthetic datasets are relatively scarce, which has affected C4 photosynthesis research. To improve C4 photosynthetic data availability, Leafweb organized an effort to systematically collect, compile, standardize, and organize measurements of leaf gas exchange and/or Pulse-Amplitude Modulated (PAM) fluorometry of C4 species. This derived a C4 photosynthetic dataset containing measurements made by independent researchers in multiple countries in various environments (field, garden, or greenhouse). It covers three biochemical subtypes – the nicotinamide adenine dinucleotide phosphate-malic enzyme (NADP-ME), nicotinamide adenine dinucleotide-malic enzyme (NAD-ME), and phosphoenolpyruvate carboxykinase (PEP-CK) subtypes. This dataset is useful for using Artificial Intelligence / Machine Learning and mechanistic models to study C4 photosynthesis and compare across different biochemical subtypes. This dataset contains 3 compressed (*.zip) folders containing 1,892 data files in comma-separate values (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma-separate values (*.csv) format and a user guide in PDF (*.pdf) format.

Zhou, Haoran [Tianjin University, China]↗