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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Production, fuel properties and combustion testing of an iso-olefins blendstock for modern vehicles

With the increasing pressure to decarbonize the transportation sector, exploring strategies that can reduce emissions from light-duty vehicles (LDV) has become critical. Bioblendstocks that allow for higher engine efficiency and fuel economy could complement vehicle electrification and help reach carbon neutrality by 2050. In this context, the potential of a mixture of iso-olefins as a bioblendstock was investigated for multimode boosted spark-ignition (SI)/advanced compression ignition (ACI) engine operation designed to achieve higher overall vehicle fuel economy. By establishing the relationship between the molecular structure of iso-olefins and research octane number (RON), octane sensitivity (S) (i.e., the difference between RON and motor octane number [MON]), and phi-sensitivity a dimethyl-hexenes rich olefins mixture (DMHROM) was identified as a preferred blendstock for SI/ACI combustion engines. Here, a pathway for DMHROM production from biomass-derived ethanol was developed and scaled up. More than 1 gallon of DMHROM blendstock was produced for fuel properties assessment including engine testing. Measurements in a Cooperative Fuel Research Engine showed that the DMHROM blendstock possesses a RON of 94 and S of 13.5, and blends synergistically. Rapid compression machine tests coupled with single-cylinder gasoline direct injection engine measurements demonstrated the 20 vol.% DMHROM blend has higher phi-sensitivity than an olefin-free gasoline base fuel and a typical California Reformulated Gasoline Blendstock for Oxygenate Blending (CARBOB) gasoline fuel. These results demonstrate the potential of DMHROM for improving gasoline fuel performance and quality for operation under ACI conditions. The effectiveness of the aftertreatment system in mitigating emissions was verified and showed that the pure DMHROM blendstock and 20 vol.% blend would not increase non-methane organic gases, NO x , and carbon monoxide (CO) emissions. The DMHROM blendstock was found to slightly decrease sooting tendency when added to a gasoline-base fuel (i.e., ~6% reduction at 20 vol.% blending level). Oxidation stability and lubricant compatibility were both confirmed for the 20 vol.% blend. Overall, these results demonstrate that dimethyl-hexenes have potential for improving engine efficiency and fuel economy while meeting emissions regulations and ASTM specifications for gasoline fuel.

09 BIOMASS FUELS↗
DOE OSTI2021

The intrinsic value of bioblendstocks: Prenol as a case study

Decarbonizing the transportation sector is likely to require both electrification and increased incorporation of biofuels and/or bioblendstocks. While the social and environmental benefits of bioblendstocks are well understood, their real value for the fuel producers has not been established. As such, this work considers prenol as a bioblendstock case study to identify sources of intrinsic value to fuel blenders by studying the properties of binary mixtures with gasoline components. The considered refinery blendstocks were samples of full range naphthas from the distillation, fluidized catalytic cracking, isomerization, alkylation, and reforming units. Octane numbers, Reid vapor pressure, distillation curves, and sulfur content were evaluated. Our results indicate the need for adjusting the formulation of the base fuel, depending on the interplay among the properties of the bioblendstock and those of the base fuel. Prenol increased research octane number (RON) and octane sensitivity (OS) of the base fuel, by up to 25 and 10 octane numbers, respectively. Additionally, 10 vol% prenol reduced RVP up to 2.2 psi, for the more volatile blendstock. Thus, considering prenol as a low volatility, RON/OS boosting bioblendstock, the composition of the preferred base fuel was proposed as containing reduced olefins and aromatics, and increase light fractions. The potential impact of this new gasoline formulation on refining processes and products gives rise to direct sources of value to the refiners, such as exporting products to the chemicals market, increasing the value of intermediate refinery streams, decreasing operating severity of certain refinery units, and broadening of the product suite.

09 BIOMASS FUELS↗
DOE OSTI2022

Multi-stage heat release of multi-component fuels: Insights and implications for advanced engine operation

Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.

42 ENGINEERING↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2024

Microwave-Assisted dry reforming of toluene as a model tar compound using low-cost iron catalyst for syngas clean-up

Gasification of waste feedstock such as biomass, waste plastics suffers from high tar yields during hydrogen-rich syngas production. The presence of tars result in lower quality syngas, lower syngas yields, reactor blockage, reactor down time, and costly maintenance. Therefore, removal of tar from syngas during gasification is essential. Catalytic reforming of tars via in-situ syngas cleanup is an effective way of mitigating tars. This work explores the possibility of microwave-assisted catalytic dry reforming of tars for syngas production. Further, due to its chemical complexity, toluene, which is one of the main tar constituents, could be used as a model tar compound. Toluene dry reforming was studied using the Fe/Al 2 O 3 catalyst under CO 2 under the temperature range of 400–700 °C. The toluene reforming reaction was conducted using microwave and conventional thermal reactors. Under microwave irradiation, CO 2 and toluene conversions are boosted to 80% at 500 °C. Hydrogen and carbon monoxide yields were approximately five times and ten times higher in the microwave reactor at 500 °C, respectively, compared to the productions obtained in the conventional fixed-bed reactor at 700 °C. Filamentous carbon was also produced as a valuable side product to improve the economy of this process and such value-added carbon was only observed in the microwave reactor. Three reaction pathways were observed during microwave reaction: the toluene decomposition produces an initial hydrogen and carbon deposit on the catalyst; the formation of methane and benzene suggests toluene hydrodemethylation as a secondary reaction; and toluene hydrogenolysis forms light alkanes such as methane, and through reforming reaction under CO 2 to syngas.

10 SYNTHETIC FUELS↗
DOE OSTI2025

"Hidden" hydrothermal technical potential & technoeconomics: Revealing permeability & fluids with more data

Historical hydrothermal estimates have largely relied on temperature or heat flow estimates ignoring the need for natural flowing fluids. More accurate hydrothermal estimates require some indication of permeability and fluids that naturally exist in the subsurface. This paper describes a novel approach that includes proxies of permeability and fluids in hydrothermal estimates by leveraging the relatively data-rich Great Basin. Specifically, nameplate capacities (megawatts) of operating geothermal plants, negative (0 megawatt) locations and 48 geophysical and geologic features are used to used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictions. Additionally, this work inputs the XGBoost-based hydrothermal predictions into the Renewable Energy Potential (reV) model to quantify technical capacity, its uncertainty and techno-economics. Compared to historical hydrothermal estimates, these predictions adhere to the 37 operating geothermal plants and negative locations. We present a method for subsampling the negative sites to bring the labels into balance that uses the geologic domain knowledge to proportionally represent negatives. Overall, the distributions of the hydrothermal technical capacity and the site levelized cost of energy are respectively much tighter, lower and more accurate than the previous estimates for the Great Basin, as they include geological and geophysical surrogates for permeability and fluids. Percentile (50th and 90th, median and high estimate, respectively) models provide bookends for these metrics.

13 HYDRO ENERGY↗
DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2025

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗
DOE OSTI2026

Facile synthesis of Co(OH)2 nanoneedle arrays grown on stainless steel for industrial electrochemical oxygen evolution reaction

The electrochemical oxygen evolution reaction (OER) is a critical half-reaction in a variety of energy conversion and storage applications, however, OER suffers from sluggish kinetics due to the four proton-electron transfer processes. To remedy this, interfacial engineering proves an effective strategy to design high active OER catalysts. Herein, we report a facile synthesis of Co(OH)2 nanoneedle (Co-NN) arrays grown on stainless steel (SS) via a one-step hydrothermal reaction, and the resultant impressive OER performance. Particularly, the Co-NN/SS needs 267, 307, and 576 mV overpotentials to reach 10, 100, and 1000 mA cm−2 in 1 M KOH & room temperature, and the overpotentials drop to 199, 257, and 283 mV to achieve the same current densities in 30 wt% KOH & 80 °C. Additionally, an alkaline water electrolysis (AWE) cell coupled with Co-NN/SS anode and PtRu/NiMo cathode only requires 1.65 and 1.73 V iR-free cell voltage to reach 1.0 and 2.0 A cm−2, respectively. The sterling OER performance could be attributed to four possible reasons, the Fe in SS surface tailoring the electronic properties of Co(OH)2, the mixed metal oxides on SS surface accelerating surface reconstruction of Co(OH)2 nanoneedles into CoOOH active species, a Ni-rich surface layer formation cooperatively boosting the OER activity, and a local electric field effect concentrating reactants on the tip surface and accelerating the mass transfer. This work provides a facile approach for synthesizing highly efficient OER catalysts for industrial applications by interfacial engineering.

Lyu, Xiang [ORNL] (ORCID:0000000208673248)↗
DOE OSTI2021

Five-Year Longitudinal Analysis of Patient-Reported Outcomes and Cosmesis in a Randomized Trial of Conventionally Fractionated Versus Hypofractionated Whole-Breast Irradiation

There are limited prospective data on predictors of patient-reported outcomes (PROs) after whole-breast irradiation (WBI) plus a boost. We sought to characterize longitudinal PROs and cosmesis in a randomized trial comparing conventionally fractionated (CF) versus hypofractionated (HF) WBI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Revealing the mechanism of lignin re-polymerization inhibitor in acidic pretreatment and its impact on enzymatic hydrolysis

Phenolic additives, including 2-naphthol, 2-naphthol-7-sulfonate, and resorcinol were evaluated for their impact on the lignin re-polymerization and subsequent hydrolysis of bamboo residues. Low surface lignin concentration (65.68%) of the substrate and high Tg value (185 °C) of lignin was observed for 2-naphthol-7-sulfonate assisted pretreatment, leading to the most significant increase (14%) in enzymatic hydrolysis efficiency of pretreated bamboo residues. Meanwhile, physicochemical properties of lignin revealed that the introduced sulfonic acid groups increased the surface charge, decreased the molecular weight, promoted the cleavage of β-O-4 linkages, and prevented the re-condensation of lignin. Even though resorcinol-assisted pretreatment, by contrast, was found to be an inhibitor for enzymatic hydrolysis. NMR analysis of modified lignin showed that resorcinol led to a higher amount of non-condensed phenolic OH groups and promoted the cleavage of β-O-4 linkages, acting as an inhibitor in lignin re-polymerization during the dilute acidic pretreatment. In short, the above findings present a detailed view of suppressing lignin re-polymerization for boosting the utilization of bioresources with acidic pretreatment.

09 BIOMASS FUELS↗
DOE OSTI2022

Green synthesis of cellulose formate and its efficient conversion into 5-hydroxymethylfurfural

5-hydroxymethylfurfural (HMF) production from cellulose is a hot topic, while the rigidity of cellulose limits its efficient conversion into HMF. Cellulose formate has exhibited the great potential for HMF production but it’s synthesis via direct formylation of cellulose in formic acid is difficult. We reported a green synthetic route of cellulose formate and its efficient conversion into HMF. Ball milled cellulose formate (BCF) was synthesized via ball milling followed by formylation in formic acid at 80 °C for 0.5 h. The structure of BCF was thoroughly characterized and BCF was subsequently converted to HMF catalyzed by AlCl3 and HCl in dimethyl sulfoxide (DMSO)-H 2 O media. Results showed that ball milling of cellulose for 1–1.5 h efficiently deconstructed its crystallinity and degree of polymerization, boosting its further formylation and catalytic conversion. An overall 66.9% HMF yield from cellulose was achieved at 150 °C for 20 min using BCF derived from cellulose ball milled for 1.5 h as the substrate. BCF exhibits high reactivity toward HMF due to its excellent solubility and accessibility in DMSO-H 2 O media, and the release of formic acid in reaction further facilitates HMF production. BCF synthesized with different ball milling times shows different degrees of substitution of formyl groups, which could serve as an indicator and regulate the HMF production. The green synthesis of cellulose formate creates an efficient pathway for HMF production.

5-hydroxymethylfurfural↗
DOE OSTI2022

Hydroxyapatite catalyzed hydrothermal liquefaction transforms food waste from an environmental liability to renewable fuel

Food waste is an abundant and inexpensive resource for the production of renewable fuels. Biocrude yields obtained from hydrothermal liquefaction (HTL) of food waste can be boosted using hydroxyapatite (HAP) as an inexpensive and abundant catalyst. Combining HAP with an inexpensive homogeneous base increased biocrude yield from 14 ± 1 to 37 ± 3%, resulting in the recovery of 49 ± 2% of the energy contained in the food waste feed. Detailed product analysis revealed the importance of fatty-acid oligomerization during biocrude formation, highlighting the role of acid-base catalysts in promoting condensation reactions. Economic and environmental analysis found that the new technology has the potential to reduce US greenhouse gas emissions by 2.6% while producing renewable diesel with a minimum fuel selling price of $1.06/GGE. HAP can play a role in transforming food waste from a liability to a renewable fuel.

09 BIOMASS FUELS↗
DOE OSTI2025

Fuel reid vapor pressure level and ethanol content on stochastic preignition, effects at steady and unsteady engine operation

The present work investigates relations between fuel Reid vapor pressure (RVP) and biofuel (ethanol) content on stochastic preignition (SPI) at both sustained steady-state engine operation and following load transients. This work stems from in-field observations that automotive original equipment manufacturers have observed consistent seasonal increases in United States customer drivability complaints and warranty claims during September and October where SPI is suspected to be responsible. The seasonal timing of these events coincides with the United States seasonal fuel property changeover initiating on September 15 each year, where fuel RVP increases. To explore potential linkage between fuel RVP and SPI the present study employs engine SPI experiments coupled with laboratory spray measurements of fuels with RVPs of 8, 12, and 16 psi in both E10 (10% ethanol) and E25 (25% ethanol) fuels. Engine results are partitioned into fuel RVP and ethanol content effects on SPI in steady-state, sustained high-load engine operation and unsteady-state low- to high-load transitions, where off-engine spray vessel patternation and tip penetration results help to elucidate the observed fuel effects on SPI. A boosted direct-injected, spark-ignition engine was fueled with three market relevant E10 and E25 fuels with RVPs of 8, 12, and 16 to characterize the interplay between winter fuels and abnormal combustion behavior. The steady-state work shows that for high-load, steady-state engine operation, SPI is directly linked to fuel retention, which was found to be dependent on fuel distillation. The unsteady-state engine operation work shows that following low-to high-load transitions, SPI can occur from a memory of fuel property effects at low-load operation. Specifically, the fuel RVP effect on fuel spray collapse at low loads was found to correlate with SPI with a more than 95% confidence interval following low- to high-engine-load transitions. Results suggest that fuel-wall impingement at low-load operation could carry over into high-load transitions and generate SPI events following low- to high-load transitions.

09 BIOMASS FUELS↗
DOE OSTI2021

Surface enhanced performance of La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ cathodes by infiltration Pr-Ni-Mn-O progress

Herein the present study reports the enhancement of electrochemical oxygen reduction activity of porous La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3–δ (LSCF) cathodes by coating a thin film of Pr-Ni-Mn oxide (PNM5) using a multi-step infiltration process. XRD examination reveals that PNM5 mainly contains a multiphase mixture of Pr 6 O 11 , PrNiO 3 , MnO and NiO. SEM morphology shows a thin PNM5 film and small particles are formed on the surface of LSCF backbone particles. Impedance spectrum analysis indicates that PNM5 infiltrated LSCF exhibits dramatically reduced polarization resistance (R p ), reaching R p of 0.244 Ω cm 2 at 973 K, which is one-half of the baseline LSCF cathode. The activation energy of LSCF cathodes infiltrated with PNM5 is 1.45 eV, slightly lower than the baseline LSCF cathode (1.77 eV). Distribution of relaxation time (DRT) function analysis shows PNM5 infiltration layer significantly promotes the oxygen reduction reaction (ORR) of cathode surface. With the increase in firing temperature, the total resistance increases and R p changes from ion transport to oxygen reduction reaction. Degradation rate of the PNM5-infiltrated LSCF is also lower, 0.02168% vs 0.07093% for the baseline LSCF over a 200-h period. A single cell testing indicated that the peak power density of the PNM5-infiltrated cell was increased by 140.66%. Overall, PNM5 could be a potential catalyst for boosting the performance of a commercial LSCF cathode for solid oxide fuel cells (SOFC).

36 MATERIALS SCIENCE↗
DOE OSTI2022

The pursuit of net-positive sustainability for industrial decarbonization with hybrid energy systems

We report signatories of the Paris Agreement are set to miss their climate targets. The net-zero pledges announced to date across many countries and private industries are insufficient to achieve carbon neutrality, which requires implementation of far-reaching and significantly scaled-up climate-positive actions. All technology options that pertain to deep decarbonization and carbon removal must be part of the mitigation portfolio. Transformative action plans must be established in which every individual/organization around the globe is an actor of changes in relation to net-zero goals. Such plans must involve governmental policy support but also encourage voluntary efforts to boost innovations, investments, initiatives, and behavioral changes. Recognition of these efforts is made quantifiable with the concepts of carbon handprint and net positivity. This paper presents a carbon handprint perspective on characterizing the environmental benefits of hybrid energy systems (HESs)—a widely applicable solution to cleaner production leveraging the capabilities of a portfolio of low-carbon energy sources—that provide heat and electricity to industrial processes. First, the carbon handprint and net positivity concepts and their calculation approach are introduced. The state of the art of HES-enabled industrial cogeneration is then surveyed, and the greenhouse gas emission intensities of different energy sources that power HESs are compared. Next, drawing on a case study about a US chemical facility's voluntary initiative to explore replacing its fossil fuel–based cogeneration infrastructure with a clean energy–generated HES, several technically viable scenarios are evaluated—especially those with small modular nuclear reactors—to illustrate how the positive-thinking handprint approach helps encourage and inform the search for widespread influence pathways in pursuit of net-positive sustainability. Finally, current knowledge gaps in the case study are identified, and opportunities to scale up the proposed handprint-based analysis are outlined with consideration of an expanded role of HESs in fulfilling climate objectives.It is envisioned that like-minded decision makers in the industry sector and beyond will adopt this perspective and act synergistically to enhance their environmental stewardship through voluntary actions and make greater contributions to the planet's climate future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Succinate based polymers drive immunometabolism in dendritic cells to generate cancer immunotherapy

Boosting the metabolism of immune cells while restricting cancer cell metabolism is challenging. Herein, we report that using biomaterials for the controlled delivery of succinate metabolite to phagocytic immune cells activates them and modulates their metabolism in the presence of metabolic inhibitors. In young immunocompetent mice, polymeric microparticles, with succinate incorporated in the backbone, induced strong pro-inflammatory anti-melanoma responses. Administration of poly(ethylene succinate) (PES MP)-based vaccines and glutaminase inhibitor to young immunocompetent mice with aggressive and large, established B16F10 melanoma tumors increased their survival three-fold, a result of increased cytotoxic T cells expressing RORγT (Tc17). Mechanistically, PES MPs directly modulate glutamine and glutamate metabolism, upregulate succinate receptor SUCNR1, activate antigen presenting cells through and HIF-1alpha, TNFa and TSLP-signaling pathways, and are dependent on alpha-ketoglutarate dehydrogenase for their activity, which demonstrates correlation of succinate delivery and these pathways. Altogether, our findings suggest that immunometabolism-modifying PES MP strategies provide an approach for developing robust cancer immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗