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DOE OSTI2024

Block copolymer self-assembly derived mesoporous magnetic materials with three-dimensionally (3D) co-continuous gyroid nanostructure

Magnetic nanomaterials are gaining interest for their many applications in technological areas from information science and computing to next-generation quantum energy materials. While magnetic materials have historically been nanostructured through techniques such as lithography and molecular beam epitaxy, there has recently been growing interest in using soft matter self-assembly. In this work, a triblock terpolymer, poly(isoprene-block-styrene-block-ethylene oxide) (ISO), is used as a structure directing agent for aluminosilicate sol nanoparticles and magnetic material precursors to generate organic–inorganic bulk hybrid films with co-continuous morphology. After thermal processing into mesoporous materials, results from a combination of small angle X-ray scattering (SAXS) and scanning electron microscopy (SEM) are consistent with the double gyroid morphology. Nitrogen sorption measurements reveal a type IV isotherm with H1 hysteresis, and yield a specific surface area of around 200 m 2 g −1 and an average pore size of 23 nm. The magnetization of the mesostructured material as a function of applied field shows magnetic hysteresis and coercivity at 300 K and 10 K. Comparison of magnetic measurements between the mesoporous gyroid and an unstructured bulk magnetic material, derived from the identical inorganic precursors, reveals the structured material exhibits a coercivity of 250 Oe, opposed to 148 Oe for the unstructured at 10 K, and presence of remnant magnetic moment not conventionally found in bulk hematite; both of these properties are attributed to the mesostructure. This scalable route to mesoporous magnetic materials with co-continuous morphologies from block copolymer self-assembly may provide a pathway to advanced magnetic nanomaterials with a range of potential applications.

Chemistry↗

DOE OSTI2025

Catalytic Borylation of Poly(vinyl chloride) Produces Adhesive Materials

Postpolymerization functionalization of polymers can create new applications for existing materials, while retaining their most favorable, intrinsic properties. Polyvinyl chloride (PVC) is a widely used, commodity polymer that is particularly challenging to modify. We report a copper-catalyzed protocol that replaces a small fraction of the C-Cl bonds in PVC with C-B bonds to boronic esters. The reaction occurs with an inexpensive catalyst comprising copper(II) chloride and an NHC ligand derived from a common ionic liquid that is distinct from the N-heterocyclic carbene (NHC) used for the borylation of small alkyl halides. The resulting materials adhere strongly to common surfaces, such as glass and metals, even more strongly than do commercial glues.

D’Angelo, Kyan A↗

DOE OSTI2026

Criteria for Retention of 3013 S1 Containers Based on Relative Risk and Expert Judgment

An evaluation was performed to assess the suitability of thirty-three 3013 containers proposed for retention. These containers have moisture levels greater than 0.08 wt.% – the S1 population. The remainder of the S1 population stored at SRS will be down blended and disposed of by the end of 2028. Based on field surveillance and shelf-life data available to date as well as informed technical judgment, no container is currently expected to fail in its 50-year storage period. However, corrosion risk varies across the S1 population. Relative risks were evaluated using predicted Consensus Scores and their 95% Upper Prediction Limits (UPLs). The predicted values are based on a statistical model of Consensus Score as a function of moisture, chloride, and whether the packaged material was electrorefining scrap packaged at Hanford. Consensus Score has been shown to be a useful indicator of corrosion potential, and the UPL captures uncertainty in the model predictions, providing a conservative indicator of corrosion potential. Using UPLs to determine relative risks, together with expert review, three containers were identified as not suitable for retention, and the remainder were determined to be suitable.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Hybrid Bio-Based Composites: Enabling Cellulose Nanofiber (CNF) Incorporation into Composites via Macroscale Natural Fiber Carriers

Cellulose nanofibers (CNFs) have significant potential in composites as additives to improve mechanical properties, melt rheology, and more. However, agglomeration of CNFs is a key challenge in composite melt processing as obtaining nano-level dispersion of CNFs often requires cost- and energy-intensive processes (e.g., solvent exchange or freeze drying) due to the strong hornification tendencies of CNF. Herein, we avoid these challenges by using a natural fiber carrier method to integrate CNF into thermoplastic composites. Fibers are co-dried to create a hybrid fiber feedstock for compounding in which natural fibers are decorated with dispersed nanofibers. The hybridized fibers result in up to a 24% increase in tensile strength and up to a 35% increase in Young’s modulus compared to composites only containing natural fibers. The lignocellulosic nanofibers are found to outperform their purely cellulosic counterpart, which is theorized to be due to either an increased propensity for fibrillation of the lignocellulosic fibers or the increased hydrophobicity of the fibers due to the presence of lignin. Surface analysis of fiber feedstocks, via streaming potential measurements and dynamic light scattering (DLS), confirmed a significant change in the feedstock hydrophobicity before and after hybridization. While mild additions of CNF (1 wt.% on the macroscale fiber) do not impact the composite melt viscosity, the viscosity is found to increase at higher CNF loadings (5 wt.% on the macroscale fiber), indicating its utility as a rheology modifier. Lastly, use of these materials as novel feedstocks for medium-scale additive manufacturing in high-fidelity part production was demonstrated.

bio-based↗

DOE OSTI2025

Novel Zwitterionic Polyurethane-in-Salt Electrolytes with High Ion Conductivity, Elasticity, and Adhesion for High-Performance Solid-State Lithium Metal Batteries

This study presents a novel polymer-in-salt (PIS) zwitterionic polyurethane-based solid polymer electrolyte (zPU-SPE) that offers high ionic conductivity, strong interaction with electrodes, and excellent mechanical and electrochemical stabilities, making it promising for high-performance all solid-state lithium batteries (ASSLBs). The zPU-SPE exhibits remarkable lithium-ion (Li+) conductivity (3.7 × 10⁻⁴ S cm−1 at 25 °C), enabled by exceptionally high salt loading of up to 90 wt.% (12.6 molar ratio of Li salt to polymer unit) without phase separation. It addresses the limitations of conventional SPEs by combining high ionic conductivity with a Li+ transference number of 0.44, achieved through the incorporation of zwitterionic groups that enhance ion dissociation and transport. The high surface energy (338.4 J m−2) and elasticity ensure excellent adhesion to Li anodes, reducing interfacial resistance and ensuring uniform Li+ flux. When tested in Li||zPU||LiFePO₄ and Li||zPU||S/C cells, the zPU-SPE demonstrated remarkable cycling stability, retaining 76% capacity after 2000 cycles with the LiFePO4 cathode, and achieving 84% capacity retention after 300 cycles with the S/C cathode. Molecular simulations and a range of experimental characterizations confirm the superior structural organization of the zPU matrix, contributing to its outstanding electrochemical performance. The findings strongly suggest that zPU-SPE is a promising candidate for next-generation ASSLBs.

Wang, Kun↗

DOE OSTI2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li + , by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH – dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li + -coupled electron transfer along the PAN backbone, causing ∼10 4 times faster sequential ring-formation of the remaining nitriles. PAN’s extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

Chahal-Crockett, Rajni [Oak Ridge National Laborat↗

DOE OSTI2025

Amphiphilic nanopores that condense undersaturated water vapor and exude water droplets

Condensation of water vapor in confined geometries, known as capillary condensation, is a fundamental phenomenon with far-reaching implications. While hydrophilic pores enable liquid formation from undersaturated vapor without energy input, the condensate typically remains confined, limiting practical utility. Here, we explore the use of amphiphilic nanoporous polymer-infiltrated nanoparticle films that condense and release liquid water under isothermal and undersaturated conditions. By tuning the polymer fraction and nanoparticle size, we optimize condensation and droplet formation. As vapor pressure increases, voids fill with condensate, which subsequently exudes onto the surface as microscopic droplets. This behavior, enabled by a balance of polymer hydrophobicity and capillarity, reveals how amphiphilic nanostructures can drive accessible water collection. Our findings provide design insights for materials supporting energy-efficient water harvesting and heat management without external input.

Science & Technology - Other Topics↗

DOE OSTI2025

Enhancing cathode composites with conductive alignment synergy for solid-state batteries

Enhancing transport and chemomechanical properties in cathode composites is crucial for the performance of solid-state batteries. Our study introduces the filler-aligned structured thick (FAST) electrode, which notably improves mechanical strength and ionic/electronic conductivity in solid composite cathodes. The FAST electrode incorporates vertically aligned nanoconducting carbon nanotubes within an ion-conducting polymer electrolyte, creating a low-tortuosity electron/ion transport path while strengthening the electrode’s structure. This design not only mitigates recrystallization of the polymer electrolyte but also establishes a densified local electric field distribution and accelerates the migration of lithium ions. The FAST electrode showcases outstanding electrochemical performance with lithium iron phosphate as the active material, achieving a high capacity of 148.2 milliampere hours per gram at 0.2 C over 100 cycles with substantial material loading (49.3 milligrams per square centimeter). This innovative electrode design marks a remarkable stride in addressing the challenges of solid-state lithium metal batteries.

Science & Technology - Other Topics↗

NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

DOE OSTI2024

Autonomous phototaxis of hydrogel swimmers

The design of synthetic soft matter capable of emulating the complex behaviors of living organisms, such as sensing and adapting to their environment, remains an important challenge in developing biomimetic materials. Functionalized hydrogels are ideal candidates for such materials since they are highly responsive to their environment and can be operated in water. In this work, we investigate a hybrid bonding hydrogel composed of peptide amphiphile supramolecular nanofibers covalently attached to a photoresponsive network, in which high-aspect-ratio ferromagnetic nanowires are aligned along the length of the sample, designed to swim under oscillating magnetic fields. This hybrid hydrogel swimmer can autonomously swim toward a light source by utilizing photoinduced interactions between supramolecular and covalent networks reminiscent of phototactic swimming in living systems. Using a combination of experimental techniques and a continuum model incorporating photochemistry, magnetoelasticity, and hydrodynamics, we explain the swimming mechanism and predict phototactic behavior. Our work highlights the potential role of hybrid bonding polymers, which leverage the interplay between supramolecular assemblies and covalent networks. We demonstrate how these polymers can be tailored to react dynamically to their environment, paving the way for developing intelligent and autonomous robotic systems.

Science & Technology - Other Topics↗

DOE OSTI2025

AUTOIGNITION DELAY TIMES FOR REFORMATE GAS MIXTURES FROM METHANE GAS ENGINES

Methane slip is a prominent issue in natural gas reciprocating engines that are used in transportation and marine applications. The incomplete combustion that results in methane slip can be resolved with the introduction of hydrogen within the combustion mixture to improve methane oxidation and further enable combustion within the engine crevices where methane has previously remained unreacted. Steam methane reforming (SMR) is a common method used to produce hydrogen and can be used to design an onboard device to reduce methane slip from reciprocating engines. The development of this reformer device requires the validation of high-fidelity chemical kinetic models at the low temperatures of the crevice volumes of these engines. In this work, auto-ignition data is obtained using a shock tube at lean (φ—0.714 or λ—1.4) and stoichiometric (φ, λ = 1) equivalence ratios spanning a temperature range of 1042–1234 K at the 80-bar operating pressure of the test engine. Blends of methane, hydrogen, and reformate products from the SMR reaction are shock-heated in synthetic air, with the ignition delay time measured using an OH* chemiluminescence detector at 310 nm and a CH* detector at 430 nm. The experimental results are compared to several state-of-the-art chemical kinetic mechanisms from the literature. In general, most of the mechanisms show very good agreement with experiments at higher temperatures, with simulation results showing little deviation from experiments at lower temperatures. A sensitivity analysis was conducted, and the results reveal that the reaction H2 + CH3O2 = H + CH3O2H has a very significant role in determining low-temperature ignition delay times (IDTs) of SMR mixtures. These findings provide valuable insights into the chemical kinetics governing methane reformate combustion and contribute to the optimization of onboard reformer designs aimed at mitigating methane slip in natural gas-fueled engines.

Fraze, Matthew↗

DOE OSTI2024

Determining Stellar Elemental Abundances from DESI Spectra with the Data-driven Payne

Abstract Stellar abundances for a large number of stars provide key information for the study of Galactic formation history. Large spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and LAMOST take median-to-low-resolution (R≲ 5000) spectra in the full optical wavelength range for millions of stars. However, the line-blending effect in these spectra causes great challenges for elemental abundance determination. Here we employDD-Payne, a data-driven method regularized by differential spectra from stellar physical models, to the DESI early data release spectra for stellar abundance determination. Our implementation delivers 15 labels, including effective temperatureT eff , surface gravity log g , microturbulence velocityv mic , and the abundances for 12 individual elements, namely C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, and Ni. Given a spectral signal-to-noise ratio of 100 per pixel, the internal precisions of the label estimates are about 20 K forT eff , 0.05 dex for log g , and 0.05 dex for most elemental abundances. These results agree with the theoretical limits from the Crámer–Rao bound calculation within a factor of 2. The majority of the accreted halo stars contributed by the Gaia–Enceladus–Sausage are discernible from the disk and in situ halo populations in the resultant [Mg/Fe]–[Fe/H] and [Al/Fe]–[Fe/H] abundance spaces. We also provide distance and orbital parameters for the sample stars, which spread over a distance out to ∼100 kpc. The DESI sample has a significantly higher fraction of distant (or metal-poor) stars than the other existing spectroscopic surveys, making it a powerful data set for studying the Galactic outskirts. The catalog is publicly available.

Astronomy & Astrophysics↗

DOE OSTI2026

Demonstration of gold nanorod systems for enhanced total efficiency: Experimental and numerical analysis

This study investigates the photothermal performance of gold nanorods engineered to exhibit longitudinal plasmon resonances at 695 nm, 780 nm, and 970 nm. The work combines synthesis, structural characterization, extinction measurements, numerical modeling, and controlled temperature experiments to quantify how nanorod geometry, resonance tuning, concentration, and chamber shape jointly influence heat generation. Transmission electron microscopy confirms that increasing nanorod aspect ratio systematically shifts the longitudinal plasmon peak toward the near-infrared region. Extinction measurements show strong agreement with theoretical predictions, validating the numerical model across two independent datasets. Three chamber geometries were tested under laser excitation at 640 nm, 808 nm, and 980 nm: an ascending stepped base, a flat base, and a descending stepped base. Without nanorods, the ascending geometry produced the highest efficiency due to enhanced natural convection. After introducing gold nanorods, all geometries exhibited substantial thermal enhancement, with total efficiencies exceeding 20%. The strongest improvement was obtained for nanorods resonant at 780 nm with a mass concentration of 4.6 mg/mL implemented on the descending stepped-base geometry. This performance resulted from the combined effect of spectral overlapping with the 808 nm laser, the highest nanorod concentration, and localized heat accumulation that intensified buoyancy-driven flow. The findings demonstrate that total efficiency is governed by a synergistic interplay between optical resonance, nanoparticle concentration, and macroscopic chamber design, revealing the system-level coupling between nanoscale plasmonic absorption and macroscale heat-transfer phenomena. The results provide a validated framework for tuning nanoscale plasmonic absorbers and optimizing thermal systems for applications requiring efficient light-to-heat conversion.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRSDate not supplied

Strong Thermoset Regolith UV-Curable Composite Technology (STRUCT) Overview

Future lunar surface missions require construction materials that can be manufactured in situ using lo-cal resources while operating under extreme environmental conditions. Many Lunar material demands can be solved solely with regolith by compacting or sintering. And yet past Lunar missions rely on polymeric materials, and sustained Lunar missions must reduce Earth-supplied polymers dependence. Dual-cure (Ultraviolet (UV) and thermal) polymer-regolith composites offer a promising pathway by leveraging solar UV radiation, moderate thermal in-put, and regolith. Mission mass limits, power availability and energy constraints on the lunar surface further motivate low-energy processing and curing strategies for surface construction materials. The Strong Thermoset Regolith UV-Curable Composite Technology (STRUCT) project has successfully synthesized and demonstrated dual-cure photopolymer resins derivable from in-situ resources [3]. Morphological, thermal, and mechanical characterization show that the newly formulated UV curable resin systems integrates well with lunar regolith simulants. Processing and chemistry changes, and computational analysis advanced the composite design. X-ray CT scanned and computational analysis demonstrate that resin, regolith and additives are well incorporated. The large fraction of regolith, 95% by mass, large char yield (82% mass), low thermal conductivity (0.26 W/m/K), confirm this material as a promising high-performance thermal and structural material.

thermal conductivity↗

DOE OSTI2026

Electron-Deficient Phenazines: Synthesis, Structures, Redox, and Optoelectronic Properties in the Gas Phase, in Solution, and in the Solid State

High-temperature gas-phase reactions of 1,4-C4F8I2 with phenazine (PHNZ) afforded new thermally- and air-stable derivatives with cyclo-C4F8 substituents, viz. 1,2-PHNZ(C4F8), 2,3-PHNZ(C4F8), 1,2:6,7-PHNZ(C4F8)2, 1,2:7,8-PHNZ(C4F8)2, and 1,2:8,9-PHNZ(C4F8)2. Reactions in the presence of Cu powder resulted in reductive-defluorination/aromatization of some of the cyclo-C4F8 substituents to produce 2,3-PHNZ(C4F4), 2,3-PHNZ(C4HF3), and 1,2:6,7-PHNZ(C4F8)(C4F4), which are formally tri- or tetrafluoro derivatives of benzo[a]phenazine. Single-crystal X-ray diffraction structures of these compounds demonstrate the planarity of 2,3-PHNZ(C4F4) and 2,3-PHNZ(C4HF3) and ordered p-stacking in 1,2-PHNZ(C4HF3), 1,2-PHNZ(C4F4), 1,2-PHNZ(C4F8), and 2,3-PHNZ(C4F8). Low-temperature gas-phase photoelectron spectra of the PHNZ(C4F8)1,2 compounds show that they are strong electron acceptors, with the three PHNZ(C4F8)2 isomers having electron affinities exceeding that of C60. Cyclic voltammetry in acetonitrile show that the five PHNZ(C4F8)1,2 compounds exhibit reversible one-electron reductions with large positive shifts in their reduction potentials relative to unsubstituted PHNZ. Spectroelectrochemical absorption and EPR spectra of PHNZ(C4F8)1,2- • anion radicals and extensive DFT calculations revealed the significant role that the different substitution patterns have on determining the optoelectronic properties of the PHNZ(C4F8)1,2 derivatives and PHNZ(C4F8)1,2- • anion radicals. Time-resolved microwave conductivity measurements and photoluminescence spectra of blends of the PHNZ(C4F8)1,2 derivatives with the donor poly(3-hexylthiophene) (P3HT) were recorded to assess the possible use of the new compounds in optoelectronic devices.

Balser, Sebastian↗

DOE OSTI2026

Heat Transfer Fluids as Co‐Diluents in Localized High‐Concentration Electrolytes for High‐Rate Lithium Metal Batteries With Enhanced Safety

Localized high-concentration electrolytes (LHCEs) have been identified as promising electrolyte formulations for lithium metal batteries, due to their effective interphase formation and promotion of compact Li deposition, yet their practical implementation is often limited by reduced ion transport kinetics. In this study, two industrially established fluorinated ethers are identified for the first time in battery research as effective co-diluents as they combine a broad electrochemical stability window with a low viscosity and intrinsic non-flammability. Incorporating these components, commonly used as heat transfer fluids, yields safer, less flammable electrolyte formulations with enhanced ion mobilities. In particular, the ternary co-diluent formulation shows improved ion mobility by reducing the electrolyte's viscosity while limiting excessive ion clustering. Based on the improved electrolyte transport kinetics, lower overvoltages and higher Coulombic efficiencies at current densities ≥ 1 mA cm −2 are achieved with the ternary co-diluent blend, resulting in markedly extended cycle life in an application-oriented zero-excess pouch cell compared with the baseline system. Complementary electrochemical and ex situ analysis of harvested electrodes at moderate current densities reveals no discernible differences in interphase morphology and composition, suggesting enhanced ion mobility as the primary cause of the improved high-rate performance.

electrolyte diluent↗

DOE OSTI2026

Advancing technologies for lignin-based jet fuel production in aqueous phase

Integrating lignin into a cellulosic ethanol plant for the co-production of lignin-based jet fuel (LJF) in aqueous phase offers a significant opportunity to boost operational efficiency, economic viability, carbon conversion, and the overall sustainability of biofuel and chemical production. LJF is lignin-structure-based jet fuel blendstocks primarily composed of alkyl-substituted mono-, bi-, and tri-cyclohexanes. It exhibits high energy density, potential for low emissions, and favourable blend characteristics that comply with drop-in specifications. An overview of lignin feedstock, catalytic processes, LJF chemical compositions, fuel properties tests, and techno-economic analysis (TEA) and life cycle assessment (LCA) indicate that (1) the reactivity of lignin plays a crucial role in its structure transformation to LJF molecules; (2) catalytic processing of lignin to LJF can occur through a simultaneous depolymerization and hydrodeoxygenation process, bypassing the intermediate step of producing and upgrading lignin-derived oil; (3) LJF's uniqueness molecules making it more promising for high energy content and low emission jet fuel properties for next generation sustainable aviation fuel (SAF); and (4) TEA and LCA demonstrate that LJF is not only potentially cost-effective but also offers favourable carbon footprint compared to other SAFs. In conclusion, this review highlights the most recent advancements in LJF technology, along with the challenges and opportunities that lie ahead in fulfilling its potential.

09 BIOMASS FUELS↗