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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Chloride Molten Salt Electrolysis Enables Integrated and Energy-Efficient Process for NdFeB Magnet Fabrication

Rare-earth elements (REEs) have been identified by NATO, the USDOE, and USGS as critical materials, i.e., materials which have significant demand yet pose supply-chain risks. Many of the existing processes for separations, metallization, and final parts production used across the REE supply chain involve energy intensive steps. For example, neodymium (Nd or NdPr) is produced using oxyfluoride electrolysis of Nd 2 O 3 , which requires hydrofluoric acid to produce a key electrolyte component (NdF 3 ) and generates undesired perfluorocarbon (PFC) gases. Such challenges make securing a resilient supply chain for NdFeB permanent magnets in countries like the United States prohibitively difficult. Here, we propose a chloride-based MSE process that circumvents these challenges, delivering high-purity NdPr from a (NdPr)Cl 3 feed from upstream REE separations. This eliminates environmentally-damaging steps of oxalate or carbonate precipitation and calcination, and enables superior production rates due to greater solubility of (NdPr)Cl 3 in chloride melts compared to Nd 2 O 3 . We show that CMSE generates high-purity NdPr (99.4 wt.%) while being energy-efficient (~ 6 kWh/kg-Nd). NdPr from CMSE was used to fabricate a NdFeB magnet with an excellent maximum energy product (> 40 MGOe), comparable to commercially available NdFeB magnets. This establishes CMSE as a leading approach for integrated, energy-efficient NdFeB magnet production.

Materials science↗
DOE OSTI2023

Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies

Abstract It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Green-Solvent Processed Blade-Coating Organic Solar Cells with an Efficiency Approaching 19% Enabled by Alkyl-Tailored Acceptors

Power-conversion-efficiencies (PCEs) of organic solar cells (OSCs) in laboratory, normally processed by spin-coating technology with toxic halogenated solvents, have reached over 19%. However, there is usually a marked PCE drop when the blade-coating and/or green-solvents toward large-scale printing are used instead, which hampers the practical development of OSCs. Here, a new series of N-alkyl-tailored small molecule acceptors named YR-SeNF with a same molecular main backbone are developed by combining selenium-fused central-core and naphthalene-fused end-group. Thanks to the N-alkyl engineering, NIR-absorbing YR-SeNF series show different crystallinity, packing patterns, and miscibility with polymeric donor. The studies exhibit that the molecular packing, crystallinity, and vertical distribution of active layer morphologies are well optimized by introducing newly designed guest acceptor associated with tailored N-alkyl chains, providing the improved charge transfer dynamics and stability for the PM6:L8-BO:YR-SeNF-based OSCs. As a result, a record-high PCE approaching 19% is achieved in the blade-coating OSCs fabricated from a green-solvent o-xylene with high-boiling point. Notably, ternary OSCs offer robust operating stability under maximum-power-point tracking and well-keep > 80% of the initial PCEs for even over 400 h. Our alkyl-tailored guest acceptor strategy provides a unique approach to develop green-solvent and blade-coating processed high-efficiency and operating stable OSCs, which paves a way for industrial development.

14 SOLAR ENERGY↗
DOE OSTI2024

Lax-Oleinik-Type Formulas and Efficient Algorithms for Certain High-Dimensional Optimal Control Problems

Two of the main challenges in optimal control are solving problems with state-dependent running costs and developing efficient numerical solvers that are computationally tractable in high dimension. In this paper, we provide analytical solutions to certain optimal control problems whose running cost depends on the state variable and with constraints on the control. We also provide Lax-Oleinik-type representation formulas for the corresponding Hamilton-Jacobi partial differential equations with state-dependent Hamiltonians. Additionally, we present an efficient, grid-free numerical solver based on our representation formulas, which is shown to scale linearly with the state dimension, and thus, to overcome the curse of dimensionality. Using existing optimization methods and the min-plus technique, we extend our numerical solvers to address more general classes of convex and nonconvex initial costs. We demonstrate the capabilities of our numerical solvers using implementations on a central processing unit (CPU) and a field-programmable gate array (FPGA). In several cases, our FPGA implementation obtains over a 10 times speedup compared to the CPU, which demonstrates the promising performance boosts FPGAs can achieve. Furthermore, our numerical results show that our solvers have the potential to serve as a building block for solving broader classes of high-dimensional optimal control problems in real-time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. Here, to address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

$χ$-$MeRA$: Computationally efficient adaptive mesh refinement of Monte Carlo mesh based tallies

Here, the reactor physics community is always focused on reducing the computational time and memory required for simulations. $χ$-$MeRA$, which stands for flux-based-($χ$)-Mesh tally Refinement Adaptively, was built to reduce the computational time and memory required to solve the neutronics side of a multiphysics problem when compared to traditional methods for mesh based tallies in Monte Carlo (MC) simulations. $χ$-$MeRA$ couples a MC code with an adaptive mesh refinement (AMR) algorithm to take advantage of the accuracy of a MC code and the efficiency of an AMR algorithm. Also developed within $χ$-$MeRA$ was a set of metrics to assess the effects of the refinement on various parameters in the simulation space. For a plutonium sphere, $χ$-$MeRA$ shows a reduction in memory usage and computation time when compared to a fully refined mesh by a factor of 14.7 and 6.7, respectively. When compared to an unstructured mesh, improvement of 1.3 and 4.8 was achieved for memory usage and computation time. The development of $χ$-$MeRA$ helps solve the neutronics side of a multiphysics problem in a faster, more computationally efficient manner than traditional methods, and the final mesh created contains accurate results that can be passed onto the next physics code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Efficient electrocatalytic conversion of CO 2 to ethanol enabled by imidazolium-functionalized ionomer confined molybdenum phosphide

An effective electrochemical carbon dioxide reduction reaction (eCO 2 RR) requires the discovery of a catalytic system that is highly active and selective for multi-carbon products together with superior CO 2 diffusion at a catalyst layer to minimize the reduction barriers. Here, we found a catalytic system that uses molybdenum phosphide (MoP) nanoparticles covered by imidazolium-functionalized ionomer (Im) that promotes CO 2 diffusion at the catalyst layer toward the catalyst surface, where CO 2 is reduced to ethanol (C 2 H 5 OH). The electrochemical results with the MoP-Im co-catalyst show a C 2 H 5 OH production Faradaic efficiency and a cathodic energy efficiency of 77.4% and 63.3%, respectively, at a potential as low as - 200 mV vs. RHE. The electrochemical experiments along with our physicochemical characterizations indicate that the Im improves CO 2 diffusion and balances water content resulting in a higher CO 2 -to-water ratio at the catalyst layer and fine-tunes the electronic properties of Mo atoms at the MoP surface. Finally, in-situ Raman spectroscopy reveals that a high number of adsorbed *CO intermediates on the surface and a higher binding strength of *CO intermediates on the Mo surface sites in the presence of imidazolium molecules are the main reasons for a superior C-C coupling and thereby the improved C 2 H 5 OH formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Heterostructured nano-catalysts with efficient metal-oxide interfaces unlock high-performance direct methanol protonic ceramic fuel cells

Direct methanol protonic ceramic fuel cells (PCFCs) are attractive due to their low cost, convenient storage, and high volumetric energy density, as well as their suitability for transportation. However, the poor coking tolerance of conventional nickel-based anodes leads to their susceptibility to severe carbon deposition and significant deactivation after long-term exposure to hydrocarbons. Herein, we report a nano-catalyst of Ce 0.6 Ni 0.2 Cu 0.2 O 2 with a heterogeneous structure that is spontaneously reduced into a Ce 0.6 Ni 0.2-x Cu 0.2-x O 2-δ (CeNCO) oxide framework interfaced with a nano NiCu alloy (denoted as NC/CeNCO) under operating conditions, as confirmed by analyses of X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy. A Ni-BaCe 0.7 Y 0.06 Yb 0.06 Zr 0.06 Hf 0.06 Gd 0.06 O 3-δ anode-supported PCFC employing the NC/CeNCO metal-oxide catalyst achieved a peak power density of 1.11 W cm −2 and operational stability of about 100 h at 700 °C when fueled by 35 % CH 3 OH-15 % H 2 O-50 % N 2 . In conclusion, the enhanced performance and coking resistance are attributed to the efficient interfaces of Ni, Cu, and ceria-based oxide in NC/CeNCO for CH 3 OH reforming, as confirmed by analyses of electrochemical performance and Raman spectroscopy with density functional theory calculations, revealing that these interfaces can enhance CH 3 OH activation and promote efficient OH-mediated carbon removal via COH intermediates.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Maximizing energy efficiency in wastewater treatment plants: A data-driven approach for waste heat recovery and an economic analysis using Organic Rankine Cycle and thermal energy storage

Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and ecofriendly operations across multiple industries, notably in wastewater treatment plants (WWTPs). This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWh th . The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems. With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R 2 ) value of 68%. On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kW e capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES. Results indicate that the LCOE and PBP are highly affected by the ORC's capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. In conclusion, this study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2023

Energy dissipation efficiency in the CP43 assembly intermediate complex of photosystem II

Photosystem II in oxygenic organisms is a large membrane bound rapidly turning over pigment protein complex. During its biogenesis, multiple assembly intermediates are formed, including the CP43-preassembly complex (pCP43). Here, to understand the energy transfer dynamics in pCP43, we first engineered a His-tagged version of the CP43 in a CP47-less strain of the cyanobacterium Synechocystis 6803. Isolated pCP43 from this engineered strain was subjected to advanced spectroscopic analysis to evaluate its excitation energy dissipation characteristics. These included measurements of steady-state absorption and fluorescence emission spectra for which correlation was tested with Stepanov relation. Comparison of fluorescence excitation and absorptance spectra determined that efficiency of energy transfer from β-carotene to chlorophyll a is 39 %. Time-resolved fluorescence images of pCP43-bound Chl a were recorded on streak camera, and fluorescence decay dynamics were evaluated with global fitting. These demonstrated that the decay kinetics strongly depends on temperature and buffer used to disperse the protein sample and fluorescence decay lifetime was estimated in 3.2–5.7 ns time range, depending on conditions. The pCP43 complex was also investigated with femtosecond and nanosecond time-resolved absorption spectroscopy upon excitation of Chl a and β-carotene to reveal pathways of singlet excitation relaxation/decay, Chl a triplet dynamics and Chl a → β-carotene triplet state sensitization process. The latter demonstrated that Chl a triplet in the pCP43 complex is not efficiently quenched by carotenoids. Finally, detailed kinetic analysis of the rise of the population of β-carotene triplets determined that the time constant of the carotenoid triplet sensitization is 40 ns.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An efficient method to integrate polynomials over polytopes and curved solids

Here in this paper, we present an efficient approach to compute the integral of monomials and polynomials over polyhedra and regions defined by parametric curved boundary surfaces. We use Euler's theorem for homogeneous functions in combination with Stokes's theorem to reduce the integration of a monomial over a three-dimensional solid to its boundary. If the solid is a polytope, through a recursive application of these theorems, the integral is further reduced to just the evaluation of the monomial and its derivatives at the vertices of the polytope. The present approach is simpler than existing techniques that rely on repeated use of the divergence theorem, which require the antiderivative of the monomials and the projection of these functions onto hyperplanes. For convex and nonconvex polytopes, our approach does not introduce any approximation for the integration of monomials. For curved solid regions bounded by surfaces that admit a parameterization, the same approach yields simplified formulas to compute the integral of any homogeneous function, including monomials. For surfaces parameterized by polynomial surfaces (such as Bezier surface triangles and B-spline patches), the method yields machine-precision accuracy for the volumetric integration of monomials with an appropriate quadrature rule. Numerical examples over regions bounded by polynomial surfaces and rational surfaces are presented to establish the accuracy and efficiency of the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Efficient solvers for hybridized three-field mixed finite element coupled poromechanics

We consider a mixed hybrid finite element formulation for coupled poromechanics. A stabilization strategy based on a macro-element approach is advanced to eliminate the spurious pressure modes appearing in undrained/incompressible conditions. The efficient solution of the stabilized mixed hybrid block system is addressed by developing a class of block triangular preconditioners based on a Schur-complement approximation strategy. Robustness, computational efficiency and scalability of the proposed approach are theoretically discussed and tested using challenging benchmark problems on massively parallel architectures.

42 ENGINEERING↗
DOE OSTI2025

Efficient shallow Ritz method for 1D diffusion problems

This paper studies the shallow Ritz method for solving the one-dimensional diffusion problem. It is shown that the shallow Ritz method improves the order of approximation dramatically for non-smooth problems. To realize this optimal or nearly optimal order of the shallow Ritz approximation, we develop a damped block Newton (dBN) method that alternates between updates of the linear and non-linear parameters. Per each iteration, the linear and the non-linear parameters are updated by exact inversion and one step of a modified, damped Newton method applied to a reduced non-linear system, respectively. The computational cost of each dBN iteration is $\mathcal{O}$(n). Starting with the non-linear parameters as a uniform partition of the interval, numerical experiments show that the dBN is capable of efficiently moving mesh points to nearly optimal locations. In conclusion, to improve the efficiency of the dBN further, we propose an adaptive damped block Newton (AdBN) method by combining the dBN with the adaptive neuron enhancement (ANE) method [28].

Diffusion problems↗
DOE OSTI2021

Ultrathin platinum nanowire based electrodes for high-efficiency hydrogen generation in practical electrolyzer cells

Significant reduction of noble metal catalyst loading and simplification of electrode fabrication are urgently needed in order to lower the cost of proton exchange membrane electrolyzer cells (PEMECs) for large-scale hydrogen production. Herein, we report an integrated electrode design comprising in-situ grown platinum nanowires (PtNW) on ultrathin titanium liquid/gas diffusion layers (LGDLs) via a cost-effective and green chemical synthesis approach. The ultrathin integrated PtNW electrodes showed a low cell voltage of 1.643 V and high efficiency of 90.08% at 1000 mA cm -2 using about 15 times lower catalyst loadings than a conventional catalyst-coated membrane in PEMEC tests. Ex-situ electrochemical characterizations and microscale visualizations further reveal that PtNW electrodes display highly efficient hydrogen evolution reactions and excellent electrode durability due to high active surface area, favorable bubble detachment, and structural stability. This work provides new insights into catalyst layer design and facile ultrathin electrode fabrication for more compact and low-cost PEM electrolyzers, fuel cells and other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Influence of design and operating parameters for additively manufactured intensified packing devices on CO 2 -Absorption column cooling and capture efficiency

Solvent-based CO 2 absorption is technologically a matured CO 2 capture pathway but suffers from: high regeneration energy demand, and solvent temperature rise and decreased capture efficiency caused by the heat of reaction. While research has focused on developing non-aqueous and low-aqueous solvents for decreasing the regeneration energy, the temperature bulge due to exothermic absorption is typically dealt with by cooling the solvent with an external inter-stage heat-exchanger. This approach may increase the overall process footprint, as well as capital and operating costs. Here, the current study explores a process intensification approach by incorporating inside the column an additively manufactured intensified packing device that consists of corrugated plates and internal coolant channels. The corrugated plates provide surface area for mass transfer between gas and liquid, while cooling fluid inside the internal channels removes heat from the exothermic reactive system. Two different intensified devices with specific surface areas of 266 m 2 /m 3 and 359 m 2 /m 3 were designed, manufactured, and tested inside a 2.06-m long and 0.203-m diameter column packed with commercial Mellapak 250Y packing. The device with a lower surface area showed up to 27 % reduction in cooling performance. A steady-state heat transfer model provides good agreement with the experimental column temperature and intra-stage heat removal data. Although a decrease in heat transfer performance was observed for the device with lower surface area, CO 2 capture experiments performed with simulated flue gas and low-aqueous solvent demonstrated that both intensified devices lead to a similar improvement of 12 % in capture efficiency. This study provides an understanding on simplifying the intensified packing device geometry and decreasing the device fabrication cost by 25 % without compromising with the CO 2 absorption performance of the packed column.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Double in-situ lignin modification in surfactant-assisted glycerol organosolv pretreatment of sugarcane bagasse towards efficient enzymatic hydrolysis

Organosolv pretreatment effectively fractionates lignocellulosic biomass for efficient enzymatic hydrolysis, yielding fermentable sugars. However, substantial lignin redeposition on the substrate surface and residual lignin content exacerbate lignin’s inhibitory effects on subsequent enzymatic hydrolysis. Here, an in-situ lignin modification is proposed to address this challenge by incorporating polyethylene glycol (PEG) series into glycerol organosolv (GO) pretreatment. According to the results obtained, PEG-assisted GO pretreated substrate exhibited significantly increased sugar yield during enzymatic hydrolysis, particularly with PEG 4000, showing 35.4% higher glucose yield over 72 h. The improved glucose yield with PEG could be attributed to changes in the physicochemical structure and properties of residual lignin, mitigating its non-productive interaction with cellulase enzymes. PEG's presence in GO pretreatment also increased β-O-4 linkages preservation by 55% and reduced phenolic hydroxyl groups by 28% in lignin fragments versus no surfactants. This outcome resulted from introducing glycerol and PEG hydroxyl tails into the lignin structure, forming α-etherified lignin and rendering it more hydrophilic. Simulations confirmed strong Van Der Waals and hydrogen bonding interactions between lignin units and PEG, hindering lignin fragments repolymerization. Incorporating PEG in the organosolv pretreatment proves highly beneficial for cellulase-mediated lignocellulosic biorefinery, resembling a lignin-first strategy. The optimized process enables efficient biomass utilization for sustainable fuels and chemicals production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Gas-solid reaction-based selective lithium leaching strategy for efficient LiFePO 4 recycling

As the electric-vehicle market continues to expand, LiFePO 4 (LFP) batteries, valued for their intrinsic safety and cost-effectiveness, are being increasingly utilized. However, this widespread adoption highlights the urgent need for innovative and environmentally friendly recycling methods for spent LFP batteries due to their relatively low material value and the environmental challenges associated with traditional recycling processes. Here, in this study, we present a novel selective lithium leaching technique that involves a gas–solid reaction with chlorine gas. This method achieves a remarkable leaching efficiency of 99.8 % and a selectivity of 98.8 % at 200 °C within just 10 min, without generating acidic wastewater. The resulting LiCl solution was successfully converted into Li 2 CO 3 with an excellent purity of 99.5 %, while producing NaCl solution as the only byproduct. Notably, the olivine structure of the LFP was preserved as FePO 4 after lithium leaching. The regenerated LFP demonstrated excellent performance, retaining 94.1 % of its capacity after 150 cycles, while the lithium-leached FePO 4 delivered a reversible capacity exceeding 150 mAh/g. This approach not only enhances the efficiency of LFP recycling but also paves the way for more sustainable battery technologies.

36 MATERIALS SCIENCE↗