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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 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Scalable fabrication of high activity nanoporous copper powders for electrochemical CO 2 reduction via ball milling and dealloying

Electrochemical CO 2 reduction (ECR) is a promising technology to close the anthropic CO 2 circle using renewable energy to achieve carbon neutrality. Future commercialization of ECR will require the development of new catalyst synthesis routes that will allow significant upscaling of catalyst production from current research-level milligram quantities to the kilogram scale and beyond while maintaining the activity and selectivity demonstrated at the research level. Here, we report on generating and testing submicron-sized nanoporous copper (npCu) particles by using a scalable approach consisting of ball milling brittle Cu-based intermetallics followed by dealloying to add nanoporosity for high surface area. The resulting npCu particles have been tested in an industry-relevant large area (25 cm 2 ) electrolyzer platform and showed Faraday efficiencies (FE) for ethylene up to 34 % at current densities of 75–100 mA/cm 2 while keeping FE for hydrogen less than 30 %. Our results demonstrate that a combination of ball milling and dealloying is a promising approach to generate large quantities of high activity and high surface area npCu particles for ECR at an industry-relevant scale.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Regular sensitivity computation avoiding chaotic effects in particle-in-cell plasma methods

Particle-in-cell (PIC) simulation methods are attractive for representing species distribution functions in plasmas. However, as a model, they introduce uncertain parameters, and for quantifying their prediction uncertainty it is useful to be able to assess the sensitivity of a quantity-of-interest (QoI) to these parameters. Such sensitivity information is likewise useful for optimization. However, computing sensitivity for PIC methods is challenging due to the chaotic particle dynamics, and sensitivity techniques remain underdeveloped compared to those for Eulerian discretizations. This challenge is examined from a dual particle–continuum perspective that motivates a new sensitivity discretization. Two routes to sensitivity computation are presented and compared: a direct fully-Lagrangian particle-exact approach provides sensitivities of each particle trajectory, and a new particle-pdf discretization, which is formulated from a continuum perspective but discretized by particles to take the advantages of the same type of Lagrangian particle description leveraged by PIC methods. Since the sensitivity particles in this approach are only indirectly linked to the plasma-PIC particles, they can be positioned and weighted independently for efficiency and accuracy. The corresponding numerical algorithms are presented here in mathematical detail. The advantage of the particle-pdf approach in avoiding the spurious chaotic sensitivity of the particle-exact approach is demonstrated for Debye shielding and sheath configurations. In essence, the continuum perspective makes implicit the distinctness of the particles, which circumvents the Lyapunov instability of the N -body PIC system. The cost of the particle-pdf approach is comparable to the baseline PIC simulation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Tusas: A fully implicit parallel approach for coupled phase-field equations

In this study, we develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials science, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our simulation approach consists of a finite element spatial discretization of the fully-coupled nonlinear system of partial differential equations at the microscale, which is treated implicitly in time with a preconditioned Jacobian-free Newton-Krylov method. The approach is algorithmically scalable as well as efficient due to an effective preconditioning strategy based on algebraic multigrid and block factorization. We implement this approach in the open-source Tusas framework, which is a general, flexible tool developed in C++ for solving coupled systems of nonlinear partial differential equations. The performance of our approach is analyzed in terms of algorithmic scalability and efficiency, while the computational performance of Tusas is presented in terms of parallel scalability and efficiency on emerging heterogeneous architectures. We demonstrate that modern algorithms, discretizations, and computational science, and heterogeneous hardware provide a robust route for predictive phase-field simulation of microstructure evolution during additive manufacturing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

MOCVD Surface Preparation of V-Groove Si for III-V Growth

V-groove nanopatterning of Si substrates has recently demonstrated promise for achieving high-quality III-V-on-Si epitaxy while providing a lower-cost processing route than chemo-mechanical polishing to produce epi-ready planar wafers. A key factor in determining the crystalline quality of III-V buffer layers is the Si surface structure and its chemical composition. Unlike planar Si surfaces, the surfaces of V-grooves prior to growth have not been studied in detail. Here, we study the surface of V-groove Si prepared for GaP nucleation via X-ray photoelectron spectroscopy and low-energy electron diffraction. We identify several pretreatments, using both 830 and 1000 annealing under an As background pressure, as being suitable for deoxidizing and cleaning the V-groove Si surface. The V-groove Si was found to behave similarly to reference Si(0 0 1) and Si(1 1 1) planar samples, demonstrating that in situ techniques such as reflection anisotropy spectroscopy can be used on reference samples to infer the state of the V-groove surface, and indicating that the extensive research on planar Si surfaces can be directly applied to V-grooves.

III-V epitaxy↗
DOE OSTI2022

A pilot-process for calcium hydroxide production from iron slag by low-temperature precipitation

The production of Ca(OH) 2 via thermal decomposition of limestone is an energy intensive process resulting in significant CO 2 emissions. However, if produced in a manner that obviates the need for the thermal decomposition of limestone, Ca(OH) 2 could be a “CO 2 -negative” material. Herein, we design and demonstrate the operation of a continuous, low-temperature (< 100 °C), aqueous-phase pilot-process to produce Ca(OH) 2 using calcium extracted from alkaline industrial wastes. The three-step process encompasses unit operations including: (i) calcium leaching from basic oxygen furnace BOF slag, (ii) leachate concentration by reverse osmosis (RO), and (iii) Ca(OH) 2 precipitation by temperature swing. Here this process presents several advantages compared to the traditional route. First, it advances the recycling of waste from the steel industry. Second, it operates significantly below the temperature used in conventional lime production (900 °C) as it bypasses limestone calcination, responsible for ~65% of the CO 2 emissions from the current process. Thus, the new process has a lower CO 2 footprint and enables the use of industrial waste heat. The mass and energy balances were quantified to reveal that increasing the calcium concentration of the feed solution and the precipitation temperature, decrease the energy demands of the RO step, thereby reducing the process’s overall CO 2 footprint. The pilot system operated continuously and achieved a production rate of nearly 1 kg per day of Ca(OH) 2 with a purity greater than 95 wt%. The average particle size of the precipitates depended on the residence time in the precipitation reactor, demonstrating an ability to produce size-controlled particulates. Importantly, the process achieved full water recirculation/reutilization level indicative of a low-consumable water demand. The outcomes offer new insights and understanding relevant to developing and upscaling low-CO 2 processes for cement, lime, and portlandite.

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

Oxygen vacancy-rich amorphous FeNi hydroxide nanoclusters as an efficient electrocatalyst for water oxidation

Here, a one-pot strategy is presented to directly synthesize amorphous Fe x Ni y hydroxide nanoclusters (denoted as ANC-Fe x Ni y , <2 nm) with oxygen vacancies induced by ionic liquids. The ANC-Fe x Ni y catalyst presents abundant catalytic sites and high intrinsic conductivity. As such, the optimized ANC-Fe 1 Ni 2 exhibits high activity in oxygen evolution reaction (OER) with a Tafel slope of 39 mV dec –1 and an overpotential of 266 mV at 10 mA cm –2 . Notably, the optimized ANC-Fe 1 Ni 2 shows an extraordinarily large mass activity of 3028 A g FeNi –1 at the overpotential of 300 mV, which is ~24-fold of commercial RuO 2 catalyst. The superior activity of these Fe x Ni y hydroxide nanoclusters is ascribed to (i) the amorphous and distorted structure with abundant oxygen vacancies, and (ii) enhanced active site density by downsizing the ANC-Fe x Ni y clusters. This strategy provides a novel route for enhancing OER electrocatalytic performance and highly encouraging for the future application of amorphous metal hydroxides in catalysis.

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

Bimetallic NiCo boride nanoparticles confined in a MXene network enable efficient ambient ammonia electrosynthesis

Ambient electrocatalytic nitrogen fixation is an emerging technology for green ammonia synthesis, but the absence of optimized, stable and performant catalysts can render its practical application challenging. Herein, bimetallic NiCo boride nanoparticles confined in MXene are shown to accomplish high-performance nitrogen reduction electrolysis. Taking advantage of the synergistic effect in specific compositions with unique electronic d and p orbits and typical architecture of rich nanosized particles embedded in the interconnected conductive network, the synthesized MXene@NiCoB composite demonstrates extensive improvements in nitrogen molecule chemisorption, active area exposure and charge transport. As a result, optimal NH 3 yield rate of 38.7 μg h -1 mgcat. -1 and Faradaic efficiency of 6.92% are acquired in 0.1 M Na 2 SO 4 electrolyte. Moreover, the great catalytic performance can be almost entirely maintained in the cases of repeatedly-cycled and long-term electrolysis. Theoretical investigations reveal that the nitrogen reduction reaction on MXene@NiCoB catalyst proceeds according to the distal pathway, with a distinctly-reduced energy barrier relative to the Co 2 B counterpart. In conclusion, this work may inspire a new route towards the rational catalyst design for the nitrogen reduction reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Synergistic double-atom catalysts of metal-boron anchored on g-C 2 N for electrochemical nitrogen reduction: Mechanistic insight and catalyst screening

The rational design of a novel catalytic center with a sound basis remains both challenging and rewarding for the electrochemical reduction of N 2 (eNRR), which has provided a feasible route for achieving clean and sustainable NH 3 production under ambient conditions. Herein, using density functional theory calculations, we demonstrate that hybrid metal (M)-boron (B) double-atom catalysts (DACs) embedded in g-C 2 N substrate (M-B@C 2 N, M = 3d, 4d and 5d transition metals) can achieve both high catalytic activity and high selectivity in eNRR. The proposed M-B@C 2 N DACs have exhibited impressive feasibility and stability thanks to the resilient and robust C 2 N substrate with abundant pyridinic N atoms distributed among right-sized pore structures. Our results reveal that like the metal center, the embedded B atom can actively involve in Ntriple bondN bond activation via π*-backdonation mechanism concomitant with the substantial charge transfer to adsorbed *N 2 , leading to sizable Nsingle bondN bond elongation. Accordingly, both adsorption energy and Nsingle bondN bond length of *N 2 can be employed as catalytic descriptors for predicting eNRR activity in terms of the limiting potentials (UL). Using high-throughput screening method, we found that six M-B@C 2 N candidates have stood out as the outstanding electrocatalysts for driving eNRR, namely, M = Ti (U L = 0 V), Mo (U L = 0 V), Nb (U L = -0.04 V), W (U L = -0.23 V), Zr (U L = -0.26 V), V (U L = -0.28 V). The underlying origin is attributed to the balanced and constrained N-affinity of M-B dual site working in synergy, which can thus be used as one important guide of catalyst design.

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

Densification of the entropy stabilized oxide (Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 )O

The first entropy-stabilized oxide, (Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 )O, was reported in 2015. Initial studies synthesized this material using solid state processing and were limited to densities < 80%. Here, we report a straightforward solid state route to sinter samples to densities up to 98% of the theoretical by identifying the role of oxygen and promoting the resulting mechanisms in densification. Previous works have studied effects of cation stoichiometry on the entropy-driven reaction to form a single phase, but few have explored the associated effects of anion stoichiometry and/or redox chemistry on both phase stability and densification. We demonstrate here that tuning heating rate and pO 2 during heating of initially-homogeneous calcined powders can enhance densifying diffusion processes and enable reliable sintering of dense (Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 )O samples.

MATERIALS SCIENCE↗
DOE OSTI2021

Investigation of $\gamma$-irradiated polyvinylidene fluoride and its acute toxicity

Polyvinylidene fluoride (PVDF) is an industrial polymer with many applications. The EPR spectrum of polyvinylidene fluoride (PVDF) irradiated by γ-rays at low temperature (-196°C) provides evidence for the presence of radicals formed by loss of a fluorine atom from PVDF, and polyenyl radicals. Polyenyl radicals dominate in the polymer irradiated at room temperature. Peroxide radicals formed as a result of oxidation of primary free radicals by atmospheric oxygen are observed in the EPR spectrum of a polymer irradiated in air, or when air is introduced to a polymer irradiated in a vacuum. Carbonization and oxidation of γ-irradiated PVDF were observed in the XPS spectrum. A model for polymer degradation based on correlated G3(MP2) molecular orbital theory electronic structure calculations is described in this report. The presence of HF in the products and a low yield of products with the composition CxFyHz indicate the absence of noticeable thermal destruction of the main polymer chain upon heating to 220°C of both the initial and γ-irradiated PVDF. Although any HF released during the PVDF dehydrofluorination would be toxic, a study of the acute toxicity of γ-irradiated PVDF showed that the polymer does not exhibit toxicity after a single intragastric route of administration to mice.

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

A generic scenario analysis of end-of-life plastic management: Chemical additives

Plastic growing demand and the increment in global plastics production have raised the number of spent plastics, out of which over 90% are either landfilled or incinerated. Both methods for handling spent plastics are susceptible to releasing toxic substances, damaging air, water, soil, organisms, and public health. Improvements to the existing infrastructure for plastics management are needed to limit chemical additive release and exposure resulting from the end-of-life (EoL) stage. This article analyzes the current plastic waste management infrastructure and identifies chemical additive releases through a material flow analysis. Additionally, we performed a facility-level generic scenario analysis of the current U.S. EoL stage of plastic additives to track and estimate their potential migration, releases, and occupational exposure. Potential scenarios were analyzed through sensitivity analysis to examine the merit of increasing recycling rates, using chemical recycling, and implementing additive extraction post-recycling. Our analyses identified that the current state of plastic EoL management possesses high mass flow intensity toward incineration and landfilling. Although maximizing the plastic recycling rate is a reasonably straightforward goal for enhancing material circularity, the conventional mechanical recycling method requires improvement because major chemical additive release and contamination routes act as obstacles to achieving high-quality plastics for future reuse and should be mitigated through chemical recycling and additive extraction. In conclusion, the potential hazards and risks identified in this research create an opportunity to design a safer closed-loop plastic recycling infrastructure to handle additives strategically and support sustainable materials management efforts to transform the US plastic economy from linear to circular.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

58 GEOSCIENCES↗
DOE OSTI2022

Soil frost controls streamflow generation processes in headwater catchments

The relationship between snowmelt and spring streamflow is changing under warming temperatures and diminishing snowpack. At the same time, the hydrologic connectivity across catchment landscape elements, such as snowpack and surface wetlands, can play a critical role in controlling the routing of snowmelt to streams. The role of hydrologic connectivity is important in headwater regions of the continental northern latitudes, where catchments have low topographic relief and seasonally frozen ground. Nevertheless, the effects of soil frost on the sequence, timing, and magnitudes of hydrologic events that drive the movement of water from a snowpack to a stream are not fully understood. Therefore, we examine two questions: First, what is the flowpath that snow melt and precipitation from spring rain events takes to generate spring streamflow, and second, what hydrologic, climatic, or landscape variables exert the most control on the magnitude of streamflow? In this work, we use long-term hydrological records from the two reference basins at the Marcell Experimental Forest in northern Minnesota to analyze the cascading effects across precipitation, snow, water table elevation, soil frost, and streamflow in peatland-dominated headwater catchments. We identify a sequence of fill-and-spill effects across the landscape that control the timing of spring streamflow generation. Then, we use stepwise regression to show that soil frost is a key supporting predictor for both the magnitude of streamflow in the spring as it adds significantly to the predictive power of precipitation and water table elevation. Our results highlight the importance of recognizing the role of soil frost, when present, on the partitioning of snowmelt between overland runoff and water table recharge during the critical snowmelt period, as well as the later partitioning between evapotranspiration and subsurface flows.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Cosolvent-tuned interactions in ionic liquids: A vibrational and quantum-chemical study of ethylene glycol ratio effects

Ionic liquids (ILs) are attractive media for CO 2 capture but remain limited by viscosity and cost. Blending ILs with ethylene glycol (EG) is a practical route to mitigate these constraints, yet the molecular origins of cosolvent effects and their dependence on composition are not well resolved. We combine Fourier-transform infrared (FT-IR) spectroscopy with quantum-chemical (DFT) analysis to elucidate how the IL:EG molar ratio modulates intermolecular interactions and electronic structure. Computed vibrational frequencies enable mode assignment and deconvolution of overlapping bands, revealing systematic, ratio-dependent shifts and broadenings in (i) EG O–H stretching, (ii) cation and EG C–H stretchings (imidazolium C2–H, C4–H, C5–H, methyl and ethyl groups, -CH2 of EG), (iii) anion signature modes (e.g., CN motifs), and (iv) EG C–O and C–C stretchings, consistent with the redistribution of hydrogen-bonding networks. Molecular electrostatic potential (MESP) maps quantify attenuation of extreme potential regions with increasing EG, indicating progressive screening of cation–anion electrostatic interactions. Quantum Theory of Atoms in Molecules (QTAIM) identifies emergent bond critical points between EG and the IL ions, while Reduced Density Gradient–Noncovalent Interaction (RDG–NCI) analysis differentiates strong directional hydrogen bonds from dispersive contacts across compositions. Together, these results show that EG fraction controls a switch from predominantly ion–ion to mixed ion–EG coordination, altering local polarity and polarizability that underlie the observed FT-IR trends. The framework provides composition–structure–spectrum relationships that can guide rational selection of IL:EG ratios to balance favorable molecular interactions with practical performance targets in scalable CO 2 capture systems.

DAC↗
DOE OSTI2026

Aqueous ionic liquid–mediated depolymerization of textile waste

Textile waste, dominated by polyester-cotton blends, largely evades recycling and ends up in landfills or incinerators. Here, we demonstrate an aqueous ionic liquid–mediated route for selective depolymerization of textile waste, including polyester and polycotton (polyester/cotton blend). Using aqueous cholinium lysinate ([Ch][Lys]), we depolymerized poly(ethylene terephthalate) (PET) textiles with over 95 % terephthalic acid (TPA) yields, comparable to virgin PET resin. Notably, colored fabrics showed no inhibitory effect from dyes. While protic ionic liquids such as ethanolammonium acetate showed limited efficiency, their precursor amine enabled near-complete PET conversion—ethanolamine promoted aminolysis with byproduct formation whereas butylamine achieved higher TPA recovery through combined hydrolysis and aminolysis. Importantly, polycotton blends achieved complete PET depolymerization with TPA yield above 85 % and preserved cotton. These results establish aqueous ionic liquids as efficient, selective agents for deconstructing polyester in blended textiles, offering flexible product and ionic liquid management pathways toward scalable and facile textile recycling.

Amines↗
DOE OSTI2020

Manufacturing Process Design of a Micro-Scale Liquid-Liquid Extractor and Multi-phase Separator

Advances in process innovation within the chemical process industry are impeded by the cost of producing new innovative components. In this paper, various manufacturing process routes were evaluated for scaling up a micro-scale liquid-liquid extractor and multi-phase separator capable of separating immiscible phases that are used for the extraction of a chemical product into a lower boiling point solvent to make downstream distillation easier. A manufacturing process design methodology was used to evaluate both photochemical machining and binder jetting as a means for producing the micropost arrays within the separator. Cost curves show that for a 0.3 mm deep micropost array, photochemical machining was found to be much cheaper than binder jetting. However, because binder jetting processes enable microposts with over 13 times higher depth-to-width aspect ratios, binder jetting was found to decrease the overall costs of the extractor/separator by more than half for a 500 barrel per day plant, with breakeven as low as 15 barrels per day. These economics were confirmed by building extractor/separator plates and characterizing them dimensionally and functionally. The binder jet plates were found to perform better than photochemical plates leading to the complete separation of the organic phase from water. The utilization of slower sintering cycles combined with surface grinding provided acceptable dimensional tolerances for the binder jet plates. Using pressurized air during depowdering improved the economics for the binder jetting process.

42 ENGINEERING↗
DOE OSTI2026

Multi-material additive manufacturing of aluminum 6061-T6 alloy with stainless steel 304: Suppression of intermetallic compounds and interface growth mechanism

Fabricating integrated aluminum-steel structures is difficult because of metallurgical incompatibilities that promote intermetallic compound (IMC) formation at their interfaces. This work introduces a practical route for additively manufacturing 6061-T6 aluminum alloy (AA6061-T6) directly onto 304 stainless steel (SS304) using a high shear strain rate-assisted interfacial bonding mechanism. Through the friction extrusion deposition-based additive manufacturing method, both single- and multi-layer deposits of AA6061-T6 were successfully fabricated on SS304, yielding a robust hybrid multi-material structure. Comprehensive analyses of deposition quality, interface porosity, and bonding performance showed that the interface achieved a tensile strength exceeding 154 MPa under quasi-static loading. Microstructural observations revealed that the deposited aluminum layers experienced severe plastic deformation, resulting in pronounced grain refinement. Importantly, the interfacial zone was found to be free of brittle IMCs, instead containing a thin amorphous layer that evolved through a non-linear reaction-zone growth law. This solid-state additive strategy establishes a promising pathway for lightweight structural systems, nuclear energy component cladding, and multifunctional engineering components, redefining how dissimilar metals can be integrated for advanced applications.

Aluminum alloy↗