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At least 991 records · Page 55

Lithography-Free Mesa Isolation of III–V Solar Cells Through Laser Ablation

Eliminating photolithography from solar cell processing is a significant opportunity for cost reduction for III-V solar cells. Here, in this work, we explore femtosecond laser ablation as an alternative to contact photolithography and wet chemical etching for mesa isolation. We demonstrate both GaAs and GaInP solar cells mesa-isolated by femtosecond laser ablation with minimal to no loss in solar cell performance. We show the best results with a 400 fs UV pulsed laser and a short clean-up etch that also serves as a contact layer removal etch.

14 SOLAR ENERGY

RIKEN TRIP Magnets Database

This dataset contains ab-initio calculation results for the temperature-dependent anomalous Hall conductivity, the anomalous Nernst effect, and the Seebeck coefficient. All calculations are based on ab-inito Quantum Espresso (PWSCF v.6.3) + Wannier90 (v.3.0.0). The dependence on carrier doping is also calculated. For all calculations a ferromagnetic order has been assumed, which might not correspond to the true ground state of the system. Tabulated values for the magnetic moments and essential input files for Quantum Espresso are available for download as attachments. This project has been supported by the RIKEN Transformative Research Innovation Platform (TRIP), Use Case: Many-body Electron Systems.

36 MATERIALS SCIENCE

Light, High Performance and Scalable Coal-Derived Composites for Construction: Precast and Cast-in-Place Applications

The overall objective of this project was to produce a coal-based construction material that has up to ~95 weight percent (wt. %) coal with physical, chemical, and thermal properties exceeding those of ordinary Portland cement (OPC)-based construction materials. Additionally, the project aimed to minimize external binders by implementing novel mixing techniques, while exceeding the performance/cost ratio of OPC. Finally, the project was to demonstrate production of precast products via the design and fabrication of products via a bench scale process. Consistent with some of these objectives, the project successfully fabricated samples of coal-based composite materials with >80 wt% coal with physical, chemical and thermal properties on par with cement-based concrete. Select samples demonstrated compressive strengths with >7,000 psi and flexural strength of >420 psi. The composite materials minimized external binders and also demonstrated durability, as evidenced by resistance to acidic and basic solutions. Finally, larger slab and beam type samples were produced using a process developed by the Recipient, although, the process was not semicontinuous in nature. Taken together, the results of this project suggest that domestic coal has potential to serve as a replacement for cementitious materials utilized in incumbent construction technologies, which could significantly reduce the energy and emissions of the construction industry

01 COAL, LIGNITE, AND PEAT

Final Report SC0022244

Final Report: Electro-Nano-Pulsing (ENP) Processing of Advanced Materials

36 MATERIALS SCIENCE

Strain-driven oxygen vacancy ordering in LaNiO 3 thin films revealed by integrated differential phase contrast imaging in scanning transmission electron microscopy

Rare-earth nickelates, such as LaNiO 3 (LNO), exhibit complex electronic properties, with ordered oxygen vacancies (OOV) influencing conductivity and magnetic behavior. We investigate the structural stability of strain-induced OOV phases in LNO thin films grown on SrTiO 3 substrates and the impact of Ruddlesden–Popper (RP) faults. Using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and integrated differential phase contrast (iDPC) STEM imaging, we conducted atomic-scale structural and compositional analyses of OOV. Geometric phase analysis (GPA) was employed to measure the strain in fault-free and RP fault regions, while density functional theory (DFT) calculations explored different OOV arrangements in the LNO phase. Simulated iDPC-STEM imaging of energy-stabilized structures was performed to correlate with experimental results. Here, our findings reveal superstructure modulation in the chemical composition and atomic-scale lattice structure in LNO, primarily due to the formation of the OOV in Ni–O layers of the LaNiO 2.5 phase. The out-of-plane compressive strain of about 2% stabilizes this phase, reducing the strain, diminishing OOV, and transforming them into LNO.

36 MATERIALS SCIENCE

Developing the Science Basis for Understanding Polymer Encapsulant Degradation Mechanisms: DuraMAT 2.0 Final Project Report

Polymeric encapsulants are essential materials in photovoltaic modules, protecting sensitive electronics from the environment while providing mechanical integrity to the multilayered assembly. However, these polymeric materials are susceptible to degradation processes driven by the ingress of environmental species, ultraviolet radiation, thermal stresses, and mechanical loading. In this study, we employ a combined atomistic simulation and accelerated aging experimental approach to study the molecular-scale mechanisms of encapsulant degradation. Classical molecular dynamics simulations quantify the diffusion of environmental and degradation species through the polymer matrix, producing composition-specific diffusion coefficients. Reactive simulations characterize activation energy barriers and reaction rate constants for key chemical pathways. In parallel, thermal-desorption analyses coupled with mass spectrometry monitor the emergence and concentration profiles of degradation products under controlled stressor conditions. By integrating simulation and experiment, we establish quantitative correlations between polymer composition, species diffusivity, and chemical reactivity. We anticipate that these relations and quantitative values could serve as high-fidelity inputs to reaction-diffusion models, enabling physics-informed lifetime predictions and guiding the design of more durable encapsulant materials for solar energy applications.

36 MATERIALS SCIENCE

Achieving the 1D Atomic Chain Limit in Van der Waals Crystals

Experiments with graphene have demonstrated that 2D van der Waals materials can be stable, robust, and efficiently manipulated at the level of individual atomic planes. However, the stability and manipulation of 1D van der Waals materials and individual atomic chains remains elusive. Here, in this work, the ability to exfoliate and process two representative van der Waals materials containing 1D motifs, namely MoI 3 and Ta 2 Se 8 I, at the scale of individual atomic chains is demonstrated. High‐resolution transmission electron microscopy and atomic force microscopy studies confirm the presence of stable individual atomic chains of MoI 3 at room temperature. It is further shown that 1D van der Waals materials with low exfoliation energy, such as Ta 2 Se 8 I, can be processed with electron beams to achieve suspended individual atomic chains. Ab initio calculations corroborate the findings regarding the cleavage energies and the thermodynamic stability of individual atomic chains in these 1D van der Waals materials. These results demonstrate that the top‐down approach in material processing can be extended to the scale of individual chains.

1D materials

Turn‐On of Current‐Induced Spin Torque Upon Non‐collinear Antiferromagnetic Ordering in Delafossite PdCrO 2

We report measurements of the current-induced spin torque produced by the delafossite antiferromagnet PdCrO 2 and acting on an adjacent ferromagnetic permalloy layer. The spin torque increases strongly as the temperature is reduced through the Néel temperature, when the PdCrO 2 transitions from a paramagnetic phase to a noncollinear antiferromagnetic state. This result is qualitatively consistent with density functional theory calculations regarding how spin-current generation changes upon antiferromagnetic ordering in PdCrO 2 .

36 MATERIALS SCIENCE

Toward engineering lattice structures with the material point method (MPM)

This study examines the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load–displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hypothetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.

97 MATHEMATICS AND COMPUTING

Polymer-iron oxide hybrid films for controlling electrokinetic properties

Electrokinetic phenomena at polymer-water interfaces are central to technologies for water purification, ion separations, and energy conversion, yet the ability to systematically control polymer surface charge and associated electrokinetic processes remains limited. Here, we demonstrate a simple liquid-phase infiltration (LPI) method to synthesize polymer–metal oxide hybrid films with controllable interfacial properties. Hydroxy-terminated poly(2-vinylpyridine) (P2VP-OH) brushes grafted to silicon substrates were infiltrated with iron nitrate from ethanolic solution, followed by low-temperature thermal treatment to convert the infiltrated precursor into iron oxide. Spectroscopic ellipsometry, X-ray photoelectron spectroscopy, and thermogravimetric analysis confirmed oxide incorporation and hybrid film formation without polymer degradation. Electrokinetic measurements reveal that the hybrid films acquire the electrokinetic properties of the infiltrated oxide, with concentration-dependent streaming potentials and surface conductivities closely matching those of pure iron oxide films. These results establish metal oxide infiltration as a scalable and low-cost strategy for controlling interfacial charge in polymer surfaces. The approach introduces new materials and design parameters for tailoring ion selectivity, transport, and energy conversion, with broad implications for the development of advanced membranes, electrokinetic harvesting devices, and polymer-supported oxide electrodes.

36 MATERIALS SCIENCE

Machine learning-enhanced design of lead-free halide perovskite materials using density functional theory

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl 4 , Rb 3 Mn 2 Cl 9 , Rb 4 MnCl 6 , Rb 3 MnCl 5 , RbMn 2 Cl 7 , RbMn 4 Cl 9 , and CsIn 2 Cl 7 . The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl 4 is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Here, through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.

36 MATERIALS SCIENCE

Upcycling Polycrystalline LiNi1/3Mn1/3Co1/3O2 to High-Performance Large-Grained LiNi0.6Mn0.2Co0.2O2 via Simplified Polyol-Mediated Recycling

The escalating demand for lithium-ion batteries (LIBs) necessitates advanced recycling strategies that can address both resource scarcity and environmental impact. While conventional hydrometallurgy shows promise, it is challenged by complexity, impurity management, and environmental footprint. Here, we report a strategic polyol-metallurgy recycling process that efficiently transforms spent polycrystalline LiNi1/3Mn1/3Co1/3O2 (NMC111) into high-performance large-grained LiNi0.6Mn0.2Co0.2O2 (NMC622), offering dual benefits of compositional upcycling and morphology upgradation. Our approach leverages a polyol system with meticulous control over nickel salt addition and the precipitation process. This yields upcycled cathode materials possessing excellent structural integrity, well-defined large-grained particles (5-10?..mu..m), and robust electrochemical performance, including a specific capacity of ~182 mAh g-1 at C/10 and 88.0% capacity retention after 100 cycles. This facile and multifunctional process provides an environmental-friendly pathway for advanced cathode recycling, significantly contributing to a circular economy for LIBs through precise control over critical material attributes.

25 ENERGY STORAGE

Static and Dynamic Thermomechanical Properties of Phase-Separated Epoxy Networks with Tuned Microstructures

Here, polymerization-induced phase separation is a useful method for the construction of heterogeneous epoxy networks with properties exceeding their homogeneous counterparts. In this work, we examine the static and dynamic thermomechanical properties of phase-separated epoxy networks salient to their application as encapsulants. Three heterogeneous epoxy-amine networks with nano-, meso-, and macro-phase-separated morphologies comprised of hard and soft domains are compared to a rigid, unstructured network. The glass transition profiles of the heterogeneous networks are complex, spanning many decades in the frequency domain. The nanophase-separated morphology leads to higher coefficient of thermal expansion, yet surprisingly is characterized by reduced residual stress. Under both quasi-static and dynamic compression (strain rates of order 10 –3 and 10 3 s –1 , respectively), the nanophase-separated network also exhibits higher modulus and strength. In split-Hopkinson bar experiments, the energy dissipation characteristics of the epoxy networks were nearly identical. Curiously, however, the Hugoniot response of the macro-phase-separated network determined by ballistic shockwave analysis indicates a remarkable ability of this material to mitigate shockwave propagation in comparison to many homogeneous and heterogeneous polymer materials. Collectively, this work reveals several previously unreported phenomena with respect to structure–property relationships in phase-separated epoxy networks, illustrating the potential value of systematically tuned microstructures for optimization of application-specific physical properties.

36 MATERIALS SCIENCE

Confinement-Driven Segregation Enables Glassy Polymer Hybrid Materials Featuring Disordered Hyperuniformity and Integrated Self-Healing

The blending of glassy copolymer-brush modified colloids with a viscoelastic linear copolymer featuring intrinsic self-healing enabled disordered hyperuniform hybrid materials that combined mechanical robustness with structural color, processability, environmental stability, and the ability to recover structure and properties after incurring physical damage via ‘integrated self-healing’. Symmetric linear n-butyl acrylate/methyl methacrylate (BA/MMA) were co-assembled with asymmetric glassy BA/MMA statistical copolymer brush (silica) particles. ‘Confinement-driven segregation’ resulted in a microphase-separated morphology in which the linear copolymer resided within the interstitial regions of a rigid (∼1 GPa) copolymer brush particle template with disordered hyperuniform microstructure. Diffusion of the self-healing copolymer additive into damage regions drove the recovery after damage, along with the restoration of structural color due to the materials hyperuniform microstructure. The synergistic action of intrinsic and extrinsic healing mechanisms could provide a versatile platform for the bottom-up fabrication of multifunctional hybrid materials with increased damage resistance and functional longevity.

36 MATERIALS SCIENCE

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE