Computational Modeling of Microstructure Formation During Laser Powder Bed Fusion of Bulk Thermoelectric Materials
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The Part III of the four-part series of articles discusses the challenges and opportunities in microscale additive manufacturing processes, specifically focusing on energy-induced deposition and electrochemical processes. Compared to the direct ink write (DIW) and laser-based processes, the energy-induced deposition methods can fabricate high-resolution, high aspect ratio and complex parts, while the hybrid electrochemical process can be used to fabricate complex parts using a wide range of conductive and photoactive materials. However, the volumetric throughput of these processes is lower than their DIW and laser-based counterparts. The processes that have been explored in this process are Focused-ion Beam Induced Deposition (FIBID), Laser Chemical Vapor Deposition (LCVD), Menicus-confined Electrodeposition (MCED) and Laser-Enabled Electrochemical Printing (LECP). The range of processable materials, feature-size resolution, geometry and volumetric throughput are used as factors to evaluate the current state-of-the-art for these processes. Finally, novel approaches have been proposed in the article to address these challenges associated with microscale AM processes.
Abstract This paper evaluates the representation of turbulence and its effect on transport and dispersion within multiscale and microscale-only simulations in an urban environment. These simulations, run using the Weather Research and Forecasting Model with the addition of an immersed boundary method, predict transport and mixing during a controlled tracer release from the Joint Urban 2003 field campaign in Oklahoma City, Oklahoma. This work extends the results of a recent study through analysis of turbulence kinetic energy and turbulence spectra and their role in accurately simulating wind speed, direction, and tracer concentration. The significance and role of surface heat fluxes and use of the cell perturbation method in the numerical simulation setup are also examined. Our previous study detailed the model development necessary for our multiscale simulations, examined model skill at predicting wind speeds and tracer concentrations, and demonstrated that dynamic downscaling from mesoscale to microscale through a sequence of nested simulations can improve predictions of transport and dispersion relative to a microscale-only simulation forced by idealized meteorology. Here, predictions are compared with observations to assess qualitative agreement and statistical model skill at predicting wind speed, wind direction, tracer concentration, and turbulent kinetic energy at locations throughout the city. We also investigate the scale distribution of turbulence and the associated impact on model skill, particularly for predictions of transport and dispersion. Our results show that downscaled large-scale turbulence, which is unique to the multiscale simulations, significantly improves predictions of tracer concentrations in this complex urban environment. Significance Statement Simulations of atmospheric transport and mixing in urban environments have many applications, including pollution modeling for urban planning or informing emergency response following a hazardous release. These applications include phenomena with spatial scales spanning from millimeters to kilometers. Most simulations resolve flow only within the urban area of interest, omitting larger scales of turbulence and regional influences. This study examines a method that resolves both the small and large-scale flow features. We evaluate simulation accuracy by comparing predictions with observations from an experiment involving the release of a tracer gas in Oklahoma City, Oklahoma, with emphasis on correctly modeling turbulent fluctuations. Our results demonstrate the importance of resolving large-scale flow features when predicting transport and dispersion in urban environments.
This report describes improvements made to the solid mechanics formulation in the MOOSE open source finite element simulation environment and the open source Nuclear Engineering Material model Library (NEML) for mechanical constitutive models. The focus of these improvements is to improve the usability and performance of simulations involving one or both pieces of software. Specifically, this work completes a new system for solid mechanics simulations in the MOOSE ecosystem providing exact linearizations and optimal (quadratic) convergence, for a variety of coordinate systems and material types, including large deformation simulations. This work then provides users a framework to build highly efficient mechanical simulations of structures or materials or to couple in additional MOOSE physics modules to build complex, scalable multiphysics simulations.
The formation of C–N bonds—of great importance to the pharmaceutical industry—can be facilitated enzymatically using nucleophilic and nitrene transfer mechanisms. However, neither natural nor engineered enzymes are known to generate and control nitrogen-centred radicals, which serve as valuable species for C–N bond formation. Here we use flavin-dependent ‘ene’-reductases with an exogenous photoredox catalyst to selectively generate amidyl radicals within the protein active site. These enzymes are engineered through directed evolution to catalyse 5-exo, 6-endo, 7-endo, 8-endo, and intermolecular hydroamination reactions with high levels of enantioselectivity. Mechanistic studies suggest that radical initiation occurs via an enzyme-gated mechanism, where the protein thermodynamically activates the substrate for reduction by the photocatalyst. Molecular dynamics studies indicate that the enzymes bind substrates using non-canonical binding interactions, which may serve as a handle to further manipulate reactivity. Furthermore, this approach demonstrates the versatility of these enzymes for controlling the reactivity of high-energy radical intermediates and highlights the opportunity for synergistic catalyst strategies to unlock previously inaccessible enzymatic functions.
Defects are essential to engineering the properties of functional materials ranging from semiconductors and superconductors to ferroics. Whereas point defects have been widely exploited, dislocations are commonly viewed as problematic for functional materials and not as a microstructural tool. In this work, we developed a method for mechanically imprinting dislocation networks that favorably skew the domain structure in bulk ferroelectrics and thereby tame the large switching polarization and make it available for functional harvesting. The resulting microstructure yields a strong mechanical restoring force to revert electric field–induced domain wall displacement on the macroscopic level and high pinning force on the local level. This induces a giant increase of the dielectric and electromechanical response at intermediate electric fields in barium titanate [electric field–dependent permittivity (ε 33 ) ≈ 5800 and large-signal piezoelectric coefficient ( d 33 *) ≈ 1890 picometers/volt]. Dislocation-based anisotropy delivers a different suite of tools with which to tailor functional materials.
Over the past few decades, granular media is gaining attention as a viable option for heat transfer fluids (HTFs). Several research efforts are studying the use of particle-based heat transfer fluids in a wide variety of applications. With this motivation, the current work focusses on analyzing the different heat transfer mechanisms in low-temperature mono-sized densely packed granular media. To study the heat transfer behavior of granular media at different scales, the current work employs a two-way coupled computational strategy. The motion of particles is solved using the Discrete Element Method (DEM) and the interstitial air is solved using a Finite-Volume (CFD) approach. The Open-Source library CFDEM Coupling® is used in the current study to join the Finite Volume PISO solver of OpenFOAM® and the DEM solver of LIGGGHTS®. Typically, particle-particle contact conduction and particle-air convection are the most popular closure models. But recent research identified a different heat transfer phenomenon in packed beds that cannot be identified by conduction or convection models. While closure models were developed to implement this on a CFD-DEM framework, they did not capture the effect of intra-particulate thermal gradients on this phenomenon. Hence the current work also employs Particle-Resolved Direct Numerical Simulations (PR-DNS) to gain valuable insights allowing for the modification of existing models. A new closure model is then proposed here and is implemented in the CFD-DEM framework. This model provides key insights into the different heat transfer mechanism of packed beds.
The practical application of graphite anodes in lithium-ion batteries (LIBs) is constrained by low specific capacity (372 mAh g -1 ) and sluggish kinetics. Here, to address these limitations, our present study focuses on high-entropy oxides (HEOs), which offer high theoretical capacity and structural stability. We synthesized spinel-structured (AlMnCoNiZn) 3 O 4 nanoparticles via a solution combustion method and fabricated an (AlMnCoNiZn) 3 O 4 @polypyrrole (PPy) nanocomposite through in-situ polymerization. Our electrochemical tests demonstrate that the PPy modification significantly enhances performance. While the pristine (AlMnCoNiZn) 3 O 4 delivered 445 mAh g −1 after 100 cycles at 100 mA g −1 and 350 mAh g −1 after 1000 cycles at 1000 mA g −1 , the (AlMnCoNiZn) 3 O 4 @PPy composite achieved 695 mAh g −1 after 100 cycles and maintained 675 mAh g −1 after 1000 cycles. Furthermore, the composite improved rate capacity at 1000 mA g −1 from 211 mAh g −1 to 403 mAh g −1 . This work highlights how conductive and flexible polymer modifications can dramatically improve the electrochemical properties of HEOs. The developed (AlMnCoNiZn) 3 O 4 @PPy composite provides a promising direction for designing advanced anodes to meet next-generation energy storage demands.
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Glycosylation is a ubiquitous modification present across all of biology, affecting many things such as physicochemical properties, cellular recognition, subcellular localization, and immunogenicity. Nucleotide sugars are important precursors needed to study glycosylation and produce glycosylated products. Saccharomyces cerevisiae is a potentially powerful platform for producing glycosylated biomolecules, but it lacks nucleotide sugar diversity. Nucleotide sugar metabolism is complex, and understanding how to engineer it will be necessary to both access and study heterologous glycosylations found across biology. This review overviews the potential challenges with engineering nucleotide sugar metabolism in yeast from the salvage pathways that convert free sugars to their associated UDP-sugars to de novo synthesis where nucleotide sugars are interconverted through a complex metabolic network with governing feedback mechanisms. Finally, recent examples of engineering complex glycosylation of small molecules in S. cerevisiae are explored and assessed.
Efficiently simulating solid mechanics is vital across various engineering applications. As constitutive models grow more complex and simulations scale up in size, harnessing the capabilities of modern computer architectures has become essential for achieving timely results. This paper presents advancements in running parallel simulations of solid mechanics on multi-core CPUs and GPUs using a single-code implementation. This portability is made possible by the C++ matrix and array (MATAR) library, which interfaces with the C++ Kokkos library, enabling the selection of fine-grained parallelism backends (e.g., CUDA, HIP, OpenMP, pthreads, etc.) at compile time. MATAR simplifies the transition from Fortran to C++ and Kokkos, making it easier to modernize legacy solid mechanics codes. We applied this approach to modernize a suite of constitutive models and to demonstrate substantial performance improvements across different computer architectures. This paper includes comparative performance studies using multi-core CPUs along with AMD and NVIDIA GPUs. Results are presented using a hypoelastic–plastic model, a crystal plasticity model, and the viscoplastic self-consistent generalized material model (VPSC-GMM). The results underscore the potential of using the MATAR library and modern computer architectures to accelerate solid mechanics simulations.
The utility of gray cast iron in engine components remains tied to the mechanical performance and cost. Repair and remanufacturing of castings offer economical and sustainable benefits; however, high thermal input from traditional fusion-based welding is unable to restore the original mechanical quality owing to brittle microstructures and porosity formed in situ. Directed energy deposition (DED) is an additive manufacturing method that has received considerable interest for repairs owing to the highly controllable nature of the process. Despite this, few works have connected the effect of DED parameters on actual interfacial strength. Consequently, distinct DED parameter combinations were identified to maximize the strength and fatigue life of the repaired cast iron. Further, high speed melt pool imaging and residual stress measurements are provided to aid in the understanding of the metallurgical quality and strength seen in these structures. In general, higher scanning speeds and lower thermal gradients promoted comparable tensile strength to that of the original gray cast iron. The results presented here provide a foundation to tune in the DED process to generate the required mechanical quality as a starting point for future process advancements.
X-ray absorption spectroscopy (XAS) and the directly linked X-ray reflectivity near absorption edges yield a wealth of specific information on the electronic structure around the resonantly addressed element. Observing the dynamic response of complex materials to optical excitations in pump–probe experiments requires high sensitivity to small changes in the spectra which in turn necessitates the brilliance of free electron laser (FEL) pulses. However, due to the fluctuating spectral content of pulses generated by self-amplified spontaneous emission (SASE), FEL experiments often struggle to reach the full sensitivity and time-resolution that FELs can in principle enable. Here, we implement a setup which solves two common challenges in this type of spectroscopy using FELs: First, we achieve a high spectral resolution by using a spectrometer downstream of the sample instead of a monochromator upstream of the sample. Thus, the full FEL bandwidth contributes to the measurement at the same time, and the FEL pulse duration is not elongated by a monochromator. Second, the FEL beam is divided into identical copies by a transmission grating beam splitter so that two spectra from separate spots on the sample (or from the sample and known reference) can be recorded in-parallel with the same spectrometer, enabling a spectrally resolved intensity normalization of pulse fluctuations in pump–probe scenarios. We analyze the capabilities of this setup around the oxygen K- and nickel L-edges recorded with third harmonic radiation of the free electron laser in Hamburg (FLASH), demonstrating the capability for pump–probe measurements with sensitivity to reflectivity changes on the per mill level.
The use of magnetic nanoparticles in the treatment of cancer using alternating current hyperthermia therapy has shown the potential to replace or supplement conventional cancer treatments, radiotherapy and chemotherapy, which have severe side effects. Though the nearly spherical sub-10 nm iron oxide nanoparticles have their approval from the US Food and Drug Administration, their low heating efficiency and removal from the body after hyperthermia treatment raises serious concerns. The majority of magnetic hyperthermia research is working to create nanomaterials with improved heating efficiency and long blood circulation time. Here, we have demonstrated a simple strategy to enhance the heating efficiency of sub-10 nm Fe3O4 nanoparticles through the replacement of Fe+2 ions with Co+2 ions. Magnetic and hyperthermia experiments on the 7 nm Fe3−xCoxO4 (x = 0–1) nanoparticles showed that the blocking temperature, the coercivity at 10 K, and the specific absorption rate followed a similar trend with a maximum at x = 0.75, which is in corroboration with the theoretical prediction. Our study revealed that the heating efficiency of the Fe3−xCoxO4 (x = 0–1) nanoparticles varies not just with the size and saturation magnetization but also with the magnetocrystalline anisotropy of the particles.
One of the most important aspects that need to be addressed to increase solar energy penetration is the power ramp-rate control. In weak grids such as the one found in Puerto Rico, it is important to smooth power fluctuations caused by the intermittence of passing clouds. In this work, a novel power ramp-rate control strategy is proposed. Additionally, a comparison with some of the most common power ramp-rate control methods is performed using a proposed model and real solar radiation data from the Coto Laurel photovoltaic power plant located in Ponce, Puerto Rico. The proposed model was validated using one-year real data from Coto Laurel. The power ramp-rate control methods were compared in real-time simulations using the OP5700 from Opal-RT Technologies considering power ramp rate fluctuations, power ramp-rate violations, fluctuations in the state-of-charge, among other indicators. Moreover, the proposed power ramp-rate control strategy, called predictive dynamic smoothing was explained and compared. Results indicate that the predictive dynamic smoothing produced a considerably reduced Levelized Cost of Storage compared to other power ramp-rate control methods and provided a higher lifetime expectancy for lithium batteries.
The development of biomaterials capable of supporting complex tissue growth remains a central challenge in regenerative medicine and tissue engineering, particularly in replicating the structural, mechanical, and transport functions of native extracellular matrices. While decellularized animal tissues have demonstrated significant success as scaffolds for tissue engineering, they are still constrained by cost, immunogenicity, and ethical concerns. In recent years, decellularized plant tissues have emerged as a compelling alternative scaffold platform due to their inherent vascular architectures, ethical sourcing, tunable mechanical properties, cytocompatibility, and sustainability. This review summarizes current strategies for the decellularization of plant tissues, including chemical, enzymatic, and physical approaches, and discusses how these methods preserve plant cell wall structure while removing immunogenic components. Advances in surface loading and functionalization, including protein coatings, oxidation, nanoparticle incorporation, peptide conjugation, and bioactive molecule loading, have further enhanced cell adhesion, differentiation, biodegradability, and immunomodulation. Recent applications of decellularized plant scaffolds in cardiac, skeletal muscle, bone, nerve, and wound healing contexts are reviewed, highlighting proof-of-concept successes and remaining challenges. Beyond therapeutic applications, plant-derived scaffolds have also enabled physiologically relevant in vitro models for vascular biology, mechanotransduction, cancer, metabolic tissues, and drug response studies. Collectively, these advances position decellularized plant tissues as versatile, low-cost, and ethically favorable biomaterials with growing relevance for both regenerative medicine and tissue modeling.
Engineered T cell therapies such as chimeric antigen receptor (CAR) expressing T cells (CAR-T cells) have great potential to treat many human diseases; however, inflammatory toxicities associated with these therapies present safety risks and can greatly limit its widespread use. This article briefly reviews our current understanding of mechanisms for inflammatory toxicities during CAR T-cell therapy, current strategies for management and mitigation of these risks and highlights key areas of knowledge gap for future research..
ABSTRACT With only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.