Anisotropic thermal transport was measured in imine-linked two-dimensional polymer (2DP) films that were prepared by interfacial polymerization. Measurements of both in-plane (k∥) and cross-plane (k⊥) thermal conductivities relied on preparing free-standing 2DP films that were readily transferred for different measurement configurations. We polymerized two 2DP (Per-PDA and TAPPy-PDA) films at a liquid-liquid interface. These polycrystalline, imine-linked 2DP films are 100-200 nm in thickness and were measured by frequency domain thermoreflectance to extract k⊥ and a suspended calorimetric platform technique to evaluate k∥. We find that k∥ is larger than k⊥ in both materials at room temperature, leading to anisotropy ratios (k∥/k⊥) as high as 2.3. We attribute this behavior to the fact that the stiff, in-plane covalent bonds of 2DPs transport heat more effectively than the flexible, supramolecular cross-plane interactions. Variable-temperature measurements revealed a positive correlation between temperature and thermal conductivity, which we attribute to phonon scattering from grain boundaries and defects in the polycrystalline 2DP films. Molecular dynamics simulations of pristine crystals predict larger thermal conductivities and anisotropy ratios exceeding 7. The simulations suggest that as higher quality 2DP films become available, higher thermal conductivities and anisotropy ratios will also manifest.

Cemented carbides such as cemented tungsten carbide (WC) are known for their use in resilient wear-resistant applications where hardness and thermal stability are imperative. They are composed of carbide particles embedded in a metal binder. Laser Powder Bed Fusion (L-PBF) is a favorable method to form cemented carbides into complex geometries, but composites pose unique challenges relative to metals typically processed by L-PBF. Resolving the melt pool temperature distributions in L-PBF is key to understanding the underlying physics of the fusion process. Using a two-color thermal imaging method, melt pool thermal maps of WC0.83-Ni0.17 were captured with linear energy densities ranging from 500-1750 J/m with and without powder. WC0.83-Ni0.17 melt pools exhibit temperatures above 4000 K, which can lead to the generation of other WC phases. Compared to more common L-PBF materials such as 316L stainless steel (SS), WC0.83-Ni0.17 melt pools reach higher temperatures. Our direct measurements find that the thermal conductivity of WC0.83-Ni0.17 is 30 W/m-K at 300 K, which is higher than the thermal conductivity of 316L SS and suggests that other heat transfer limitations must cause the elevated melt pool temperatures. A FLOW-3D CFD model based on the composite properties was compared to both the melt pool centerline temperatures and width measurements of the samples fabricated by L-PBF. The simulations indicate that specifying the onset of fluidity is key to reproducing the high temperatures observed experimentally. Although Ni has a melting point of 1728 K, the simulations do not match experiments unless the onset of fluidity is set at the melting point of WC (3143 K). Within FLOW-3D, the onset of fluidity is controlled by the critical solid fraction, which is a uniquely important parameter for simulating composite materials.

The thermal conductivities of (100) γ-Ga2O3 films deposited on (100) MgAl2O4 substrates with various thicknesses were measured using frequency-domain thermoreflectance. The measured thermal conductivities of γ-Ga2O3 films are lower than the thermal conductivities of (2¯ 01) β-Ga2O3 films of comparable thickness, which suggests that γ-phase inclusions in the doped or alloyed β-phase may affect its thermal conductivity. The thermal conductivity of γ-Ga2O3 increases from 2.3−0.5+0.9 to 3.5±0.7 W/m K for films with thicknesses of 75–404 nm, which demonstrates a prominent size effect on thermal conductivity. The thermal conductivity of γ-Ga2O3 also shows a slight increase as temperature increases from 293 to 400 K. This increase in thermal conductivity occurs when defect and boundary scattering suppress signatures of temperature-dependent Umklapp scattering. γ-Ga2O3 has a cation-defective spinel structure with at least two gallium vacancies in every unit cell, which are the likely source of defect scattering.

Laser powder bed fusion offers a high degree of geometric freedom for manufacturing with novel materials, yet failures during fabrication remain a critical barrier to achieving more complex components. Recoater blade collisions, cracking, and build plate delamination damage parts and performance, especially with hard and high-temperature materials. Cemented carbides are optimal for high-hardness machining and tooling parts, but high thermal gradients and complex composite behaviors exacerbate fabrication issues. Understanding the effects of build process parameters on macroscopic failure modes is critical to mitigate such issues. This study leverages thermomechanical modeling to investigate the effects of process parameter alterations on build stresses and deflection for WC-Ni part fabrication strategies. A comparative analysis revealed that reductions in laser energy density and part sizes reduced part deflection. The simulations reinforced that bed preheating reduced stresses and thermal gradients, but reductions in interlayer timing also benefited builds by adding additional interlayer heating. Sharp geometric features common in cemented carbide machining and tooling parts significantly increased deflection. Exploratory builds achieved high-density (> 97

In this work, high-speed imaging of experiments was conducted with varied processing conditions and materials to parametrically understand the plume’s severity, size, and trajectory on a commercial laser powder bed fusion (LPBF) machine. In this context, the plume refers to the bright melt-pool-scale metal vapor and condensate. A U-Net convolutional neural network (CNN) was trained to segment the plume from experimental images. A comparison of the plume generated from different metals shows that Ti-6Al-4V has a significantly brighter and larger plume than Inconel 718 and 316L SS, which may be attributed to increased emission from the melt pool, increased scattering of the emission from the melt pool, or elemental emission lines. The effect of powder was studied for both Ti-6Al-4V and 316L SS and shows a reduction in the size and brightness of the plume in most overhead images, suggesting that the plume visibility and/or the plume itself is suppressed by the powder and spatter. Process mapping the plume in power and scanning velocity space shows that the plume transitions from ejecting toward the rear of the melt pool in the transitional regime to ejecting directly above the melt pool in both the severe keyholing and conduction-dominated regimes, consistent with the vapor depression geometry under the laser. The temporal variability of the plume increases with increasing power-to-velocity ratio, which is attributed to melt pool and vapor depression instability, but can also be large at very low power-to-velocity ratios. This experimental study aims to increase our understanding of the plume’s behavior at the melt pool scale in LPBF and can be used to validate multi-physics models of the plume and inform parameter selection for both minimal melt-pool-scale laser-plume interaction and avoiding plume interference in melt pool imaging.

Previous work on additively-manufactured oxide dispersion strengthened alloys focused on experimental approaches, resulting in larger dispersoid sizes and lower number densities than can be achieved with conventional powder metallurgy. To improve the as-fabricated microstructure, this work integrates experiments with a thermodynamic and kinetic modeling framework to probe the limits of the dispersoid sizes and number densities that can be achieved with powder bed fusion-laser beam. Bulk samples of a Ni–20Cr ＋ 1 wt% Y2O3 alloy are fabricated using a range of laser power and scanning velocity combinations. Scanning transmission electron microscopy characterization is performed to quantify the dispersoid size distributions across the processing space. The smallest mean dispersoid diameter (29 nm) is observed at 300 W and 1200 mm/s, with a number density of 1.0 × 1020 m−3. The largest mean diameter (72 nm) is observed at 200 W and 200 mm/s, with a number density of 1.5 × 1019 m−3. Scanning electron microscopy suggests that a considerable fraction of the oxide added to the feedstock is lost during processing, due to oxide agglomeration and the ejection of oxide-rich spatter from the melt pool. After accounting for these losses, the model predictions for the dispersoid diameter and number density align with the experimental trends. The results suggest that the mechanism that limits the final number density is collision coarsening of dispersoids in the melt pool. The modeling framework is leveraged to propose processing strategies to limit dispersoid size and increase number density.

Anomalous melt pools during metal additive manufacturing (AM) can lead to deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. We employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. Specifically, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental no-powder melt pool observations. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical models. Additionally, the use of ratiometric temperature estimates improves the accuracy of the model predictions compared to monochromatic imaging. This work establishes a framework extensible towards powder-based AM builds.

Here, we describe the design features that lead to intrinsically thermally conductive polymers. Though polymers are conventionally assumed to be thermal insulators (<0.3 W m(-1) K-1), significant efforts by the thermal transport community have shown that polymers can be intrinsically thermally conductive (>1.0 W m(-1) K-1). However, these findings have not yet driven comprehensive synthetic efforts to expose how different macromolecular features impact thermal conductivity. Preliminary theoretical and experimental investigations have revealed that high k polymers can be realized by enhancing the alignment, crystallinity, and intermolecular interactions. While a holistic mechanistic framework does not yet exist for thermal transport in polymeric materials, contemporary literature suggests that phonon-like heat carriers may be operative in macromolecules that meet the abovementioned criteria. In this review, we offer a perspective on how high thermal conductivity polymers can be systematically engineered from this understanding. Reports for several classes of macromolecules, including linear polymers, network polymers, liquid-crystalline polymers, and two-dimensional polymers substantiate the design principles we propose. Throughout this work, we offer opportunities for continued fundamental and technological development of polymers with high thermal conductivity.

Energy assessment of buildings is important to many applications including energy labeling and building retrofit strategies. However, thermal transmittance, which is key to energy assessment, poses a challenge for in-situ evaluation. To address this, we present an exploration of theoretical and experimental data correlating thermal conductivity and dielectric properties of multilayered assemblies consisting of wood-frame construction materials. We experimentally found an R2 score of 0.88 between thermal conductivities and dielectric constants for 26 small-sized assemblies. We predicted their thermal conductivity based on dielectric properties and obtained an R2 score of 0.95 between experimental and predicted results.

Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool. For instance, the melt pool can directly contribute to the formation of undesirable porosity, residual stress, and surface roughness in the final part. Experimental in-situ monitoring of the three-dimensional melt pool physical fields is challenging, due to the short length and time scales involved in the process. Multi-physics simulation methods can describe the three-dimensional dynamics of the melt pool, but are computationally expensive at the mesh refinement required for accurate predictions of complex effects, such as the formation of keyhole porosity. Therefore, in this work, we develop a generative deep learning model based on the probabilistic diffusion framework to map low-fidelity, coarse-grained simulation information to the high-fidelity counterpart. By doing so, we bypass the computational expense of conducting multiple high-fidelity simulations for analysis by instead upscaling lightweight coarse mesh simulations. Specifically, we implement a 2-D diffusion model to spatially upscale cross-sections of the coarsely simulated melt pool to their high-fidelity equivalent. We demonstrate the preservation of key metrics of the melting process between the ground truth simulation data and the diffusion model output, such as the temperature field, the melt pool dimensions and the variability of the keyhole vapor cavity. Specifically, we predict the melt pool depth within 3 $\mu m$ based on low-fidelity input data 4$\times$ coarser than the high-fidelity simulations, reducing analysis time by two orders of magnitude.

Modern microprocessor performance is limited by local hot spots induced at high frequency by busy integrated circuit elements such as the clock generator. Locally embedded thermoelectric devices (TEDs) are proposed to perform active cooling whereby thermoelectric effects enhance passive cooling by the Fourier law in removing heat from the hot spot to colder regions. To mitigate transient heating events and improve temperature stability, we propose a novel analytical solution that describes the temperature response of a periodically heated hot spot that is actively cooled by a TED driven electrically at the same frequency. The analytical solution that we present is validated by experimental data from frequency domain thermal reflectance (FDTR) measurements made directly on an actively cooled Si thermoelectric device where the pump laser replicates the transient hot spot. We herein demonstrate a practical method to actively cancel the transient temperature variations on circuit elements with TEDs. This result opens a new path to optimize the design of cooling systems for transient localized hot spots in integrated circuits. The authors model the transient temperature response of micro-thermoelectric devices with AC current applied, which is locally integrated under a transient hot spot. They develop a strategy for actively canceling the transient temperature variations.

Powder-based additive manufacturing has transformed the manufacturing industryover the last decade. In Laser Powder Bed Fusion, a specific part is built in aniterative manner in which two-dimensional cross-sections are formed on top of eachother by melting and fusing the proper areas of the powder bed. In this process, thebehavior of the melt pool and its thermal field has a very important role in predictingthe quality of the manufactured part and its possible defects. However, the simulationof such a complex phenomenon is usually very time-consuming and requires hugecomputational resources. Flow-3D is one of the software packages capable of executingsuch simulations using iterative numerical solvers. In this work, we create threedatasets of single-trail processes using Flow-3D and use them to train a convolutionalneural network capable of predicting the behavior of the three-dimensional thermalfield of the melt pool solely by taking three parameters as input: laser power, laservelocity, and time step. The CNN achieves a relative Root Mean Squared Error of2% to 3% for the temperature field and an average Intersection over Union score of80% to 90% in predicting the melt pool area. Moreover, since time is included asone of the inputs of the model, the thermal field can be instantly obtained for anyarbitrary time step without the need to iterate and compute all the steps.

Infrared-pump, electronic-probe (IPEP) spectroscopy is used to measure heat flow into and out of CdSe nanocrystals suspended in an organic solvent, where the surface ligands are initially excited with an infrared pump pulse. Subsequently, the heat is transferred from the excited ligands to the nanocrystals and in parallel to the solvent. Parallel heat transfer in opposite directions uniquely enables us to differentiate the thermal conductances at the nanocrystal/ligand and ligand/solvent interfaces. Using a novel solution to the heat diffusion equation, we fit the IPEP data to find that the nanocrystal/ligand conductances range from 88 to 135 MW m(-2) K-1 and are approximately 1 order of magnitude higher than the ligand/solvent conductances, which range from 7 to 26 MW m(-2) K-1. Transient nonequilibrium molecular dynamics (MD) simulations of nanocrystal suspensions agree with IPEP data and show that ligands bound to the nanocrystal by bidentate bonds have more than twice the per-ligand conductance as those bound by monodentate bonds.

We introduce an experimental method to image melt pool temperature with a single commercial color camera and compare the results with multi-physics computational fluid dynamic (CFD) models. This approach leverages the principle of two-color (i.e., ratiometric) thermal imaging, which is advantageous because it negates the need for a priori knowledge of melt pool emissivity, plume transmissivity, and the camera's view factor. The color camera's ability to accurately measure temperature was validated with a National Institute of Standards and Technology (NIST) blackbody source and tungsten filament lamp between temperatures of 1600 K and 2800 K. To demonstrate the technique, an off-axis high-speed color camera operating at 22,500 frames per second capturing a 2.8 mm × 2.8 mm area on the build plate was used to image both no-powder and powder single beads on a commercial laser powder bed fusion machine. Melt pool temperature fields for 316L stainless steel at varying processing conditions show peaks between 3300 K and 3700 K depending on the laser power and increased variability in the presence of powder. Measurements of nickel superalloy 718 and Ti-6Al-4V show comparable temperatures, with increased plume obstruction, especially in Ti-6Al-4V due to vaporization of aluminum. Multi-physics CFD models are used to simulate metal melt pools but some parameters such as the accommodation and Fresnel coefficients are not well characterized. Fitting a FLOW-3D® CFD model to ex-situ measurements of the melt pool cross-sectional geometry for 316L stainless steel identifies multiple combinations of Fresnel coefficient and accommodation coefficient that lead to geometric agreement. Only two of these combinations show agreement with the thermal images, motivating the need for thermal imaging as a means to advance validation of complex physics models. Our methodology can be applied to any color camera to better monitor and understand melt pools that yield high-quality parts.

This work demonstrates an experimental technique to image melt pool temperature fields with a commercial color camera for laser-directed energy deposition (L-DED) processes. The technique relies on two-color thermography to construct spatially-resolved temperature fields and is demonstrated on 316L stainless steel (SS) melt pools from solidus to near-boiling temperatures. This two-color thermal imaging system negates the need for a priori knowledge of melt pool emissivity or the camera’s view factor and was validated with a calibrated tungsten filament lamp between temperatures of 1220 K and 2850 K. In-situ temperature measurements are combined with ex-situ cross-sectional geometry to determine the material’s effective absorptivity and coefficient of temperature-dependent surface tension, which is responsible for Marangoni convection, in a multi-physics computational fluid dynamics (CFD) model. Measured peak melt pool temperatures are important for understanding the molten convection within the melt pool, and measured cooling rates can be related to the resulting part microstructure. Peak temperatures from 1750 K to 3000 K show that below the boiling point, temperature increases with increasing laser power density and decreases with increasing scanning velocity. Thermal images are used to estimate cooling rates in the melt pool tail, which increase with the ratio of scanning velocity to power. Our thermal imaging strategy will advance measurement science in L-DED processes by validating multi-physics CFD models, quantifying cooling rates, providing real-time feedback control, and allowing process mapping of critical melt pool behaviors.

The impact of rapid thermal annealing (1000 °C for 1 min) on the thermal transport properties of amorphous alumina (a-Al2O3) thin films grown by atomic layer deposition on β−Ga2O3 and amorphous silica (a-SiO2) substrates is determined using frequency-domain thermoreflectance measurements. The annealing more than doubles the a-Al2O3 thermal conductivity for both substrates (1.54 ± 0.13 to 3.14 ± 0.27 W m−1 K−1 for β−Ga2O3 and 1.60 ± 0.14 to 3.87 ± 0.33 W m−1 K−1 for a-SiO2) while keeping the film amorphous. The thermal conductivity increase is attributed to partial recrystallization and off-gassing of embedded impurities. Annealing halves the thermal boundary resistance of the a-Al2O3/a-SiO2 interface (10.5 ± 1.0 to 4.47 ± 0.42 m2 K GW−1), which is attributed to compositional mixing and structural reorganization that are enabled by the elastic matching of these two materials. The thermal boundary resistance of the a-Al2O3/β−Ga2O3 interface is not affected by annealing due to the elastic mismatch. Reducing the thermal resistance of a-Al2O3 dielectric films and adjacent interfaces by annealing will promote lateral heat spreading adjacent to hot spots and improve device longevity.

Powder bed fusion is a method of additive manufacturing (AM) where parts are constructed by iteratively melting metal cross-sections to build complex 3D structures. Defects often form during the printing process, where the dynamics of the melt pool can directly contribute to the formation of porous defects in the final part. For instance, insufficient overlap of the produced melt pools can result in unmelted regions of powder, while deep, unstable vapor depression cavities can lead to spherical voids becoming trapped in the substrate. Therefore, in situ of monitoring the melt pool during the melting process can telegraph the formation of defects and assist the creation of fully dense parts. Here, we augment data-driven-based monitoring techniques to enable the 3D visualization of the melt pool underneath the surface, based on the melt pool surface temperature and processing parameters. Specifically, a convolutional neural network (CNN) predicts the topography of the melt pool and keyhole cavity, based on the surface temperature data near the laser focal point and the nominal operating conditions. The data for the laser powder bed fusion process used to train the model is produced by full-field simulations of the meso-scale melting process, with the CFD software FLOW-3D. Data augmentation techniques are implemented to ensure generalizable performance in cases where the temperature data may be obscured and to ensure sharp, accurate predictions of the melt pool boundaries.

Sintering theory predicts no long-range mass transport or distortion for uniformly heated particles during particle coalescence. However, in sintering-based manufacturing processes, permanent part distortion is often observed. The driving forces and mechanisms leading to this phenomenon are not understood, and efforts to reduce distortion are largely limited to a trial-and-error approach. In this paper, we demonstrate that distortion during sintering results from mass-transport driven by nonhomogeneous temperature distribution. We then show that hitherto unknown mass transport mechanisms, working in the direction opposite to temperature gradient are the likely cause of distortion. The experimental setup, designed for this purpose, enables the quantification of distortion during sintering. Two possible mass transport mechanisms are defined, and the continuum model applicable to both is formulated. The model accurately predicts the transient and permanent distortion observed during experiments, including their size dependence. Methods to control distortion that can give rise to 4D printing are discussed.

Colloidal nanocrystal (NC) assemblies are promising for optoelectronic, photovoltaic, and thermoelectric applications. However, using these materials can be challenging in actual devices because they have a limited range of thermal conductivity and elastic modulus, which results in heat dissipation and mechanical robustness challenges. Here, we report thermal transport and mechanical measurements on single-domain colloidal PbS nanocrystal superlattices (NCSLs) that have long-range order as well as measurements on nanocrystal films (NCFs) that are comparatively disordered. Over an NC diameter range of 3.0-6.1 nm, we observe that NCSLs have thermal conductivities and Young's moduli that are up to ∼3 times higher than those of the corresponding NCFs. We also find that these properties are more sensitive to NC diameter in NCSLs relative to NCFs. Our measurements and computational modeling indicate that stronger ligand-ligand interactions due to enhanced ligand interdigitation and alignment in NCSLs account for the improved thermal transport and mechanical properties.