Anisotropic Thermal Conductivity Enhancement of the Aligned Metal-Organic Framework under Water Vapor Adsorption.

Metal-organic frameworks (MOFs) exhibit high adsorption and catalytic activities for various gas species. Because gas adsorption can cause a temperature increase in the MOF, which decreases the capacity and adsorption rate, a strict evaluation of its effect on the thermal conductivity of MOFs is essential. In this study, the thermal conductivity measurement of the MOF under water vapor adsorption was performed using an oriented film of copper tetrakis(4-carboxyphenyl)porphyrin (Cu-TCPP) MOF. A recently developed bidirectional 3ω method enabled the anisotropic thermal conductivity measurement of layered Cu-TCPP while maintaining its ordered structure. The water adsorption was found to increase the thermal conductivity in both in-plane and cross-plane directions with different trends and magnitudes, owing to the structural anisotropy. Molecular dynamics simulations suggest that additional vibrational modes provided by the adsorbed water molecules were the reason for the thermal conductivity enhancement.

Thermoelectric Power of a Single van der Waals Interface between Carbon Nanotubes.

Control of van der Waals interfaces is crucial for fabrication of nanomaterial-based high-performance thermoelectric devices because such interfaces significantly affect the overall thermoelectric performances of the device due to their relatively high thermal resistance. Such interfaces could induce different thermoelectric power from the bulk, i.e., interfacial thermoelectric power. However, from a macroscopic point of view, a correct evaluation of the interfacial thermoelectric power is difficult owing to various interface configurations. Therefore, the study of the thermoelectric properties at a single interface is crucial to address this problem. Herein, we used in situ transmission electron microscopy and nanomanipulation to investigate the thermoelectric properties of carbon nanotubes and their interfaces. The thermoelectric power of the bridged carbon nanotubes was individually measured. The existence of the interfacial thermoelectric power was determined by systematically changing the contact size between the two parallel nanotubes. The effect of interfacial thermoelectric power was qualitatively supported by Green's function calculations. When the contact length between two parallel nanotubes was less than approximately 100 nm, the experimental results and theoretical calculations indicated that the interface significantly contributed to the total thermoelectric power. However, when the contact length was longer than approximately 200 nm, the total thermoelectric power converged to the value of a single nanotube. The findings herein provide a basis for investigating thermoelectric devices with controlled van der Waals interfaces and contribute to thermal management in nanoscale devices and electronics.

General deep learning framework for emissivity engineering.

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

Ultrahigh-efficient material informatics inverse design of thermal metamaterials for visible-infrared-compatible camouflage.

Multispectral camouflage technologies, especially in the most frequently-used visible and infrared (VIS-IR) bands, are in increasing demand for the ever-growing multispectral detection technologies. Nevertheless, the efficient design of proper materials and structures for VIS-IR camouflage is still challenging because of the stringent requirement for selective spectra in a large VIS-IR wavelength range and the increasing demand for flexible color and infrared signal adaptivity. Here, a material-informatics-based inverse design framework is proposed to efficiently design multilayer germanium (Ge) and zinc sulfide (ZnS) metamaterials by evaluating only ~1% of the total candidates. The designed metamaterials exhibit excellent color matching and infrared camouflage performance from different observation angles and temperatures through both simulations and infrared experiments. The present material informatics inverse design framework is highly efficient and can be applied to other multi-objective optimization problems beyond multispectral camouflage.

Quantitative Description of Bubble Formation in Response to Electrolyte Engineering.

The green hydrogen economy is expected to play a crucial role in carbon neutrality, but industrial-scale water electrolysis requires improvements in efficiency, operation costs, and capital costs before broad deployment. Electrolysis operates at a high current density and involves the substantial formation of gaseous products from the electrode surfaces to the electrolyte, which may lead to additional resistance and a resulting loss of efficiency. A detailed clarification of the bubble departure phenomena against the electrode surface and the surrounding electrolytes is needed to further control bubbles in a water electrolyzer. This study clarifies how electrolyte properties affect the measured bubble detachment sizes from the comparisons with analytical expressions and dynamic simulations. Bubble behavior in various electrolyte solutions and operating conditions was described using various physical parameters. A quantitative relationship was then established to connect electrolyte properties and bubble departure diameters, which can help regulate the bubble management through electrolyte engineering.

Module-Level Polaritonic Thermophotovoltaic Emitters via Hierarchical Sequential Learning.

Thermophotovoltaic (TPV) generators provide continuous and high-efficiency power output by utilizing local thermal emitters to convert energy from various sources to thermal radiation matching the bandgaps of photovoltaic cells. Lack of effective guidelines for thermal emission control at high temperatures, poor thermal stability, and limited fabrication scalability are the three key challenges for the practical deployment of TPV devices. Here we develop a hierarchical sequential-learning optimization framework and experimentally realize a 6″ module-scale polaritonic thermal emitter with bandwidth-controlled thermal emission as well as excellent thermal stability at 1473 K. The 300 nm bandwidth thermal emission is realized by a complex photon polariton based on the superposition of Tamm plasmon polariton and surface plasmon polariton. We experimentally achieve a spectral efficiency of 65.6% (wavelength range of 0.4-8 µm) with statistical deviation less than 4% over the 6″ emitter, demonstrating industrial-level reliability for module-scale TPV applications.

Enhanced High Thermal Conductivity Cellulose Filaments via Hydrodynamic Focusing.

Nanocellulose is regarded as a green and renewable nanomaterial that has attracted increased attention. In this study, we demonstrate that nanocellulose materials can exhibit high thermal conductivity when their nanofibrils are highly aligned and bonded in the form of filaments. The thermal conductivity of individual filaments, consisting of highly aligned cellulose nanofibrils, fabricated by the flow-focusing method is measured in dried condition using a T-type measurement technique. The maximum thermal conductivity of the nanocellulose filaments obtained is 14.5 W/m-K, which is approximately five times higher than those of cellulose nanopaper and cellulose nanocrystals. Structural investigations suggest that the crystallinity of the filament remarkably influence their thermal conductivity. Smaller diameter filaments with higher crystallinity, that is, more internanofibril hydrogen bonds and less intrananofibril disorder, tend to have higher thermal conductivity. Temperature-dependence measurements also reveal that the filaments exhibit phonon transport at effective dimension between 2D and 3D.

Biomimetic reconstruction of butterfly wing scale nanostructures for radiative cooling and structural coloration.

A great number of butterfly species in the warmer climate have evolved to exhibit fascinating optical properties on their wing scales which can both regulate the wing temperature and exhibit structural coloring in order to increase their chances of survival. In particular, the Archaeoprepona demophon dorsal wing demonstrates notable radiative cooling performance and iridescent colors based on the nanostructure of the wing scale that can be characterized by the nanoporous matrix with the periodic nanograting structure on the top matrix surface. Inspired by the natural species, we demonstrate a multifunctional biomimetic film that reconstructs the nanostructure of the Archaeoprepona demophon wing scales to replicate the radiative cooling and structural coloring functionalities. We resorted to the SiO2 sacrificial template-based solution process to mimic the random porous structure and laser-interference lithography to reproduce the nanograting architecture of the butterfly wing scale. As a result, the biomimetic structure of the nanograted surface on top of the porous film demonstrated desirable heat transfer and optical properties for outstanding radiative cooling performance and iridescent structural coloring. In this regard, the film is capable of inducing the maximum temperature drop of 8.45 °C, and the color gamut of the biomimetic film can cover 91.8% of the standardized color profile (sRGB).

Ultrafast water permeation through nanochannels with a densely fluorous interior surface.

Ultrafast water permeation in aquaporins is promoted by their hydrophobic interior surface. Polytetrafluoroethylene has a dense fluorine surface, leading to its strong water repellence. We report a series of fluorous oligoamide nanorings with interior diameters ranging from 0.9 to 1.9 nanometers. These nanorings undergo supramolecular polymerization in phospholipid bilayer membranes to form fluorous nanochannels, the interior walls of which are densely covered with fluorine atoms. The nanochannel with the smallest diameter exhibits a water permeation flux that is two orders of magnitude greater than those of aquaporins and carbon nanotubes. The proposed nanochannel exhibits negligible chloride ion (Cl-) permeability caused by a powerful electrostatic barrier provided by the electrostatically negative fluorous interior surface. Thus, this nanochannel is expected to show nearly perfect salt reflectance for desalination.

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach.

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

Vibration sorting of small droplets on hydrophilic surface by asymmetric contact-line friction.

Droplet spreading and transport phenomenon is ubiquitous and has been studied by engineered surfaces with a variety of topographic features. To obtain a directional bias in dynamic wetting, hydrophobic surfaces with a geometrical asymmetry are generally used, attributing the directionality to one-sided pinning. Although the pinning may be useful for directional wetting, it usually limits the droplet mobility, especially for small volumes and over wettable surfaces. Here, we demonstrate a pinning-less approach to rapidly transport millimeter sized droplets on a partially wetting surface. Placing droplets on an asymmetrically structured surfaces with micron-scale roughness and applying symmetric horizontal vibration, they travel rapidly in one direction without pinning. The key, here, is to generate capillary-driven rapid contact-line motion within the time-scale of period of vibration. At the right regime where a friction factor local at the contact line dominates the rapid capillary motion, the asymmetric surface geometry can induce smooth and continuous contact-line movement back and forth at different speed, realizing directional motion of droplets even with small volumes over the wettable surface. We found that the translational speed is selective and strongly dependent on the droplet volume, oscillation frequency, and surface pattern properties, and thus droplets with a specific volume can be efficiently sorted out.

Electronic transport descriptors for the rapid screening of thermoelectric materials.

The discovery of novel materials for thermoelectric energy conversion has potential to be accelerated by data-driven screening combined with high-throughput calculations. One way to increase the efficacy of successfully choosing a candidate material is through its evaluation using transport descriptors. Using a data-driven screening, we selected 12 potential candidates in the trigonal ABX2 family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Using these data, we further derive ground-state transport descriptors for the carrier mobility and the thermoelectric powerfactor. In addition to low carrier mass, high dielectric constant was found to be an important factor towards high carrier mobility. A quadratic correlation between dielectric constant and transport performance was established and further validated with literature. Looking ahead, dielectric constant can potentially be exploited as an independent criterion towards improved thermoelectric performance. Combined with calculations of thermal conductivity including Peierls and inter-branch coherent contributions, we conclude that the trigonal ABX2 family has potential as high performance thermoelectrics in the intermediate temperature range for low grade waste heat harvesting.

Modulation of Interfacial Thermal Transport between Fumed Silica Nanoparticles by Surface Chemical Functionalization for Advanced Thermal Insulation.

Since solid-state heat transport in a highly porous nanocomposite strongly depends on the thermal boundary conductance (TBC) between constituent nanomaterials, further suppression of the TBC is important for improving performance of thermal insulators. Here, targeting a nanocomposite fabricated by stamping fumed silica nanoparticles, we perform a wide variety of surface functionalizations on fumed silica nanoparticles by a silane coupling method and investigate the impact on the thermal conductivity (Km). The Km of the silica nanocomposite is approximately 20 and 9 mW/m/K under atmospheric and vacuum conditions at the material density of 0.2 g/cm3 without surface functionalization, respectively, and the experimental results indicate that the Km can be modulated depending on the chemical structure of molecules. The surface modification with a linear alkyl chain of optimal length significantly suppresses Km by approximately 30%, and the suppression can be further enhanced to approximately 50% with an infrared opacifier. The magnitude of suppression was found to sensitively depend on the length of the terminal chain. The magnitude is also related to the number of reactive silanol groups in the chemical structure, where the surface modification with fluorocarbon gives the largest suppression. The surface hydrophobization merits thermal insulation through significant suppression of the TBC, presumably by reducing the water molecules that otherwise would serve as heat conduction channels at the interface. On the other hand, when the chain length is long, the suppression is counteracted by the enhanced phonon transmission through the silane coupling molecules that grow with the chain length. This is supported by the analytical model and present simulation results, leading to prediction of the optimal chemical structure for better thermal insulation.

Anisotropic thermal conductivity measurement of organic thin film with bidirectional 3ω method.

Organic thin film materials with molecular ordering are gaining attention as they exhibit semiconductor characteristics. When using them for electronics, the thermal management becomes important, where heat dissipation is directional owing to the anisotropic thermal conductivity arising from the molecular ordering. However, it is difficult to evaluate the anisotropy by simultaneously measuring in-plane and cross-plane thermal conductivities of the film on a substrate because the film is typically as thin as tens to hundreds of nanometers and its in-plane thermal conductivity is low. Here, we develop a novel bidirectional 3ω system that measures the anisotropic thermal conductivity of thin films by patterning two metal wires with different widths and preparing the films on top and extracting the in-plane and cross-plane thermal conductivities using the difference in their sensitivities to the metal-wire width. Using the developed system, the thermal conductivity of spin-coated poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) with thickness of 70 nm was successfully measured. The measured in-plane thermal conductivity of PEDOT:PSS film was as high as 2.9 W m-1 K-1 presumably due to the high structural ordering, giving an anisotropy of 10. The calculations of measurement sensitivity to the film thickness and thermal conductivities suggest that the device can be applied to much thinner films by utilizing metal wires with a smaller width.

Nanoconfinement between Graphene Walls Suppresses the Near-Wall Diffusion of the Ionic Liquid [BMIM][PF6].

We identify two distinct regimes for the diffusion of the ionic liquid [BMIM][PF6] confined between parallel graphene walls using molecular dynamics simulations. Within 2 nm of the wall, the cations and anions form a well-defined layered structure. In this region, the in-plane diffusion coefficients are suppressed when compared to their bulk values and increase monotonically with the distance away from the wall. Beyond 2 nm from the wall, the density profile and in-plane diffusion coefficients recover their bulk values. The channel-averaged in-plane diffusion coefficients increase monotonically with wall separation and recover the bulk values at a separation of 15 nm. A simple semianalytical model is proposed that mirrors this trend. The results also highlight the importance of applying a finite-size correction to molecular dynamics-predicted diffusion coefficients of confined liquids, which may otherwise be unusually larger than their bulk values.

Weaker bonding can give larger thermal conductance at highly mismatched interfaces.

Thermal boundary conductance is typically positively correlated with interfacial adhesion at the interface. Here, we demonstrate a counterintuitive experimental result in which a weak van der Waals interface can give a higher thermal boundary conductance than a strong covalently bonded interface. This occurs in a system with highly mismatched vibrational frequencies (copper/diamond) modified by a self-assembled monolayer. Using finely controlled fabrication and detailed characterization, complemented by molecular simulation, the effects of bridging the vibrational spectrum mismatch and bonding at the interface are systematically varied and understood from a molecular dynamics viewpoint. The results reveal that the bridging and binding effects have a trade-off relationship and, consequently, that the bridging can overwhelm the binding effect at a highly mismatched interface. This study provides a comprehensive understanding of phonon transport at interfaces, unifying physical and chemical understandings, and allowing interfacial tailoring of the thermal transport in various material systems.

Heat diffusion-related damping process in a highly precise coarse-grained model for nonlinear motion of SWCNT.

Second sound and heat diffusion in single-walled carbon nanotubes (SWCNT) are well-known phenomena which is related to the high thermal conductivity of this material. In this paper, we have shown that the heat diffusion along the tube axis affects the macroscopic motion of SWCNT and adapting this phenomena to coarse-grained (CG) model can improve the precision of the coarse-grained molecular dynamics (CGMD) exceptionally. The nonlinear macroscopic motion of SWCNT in the free thermal vibration condition in adiabatic environment is demonstrated in the most simplified version of CG modeling as maintaining finite temperature and total energy with suggested dissipation process derived from internal heat diffusion. The internal heat diffusion related to the cross correlated momentum from different potential energy functions is considered, and it can reproduce the nonlinear dynamic nature of SWCNTs without external thermostatting in CG model. Memory effect and thermostat with random noise distribution are not included, and the effect of heat diffusion on memory effect is quantified through Mori-Zwanzig formalism. This diffusion shows perfect syncronization of the motion between that of CGMD and MD simulation, which is started with initial conditions from the molecular dynamics (MD) simulation. The heat diffusion related to this process has shown the same dispersive characteristics to second wave in SWCNT. This replication with good precision indicates that the internal heat diffusion process is the essential cause of the nonlinearity of the tube. The nonlinear dynamic characteristics from the various scale of simple beads systems are examined with expanding its time step and node length.

Mechanically Strong, Scalable, Mesoporous Xerogels of Nanocellulose Featuring Light Permeability, Thermal Insulation, and Flame Self-Extinction.

Scalability is a common challenge in the structuring of nanoscale particle dispersions, particularly in the drying of these dispersions for producing functional, porous structures such as aerogels. Aerogel production relies on supercritical drying, which exhibits poor scalability. A solution to this scalability limitation is the use of evaporative drying under ambient pressure. However, the evaporative drying of wet gels comprising nanoscale particles is accompanied by a strong capillary force. Therefore, it is challenging to produce evaporative-dried gels or "xerogels" that possess the specific structural profiles of aerogels such as mesoscale pores, high porosity, and high specific surface area (SSA). Herein, we demonstrate a structure of mesoporous xerogels with high porosity (∼80%) and high SSA (>400 m2 g-1) achieved by exploiting cellulose nanofibers (CNFs) as the building blocks with tunable interparticle interactions. CNFs are sustainable, wood-derived materials with high strength. In this study, the few-nanometer-wide CNFs bearing carboxy groups were structured into a stable network via ionic inter-CNF interaction. The outline of the resulting xerogels was then tailored into a regular, millimeter-thick, board-like structure. Several characterization techniques highlighted the multifunctionality of the CNF xerogels combining outstanding strength (compression E = 170 MPa, σ = 10 MPa; tension E = 290 MPa, σ = 8 MPa), moderate light permeability, thermal insulation (0.06-0.07 W m-1 K-1), and flame self-extinction. As a potential application of the xerogels, daylighting yet insulating, load-bearing wall members can be thus proposed.

Identifying Optimal Strain in Bismuth Telluride Thermoelectric Film by Combinatorial Gradient Thermal Annealing and Machine Learning.

The thermoelectric properties of bismuth telluride thin film (BTTF) was tuned by inducing internal strain through a combination of combinatorial gradient thermal annealing (COGTAN) and machine learning. BTTFs were synthesized via magnetron sputter coating and then treated by COGTAN. The crystal structure and thermoelectric properties, namely Seebeck coefficient and thermal conductivity, of the treated samples were analyzed via micropoint X-ray diffraction and scanning thermal probe microimaging, respectively. The obtained combinatorial data reveals the correlation between internal strain and the thermoelectric properties. The Seebeck coefficient of BTTF exhibits largest sensitivity, where the value ranges from 7.9 to -108 µV/K. To further explore the possibility to enhance Seebeck coefficient, the combinatorial data were subjected to machine learning. The trained model predicts that optimal strains of 3-4% and 1-2% along the a- and c-axis, respectively, significantly improve Seebeck coefficient. The technique demonstrated herein can be used to predict and enhance the performance of thermoelectric materials by inducing internal strain.

Ultimate suppression of thermal transport in amorphous silicon nitride by phononic nanostructure.

Engineering the thermal conductivity of amorphous materials is highly essential for the thermal management of future electronic devices. Here, we demonstrate the impact of ultrafine nanostructuring on the thermal conductivity reduction of amorphous silicon nitride (a-Si3N4) thin films, in which the thermal transport is inherently impeded by the atomic disorders. Ultrafine nanostructuring with feature sizes below 20 nm allows us to fully suppress contribution of the propagating vibrational modes (propagons), leaving only the diffusive vibrational modes (diffusons) to contribute to thermal transport in a-Si3N4 A combination of the phonon-gas kinetics model and the Allen-Feldmann theory reproduced the measured results without any fitting parameters. The thermal conductivity reduction was explained as extremely strong diffusive boundary scattering of both propagons and diffusons. These findings give rise to substantial tunability of thermal conductivity of amorphous materials, which enables us to provide better thermal solutions in microelectronic devices.