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A key requirement for achieving passive radiative cooling for an ideal emitter, in the sky window (8–13 µm), during daytime is a total solar reflection >85%, and every 1% above this threshold results in ∼10 W/m2 gain in cooling power. One promising, inexpensive, and scalable solution for achieving high total solar reflectance is a dielectric nanoparticle-polymer composite coating. Past works have widely used a single particle size. However, it is challenging to achieve solar reflectance significantly above 85%. Here, recognizing the broadband nature of the solar irradiation, we propose and test a new concept of enhancing solar reflection at a given particle volume concentration by using hierarchical particle sizes, which we hypothesize to scatter each band of the solar spectrum i.e. VIS, NIR, and UV effectively. The hypothesis is tested using a TiO2 nanoparticle-acrylic system. Using the Mie Theory, the scattering and absorption efficiencies and asymmetric parameter of nanoparticles with different sizes and combinations are calculated, then the Monte Carlo Method is used to solve the Radiative Transfer Equation. When validating our computational model to in-house experimental results it is found that a nanoparticle size distribution of d = 104 ± 37 nm creates an overall better fit to the experimental data and increases the total solar reflection when compared to the single size model of d = 104 nm. We then purposely design hierarchical combinations of particle sizes in the broader range of 50 nm to 800 nm, and we have achieved an overall total solar reflection of ≈91%, which is higher than the ≈78% and ≈88% for 100 nm and 400 nm single particle sizes, respectively. The results confirm our hypothesis that hierarchical particle sizes can scatter over a broad spectrum more effectively rather than any single particle size. Moreover, our findings could also cut the manufacturing cost since no precise control of particle size is necessary.

The coupling of lattice dynamics and phonon transport methodologies with density functional theory has become a powerful tool for calculating lattice thermal conductivity (κ) with demonstrated quantitative accuracy and applicability to a wide range of materials. More importantly, these first-principles transport methods lack empirical tuning parameters so that reliable predictions of κ behaviors in new and old materials can be formulated. Since its inception nearly a decade ago, first-principles thermal transport has vastly expanded the range of materials examined, altered our physical intuition of phonon interactions and transport behaviors, provided deeper understanding of experiments, and accelerated the design of materials for targeted thermal functionalities. Such advances are critically important for developing novel thermal management materials and strategies as heat sets challenging operating limitations on engines, microelectronics, and batteries. This article provides a comprehensive survey of first-principles Peierls-Boltzmann thermal transport as developed in the literature over the last decade, with particular focus on more recent advances. This review will demonstrate the wide variety of calculations accessible to first-principles transport methods (including dimensionality, pressure, and defects), highlight unusual properties and predictions that have been made, and discuss some challenges and behaviors that lie beyond.

One passive cooling approach is pumping energy to outer space through thermal radiation. Such a radiative cooling mechanism widely exists in nature and is important to maintain the temperature of the earth. However, natural materials generally have poor radiative cooling efficiency. To better utilize the radiative cooling for thermal management applications, the surface should be designed to have a high reflectivity in the solar spectrum and high emissivity in the "sky window" region (8–13 µm in wavelength). In this work, we propose and demonstrate a highly scalable nanoparticle-based double-layer coating to achieve such selective radiative properties. Double-layer coatings consisting of a top reflective layer with high solar albedo and a bottom emissive layer are achieved by properly designed TiO 2 , SiO 2 , and SiC nanoparticles. These coatings were fabricated on both low- and high-emissivity substrates and their spectral radiative properties were characterized. The coating composed of TiO 2 and SiO 2 on a reflective substrate has excellent selective emission property for radiative cooling purpose. Under dry air conditions and assuming non-radiative heat transfer coefficient h c =4 W/m 2 K, TiO 2 +SiO 2 and TiO 2 +SiC can theoretically achieve about 17 °C below ambient at night and 5 °C below ambient under direct solar radiation (AM1.5). On-site measurements have also been conducted. Under direct solar irradiation, significant temperature reduction was observed for both aluminum and black substrate after the coating was applied. At nighttime, radiative cooling effect can cool the surface to a few degrees below ambient temperature. Although the theoretical cooling under dry weather condition is not observed, the experiment results can be well explained by theoretical calculations with the consideration of high humidity and non-radiative heat transfer. This nanoparticle-based approach can be easily applied to large area, which is a significant step of achieving large scale application of the radiative cooling technology.

Radiative cooling is a passive cooling method by emitting heat to outer space without energy input. In this work we propose a cost effective double-layer coating embedded with nanoparticles for both nighttime and daytime radiative cooling. The top and bottom layers are acrylic resin embedded with titanium dioxide and carbon black particles, respectively responsible for reflecting the solar irradiation and emitting the heat in the atmospheric transparency window. The carbon black layer is considered as the black substrate. For the top layer, different sizes of titanium dioxide particles are examined, and 0.2 μm radius is found to give the best cooling performance. More than 90% of the solar irradiation can be reflected, and the average emissivity in the atmospheric transparency window is larger than 0.9 in most directions. A daytime net cooling power over 100 W/m2 is predicted at the ambient temperature. The cooling effect persists even if significant conduction and convection heat exchange is considered.

Many crystals of scientific and technical importance have the face-centered cubic (FCC) lattice. Computational studies of electronic, thermal, and optical properties of FCC crystals usually involve the first Brillouin zone. In this work, we examine the geometry and discretization of the first Brillouin zone of FCC crystals. We report the coordinates of the high symmetry k points in the first Brillouin zone and a systematic way of determining the coordinates of the symmetry k points. We find that using the symmetry k points could reduce the total number of k points by as much as 97.92% and thus greatly reduce the computational cost. We propose a formula for calculating lattice thermal conductivity by using phonon properties at the symmetry k points. The formula is validated by calculating the thermal conductivity of solid argon in the temperature range from 10 to 80 K with the phonon spectral energy density (SED) method and comparing the results with those from equilibrium molecular dynamics (EMD) simulations and experiments.

This work provides a comprehensive investigation on the spectral phonon properties in graphene nanoribbons (GNRs) by the normal mode decomposition (NMD) method, considering the effects of edge chirality, width, and temperature. We find that the edge chirality has no significant effect on the phonon relaxation time but has a large influence to the phonon group velocity. As a result, the thermal conductivity of around 707 W/(m K) in the 4.26 nm-wide zigzag GNR at room temperature is higher than that of 467 W/(m K) in the armchair GNR with the same width. As the width decreases or the temperature increases, the thermal conductivity reduces significantly due to the decreasing relaxation times. Good agreement is achieved between the thermal conductivities predicted from the Green–Kubo method and the NMD method. We find that optical phonons dominate the thermal transport in the GNRs while the relative contribution of acoustic phonons to the thermal conductivity is only 10.1% and 13% in the zigzag GNR and the armchair GNR, respectively. Interestingly, the ZA mode is found to contribute only 1–5% to the total thermal transport in GNRs, being much lower than that of 30–70% in single layer graphene.

Summary Radiative cooling is a passive cooling technology that acts by reflecting sunlight and emitting radiation in the sky window. Although highly desirable, full daytime sub-ambient radiative cooling in commercial-like single-layer particle-matrix paints has not been achieved. Here, we demonstrate full daytime sub-ambient radiative cooling in CaCO3-acrylic paint by using large band gap CaCO3 fillers, a high particle concentration of 60%, and a broad size distribution. Our paint shows a high solar reflectance of 95.5% and a high normal emissivity of 0.94 in the sky window. Field tests show cooling power exceeding 37 W/m2 and a surface temperature of >1.7°C below ambient at noon. A figure of merit RC is proposed to compare the cooling performance independent of weather conditions. The standard RC of our paint is 0.49, among the best radiative cooling performances, while offering the benefits of convenient paint form, low cost, and compatibility with commercial paint fabrication processes.

Discovering exceptions has been a major route for advancing sciences but a challenging and risky process. Machine learning has shown effectiveness in high throughput search of materials and nanostructures, but using it to discover exceptions has been out of the norm. Here we demonstrate the use of genetic algorithm to discover unexpected thermal conductivity enhancement in disordered nanoporous graphene as compared to periodic nanoporous graphene. Recent studies have concluded that random pores in nanoporous graphene lead to reduced thermal conductivity than periodic pores, due to phonon Anderson localization. This work, however, aims to challenge this accepted knowledge by searching for exceptions. A manual search was first shown to be expensive and unsuccessful. An efficient “two-step” search process coupled with genetic algorithm was then designed, and unexpected thermal conductivity enhancement was successfully discovered in certain structures with random pores, at a fraction of the computational cost of the manual search. Through structural analysis, we proposed that such unusual enhancement is due to the effect of shape factor and channel factor dominating over that of the phonon localization. Our work not only provides insights in thermal transport in disordered materials but also demonstrates the effectiveness of machine learning to discover small probability events and the intriguing physics behind.

Nanostructuring materials to achieve ultra-low lattice thermal conductivity has proven to be extremely attractive for numerous applications such as thermoelectric energy conversion. Anderson localization of phonons due to aperiodicity can reduce thermal conductivity in superlattices, but the lower limit of thermal conductivity remains elusive due to the prohibitively large design space. In this work, we demonstrate that an intuition-based manual search for aperiodic superlattice structures (random multilayers or RMLs) with the lowest thermal conductivity yields only a local minimum, while a genetic algorithm (GA) based approach can efficiently identify the globally minimum thermal conductivity by only exploring a small fraction of the design space. Our results show that this minimum value occurs at an average RML period that is, surprisingly, smaller than the period corresponding to the minimum SL thermal conductivity. Above this critical period, scattering of incoherent phonons at interfaces is less, whereas below this period, the room for randomization becomes less, thus putting more coherent phonons out of Anderson localization and causing increased thermal conductivity. Moreover, the lower limit of the thermal conductivity occurs at a moderate rather than maximum randomness of the layer thickness. Our machine learning approach demonstrates a general process of exploring an otherwise prohibitively large design space to gain non-intuitive physical insights.

Thermal radiative properties of thin graphitic petal arrays are theoretically and experimentally investigated. Finite-difference time-domain (FDTD) simulations are first performed to calculate optical properties of vertical graphitic arrays of different structures, namely, graphitic gratings, periodic graphitic cavities, and random graphitic cavities. For graphitic gratings, the absorptance and reflectance are relatively larger when the incident electric field is parallel to the graphitic plane, while the absorptance and reflectance are both significantly lower when the electric field is polarized perpendicular to the graphitic plane. Ordered graphitic petal cavity arrays show optical properties falling between the above two cases of different polarizations. Random vertical cavity arrays with various angles of orientation show similar properties to ordered petal cavities. For oblique gratings, the reflectance will increase with oblique angle for both polarizations, while the absorptance decreases with oblique angle for the in-plane polarization and increases with oblique angle for the out-of-plane polarization. The oblique effects are explained by the strong anisotropic nature of graphitic petals. The FDTD results are compared to effective medium theory to find that the latter describes the optical properties of the graphitic grating and cavity well, and we propose an approach based on effective medium theory to approximate the dielectric function of graphitic petals with random orientation. The predicted hemispherical total reflectance based on this model gives about 2% reflectance in the visible spectrum and agrees well with experimental data from a fabricated graphitic petals sample.

The optical properties of multi-walled carbon nanotube arrays are investigated using the finite-difference time-domain method, focusing on the effects of various structural randomness, including random position, diameter, length, and orientation. It is found that the arrays with random position, diameter, length, and small inclination angle have quite small absorption enhancement compared to the ordered arrays. The reflection spectra of the arrays with random position, diameter, and small inclination angles are almost identical to the ordered array, but large reflection suppression is seen in vertical arrays with random length. For oblique arrays, the absorptance increases with inclination angle for S-polarized light, but weakly depends on inclination angle for P-polarized light. By comparing the inclined arrays with different volume fractions, the reflectance is found to be largely determined by the local volume fraction of the top surface of carbon nanotube arrays.

The Lorentz oscillator model is well-known for its effectiveness to describe the far-infrared optical properties of polar materials. The oscillator strength and damping factor in this model are usually obtained by fitting to experimental data. In this work, a method based on first-principles simulations is developed to parameterize the Lorentz oscillator model without any fitting parameters. The high frequency dielectric constant is obtained from density functional perturbation theory, while the optical phonon frequencies and damping factors are calculated using an analysis of ab initio molecular dynamics trajectories. This method is then used to predict the far-infrared properties of GaAs, and the results are in good agreement with experimental data. (c) 2012 Elsevier Ltd. All rights reserved.

High-crystalline 0D, 1D and 2D ultra-small Bi2Te3 nanocrystals were synthesized using the pyrolysis of organometallic compound method. The Bi2Te3 nanocrystals was studied with transmission electron microscopy (TEM). Samples synthesized at 35 degrees C possessed a dominant morphology of 0D nanoparticles or 1D nanorods, while samples synthesized at temperatures higher than 75 degrees C possessed a dominant morphology of 2D nanoplates. Phonon vibrations were investigated by Raman spectroscopy. The 2D nanoplates showed similar Raman features as few-quintuple-thick Bi2Te3 layers, while the 0D and 1D nanostructures showed a blue-shifted A(1g)(2) mode and a much stronger A(1u) mode. This is the first report regarding the morphology effect on the Raman-active phonon modes of Bi2Te3 nanocrystals. (C) 2012 Elsevier B.V. All rights reserved.