Porous materials provide a large surface‐to‐volume ratio, thereby providing a knob to alter fundamental properties in unprecedented ways. In thermal transport, porous nanomaterials can reduce thermal ...conductivity by not only enhancing phonon scattering from the boundaries of the pores and therefore decreasing the phonon mean free path, but also by reducing the phonon group velocity. Herein, a structure–property relationship is established by measuring the porosity and thermal conductivity of individual electrolessly etched single‐crystalline silicon nanowires using a novel electron‐beam heating technique. Such porous silicon nanowires exhibit extremely low diffusive thermal conductivity (as low as 0.33 W m−1 K−1 at 300 K for 43% porosity), even lower than that of amorphous silicon. The origin of such ultralow thermal conductivity is understood as a reduction in the phonon group velocity, experimentally verified by measuring the Young's modulus, as well as the smallest structural size ever reported in crystalline silicon (<5 nm). Molecular dynamics simulations support the observation of a drastic reduction in thermal conductivity of silicon nanowires as a function of porosity. Such porous materials provide an intriguing platform to tune phonon transport, which can be useful in the design of functional materials toward electronics and nanoelectromechanical systems.
Ultralow thermal conductivity of single‐crystalline porous silicon nanowires is probed. An electron‐beam technique is employed to directly measure the porosity of individual nanowires and also measure their thermal conductivity. A classical size effect due to small structure size combined with phonon softening arising from a large surface‐to‐volume ratio is shown to explain this behavior within the framework of diffusive thermal transport.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Combining high‐throughput experiments with machine learning accelerates materials and process optimization toward user‐specified target properties. In this study, a rapid machine learning‐driven ...automated flow mixing setup with a high‐throughput drop‐casting system is introduced for thin film preparation, followed by fast characterization of proxy optical and target electrical properties that completes one cycle of learning with 160 unique samples in a single day, a >10× improvement relative to quantified, manual‐controlled baseline. Regio‐regular poly‐3‐hexylthiophene is combined with various types of carbon nanotubes, to identify the optimum composition and synthesis conditions to realize electrical conductivities as high as state‐of‐the‐art 1000 S cm−1. The results are subsequently verified and explained using offline high‐fidelity experiments. Graph‐based model selection strategies with classical regression that optimize among multi‐fidelity noisy input‐output measurements are introduced. These strategies present a robust machine‐learning driven high‐throughput experimental scheme that can be effectively applied to understand, optimize, and design new materials and composites.
A graph‐based regressor and optimizer for high‐throughput multi‐fidelity, noisy experiments that designs electronically conducting composites is presented. An automated flow mixing and drop‐casting setup for P3HT‐CNT thin film preparation, followed by rapid characterization of optical and electrical properties of 160 unique samples per day, a >10× improvement relative to baseline, realizing electrical conductivities as high as ≈1000 S cm−1 is presented.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The instability of isolate catalysts during metal-assisted chemical etching is a major hindrance to achieve high aspect ratio structures in the vertical and directional etching of silicon (Si). In ...this work, we discussed and showed how isolate catalyst motion can be influenced and controlled by the semiconductor doping type and the oxidant concentration ratio. We propose that the triggering event in deviating isolate catalyst motion is brought about by unequal etch rates across the isolate catalyst. This triggering event is indirectly affected by the oxidant concentration ratio through the etching rates. While the triggering events are stochastic, the doping concentration of silicon offers a good control in minimizing isolate catalyst motion. The doping concentration affects the porosity at the etching front, and this directly affects the van der Waals (vdWs) forces between the metal catalyst and Si during etching. A reduction in the vdWs forces resulted in a lower bending torque that can prevent the straying of the isolate catalyst from its directional etching, in the event of unequal etch rates. The key understandings in isolate catalyst motion derived from this work allowed us to demonstrate the fabrication of large area and uniformly ordered sub-500 nm nanoholes array with an unprecedented high aspect ratio of ∼12.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM
High-entropy oxides (HEOs) are considered promising electrode materials as they have great potential to provide much higher energy density and cyclability than their conventional electrode ...counterparts such as graphite. In the present work, nanostructured HEOs were fabricated on the surface of conductive carbon black using laser beam irradiation, which generally implements the rapid bottom-up carbothermal process. Furthermore, electrochemical performances of Co-free and Co-incorporated HEO nanoparticles in comparison with bulk-HEO counterparts were investigated. In particular, the Co-free (LiFeNiMnCuZn) 3 O 4 nanoparticle electrode showed the best capability presenting both the highest cycling value of 866 mA h g −1 (100% capacity retention) after 800 cycles at 0.5 A g −1 and rate performances of 585 at 2.0 A g −1 and 436 mA h g −1 at 5.0 A g −1 without decay. The long cycling performance of Co-free HEOs could be derived from the reversible spinel structure, according to the in situ X-ray diffraction (XRD) results, as well as the strong thermal stability of high-entropy mixing phases, as indicated by a large positive decomposition enthalpy according to density functional theory (DFT) calculations. Additionally, the assembled full cell (LiFeNiMnCuZn) 3 O 4 ‖LiNi 0.6 Co 0.2 Mn 0.2 O 2 delivered a power density of 670 W h kg −1 with a high discharge voltage around 3.7 V based on the 0.1C discharge profile. As manifested by the DFT calculations, the low anode voltage of HEOs measured here is due to the electron-sufficient Zn, which favors the Ni 2+ /Ni 3+ redox couple. This work is expected to provide a guideline for the development of advanced high-entropy nanostructured electrode materials for efficient batteries.
Direct patterning of thermoelectric metal chalcogenides can be challenging and is normally constrained to certain geometries and sizes. Here we report the synthesis, characterization, and direct ...writing of sub-10 nm wide bismuth sulfide (Bi2S3) using a single-source, spin-coatable, and electron-beam-sensitive bismuth(III) ethylxanthate precursor. In order to increase the intrinsically low carrier concentration of pristine Bi2S3, we developed a self-doping methodology in which sulfur vacancies are manipulated by tuning the temperature during vacuum annealing, to produce an electron-rich thermoelectric material. We report a room-temperature electrical conductivity of 6 S m–1 and a Seebeck coefficient of −21.41 μV K–1 for a directly patterned, substoichiometric Bi2S3 thin film. We expect that our demonstration of directly writable thermoelectric films, with further optimization of structure and morphology, can be useful for on-chip applications.
Full text
Available for:
IJS, KILJ, NUK, PNG, UL, UM
Bayesian optimization (BO) has emerged as the algorithm of choice for guiding the selection of experimental parameters in automated active learning driven high throughput experiments in materials ...science and chemistry. Previous studies suggest that optimization performance of the typical surrogate model in the BO algorithm, Gaussian processes (GPs), may be limited due to its inability to handle complex datasets. Herein, various surrogate models for BO, including GPs and neural network ensembles (NNEs), are investigated. Two materials datasets of different complexity with different properties are used, to compare the performance of GP and NNE—the first is the compressive strength of concrete (8 inputs and 1 target), and the second is a simulated high‐dimensional dataset of thermoelectric properties of inorganic materials (22 inputs and 1 target). While NNEs can converge faster toward optimum values, GPs with optimized kernels are able to ultimately achieve the best evaluated values after 100 iterations, even for the most complex dataset. This surprising result is contrary to expectations. It is believed that these findings shed new light on the understanding of surrogate models for BO, and can help accelerate the inverse design of new materials with better structural and functional performance.
Bayesian optimization (BO) has emerged as the algorithm of choice for driving automated experiments for materials discovery. It is demonstrated that BO with Gaussian processes (GPs) and neural network ensemble surrogate models can extrapolate efficiently. In particular, GP with properly tuned parameters can outperform other models, even on a complex high‐dimensional 22‐input materials dataset.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In article number 2004896, John T. L. Thong, Jing Wu, and co‐workers investigate thermoelectric transport in novel 2D palladium diselenide (PdSe2) with a low‐symmetry pentagonal lattice. Due to its ...sensitive dependence on the interlayer coupling originating from the special lattice structure, the thermoelectric performance can be largely enhanced. The high band convergence and quantum confinement through thickness engineering advance and broaden the applications of thermoelectric such as in soft robotics, health monitoring, and human‐machine interfaces.
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Polymer‐based solid‐state electrolytes are shown to be highly promising for realizing low‐cost, high‐capacity, and safe Li batteries. One major challenge for polymer solid‐state batteries is the ...relatively high operating temperature (60–80 °C), which means operating such batteries will require significant ramp up time due to heating. On the other hand, as polymer electrolytes are poor thermal conductors, thermal variation across the polymer electrolyte can lead to nonuniformity in ionic conductivity. This can be highly detrimental to lithium deposition and may result in dendrite formation. Here, a polyethylene oxide‐based electrolyte with improved thermal responses is developed by incorporating 2D boron nitride (BN) nanoflakes. The results show that the BN additive also enhances ionic and mechanical properties of the electrolyte. More uniform Li stripping/deposition and reversible cathode reactions are achieved, which in turn enable all‐solid‐state lithium–sulfur cells with superior performances.
A boron nitride (BN) incorporated solid polymer electrolyte is developed with improved ionic conductivity, thermal responses, and mechanical properties for the application of all‐solid‐state Li–S batteries. The thermal and current uniformity derived from BN additive leads to a uniform Li deposition and preferred S conversion. Thus, an all‐solid‐state Li–S battery is successfully demonstrated with outstanding electrochemical performances.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Solid state lithium- and sodium-ion batteries utilize solid ionically conducting compounds as electrolytes. However, the ionic conductivity of such materials tends to be lower than their liquid ...counterparts, necessitating research efforts into finding suitable alternatives. The process of electrolyte screening is often based on a mixture of domain expertise and trial-and-error, both of which are time and resource-intensive. In this work, we present a novel machine-learning based approach to predict the ionic conductivity of sodium and lithium-based SICON compounds. Using primarily theoretical elemental feature descriptors derivable from tabulated information on the unit cell and the atomic properties of the components of a target compound on a limited dataset of 70 NASICON-examples, we have designed a logistic regression-based model capable of distinguishing between poor and good superionic conductors with a validation accuracy of over 84%. Moreover, we demonstrate how such a system is capable of cross-domain classification on lithium-based examples with the same accuracy, despite being introduced to zero lithium-based compounds during training. Through a systematic permutation-based evaluation process, we reduced the number of considered features from 47 to 7, reduction of over 83%, while simultaneously improving model performance. The contributions of different electronic and structural features to overall ionic conductivity is also discussed, and contrasted with accepted theories in literature. Our results demonstrate the utility of such a facile tool in providing opportunities for initial screening of potential candidates as solid-state electrolytes through the use of existing data examples and simple tabulated or calculated features, reducing the time-to-market of such materials by helping to focus efforts on promising candidates. Given enough data utilizing suitable descriptors, high accurate cross-domain classifiers could be created for experimentalists, improving laboratory and computational efficiency.
A computationally efficient first-principles approach to predict intrinsic semiconductor charge transport properties is proposed. By using a generalized Eliashberg function for short-range ...electron–phonon scattering and analytical expressions for long-range electron–phonon and electron–impurity scattering, fast and reliable prediction of carrier mobility and electronic thermoelectric properties is realized without empirical parameters. This method, which is christened “Energy-dependent Phonon- and Impurity-limited Carrier Scattering Time AppRoximation (EPIC STAR)” approach, is validated by comparing with experimental measurements and other theoretical approaches for several representative semiconductors, from which quantitative agreement for both polar and non-polar, isotropic and anisotropic materials is achieved. The efficiency and robustness of this approach facilitate automated and unsupervised predictions, allowing high-throughput screening and materials discovery of semiconductor materials for conducting, thermoelectric, and other electronic applications.
PDF
