Search NASA⌕ Search

SEARCH · Search NASA

Results for “BISMUTH TELLURIDE”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Structural Design of Bismuth Telluride Nanoplates through Process Variables

Binary pnictogen chalcogen compounds, primarily bismuth tellurides and selenides, are of great interest due to their applications in emerging quantum devices, as well as thermoelectric generators. The performance of bismuth telluride in these roles depends on its structure at the nanoscale, particularly the size, shape, and crystallinity of its nanocrystalline forms. However, current methods for controlling these features are often slow, inconsistent, or difficult to scale. Here, we demonstrate that through a solvothermal synthesis and hot injection process, precise control over the morphology of bismuth telluride nanoplates is possible with independent tuning of process variables, such as temperature and reaction time. We find that the nanoplate shape and internal porosity vary systematically with synthesis temperature and that the same morphological outcomes can be rapidly achieved at a fixed temperature by adjusting reaction duration. These results reveal that both the temperature and time can independently direct bismuth telluride morphological features, allowing for rapid, tunable synthesis strategies. Our approach offers a scalable framework, not only for bismuth telluride but also for related layered chalcogenides used in energy harvesting and quantum technologies.

Ackley, Jordan [Boise State Univ., ID (United Stat↗
DOE OSTI2023

Freeform thermoelectrics in single-step manufacturing: additive manufacturing of bismuth-telluride thermoelectrics

The project succeeded in producing crack-free Bismuth Telluride thermoelectric parts with density exceeding 98% through laser powder bed fusion (LPBF) additive manufacturing (AM). This greatly exceeded the highest previously reported density of 88% and is the highest among all semiconducting materials processed by LPBF. The additively manufactured material shows comparable Seebeck coefficient as conventional form and can be made into complex geometries with reduced material loss. On the other hand, measured properties are dramatically sensitive to the AM process parameters used, such that with identical composition, the Seebeck coefficient can be controllably tuned from +120 µV/K to -207 µV/K, which means the material switches between an n-type to a p-type semiconductor depending on processing. These changes are accompanied by significant differences in the as-processed microstructure due to rapid solidification. A machine learning protocol was developed and greatly reduced the experimental burden of the project, reducing the typical process optimization period of 2 years to 6 months. The project was fully successful in the objective of producing defect-free, complex geometry of bismuth-telluride parts through LPBF, but only partially successful in achieving performance goals. First, cost reduction of manufacturing, as measured by material waste, was successfully reduced by up to 70% compared to conventional manufacturing methods. This exceeded the proposed 30% reduction in materials waste needed to reach the 20% cost reduction goal of the project. On the other hand, the device performance, as measured by Seebeck coefficient, failed to reach the 40% improvement in efficiency. Rather, we observe comparable Seebeck coefficient between AM samples and conventionally processed counterparts. The device-level efficiency improvement does exceed 40% for complex geometry samples due to shape-induced increase in temperature gradients, but this was not the originally proposed metric. The machine learning approach developed in this project greatly accelerated the process optimization and can be adopted for fast development of AM processing parameters for other brittle and otherwise difficult-to-print materials. For the public, we deliver an efficient and widely adoptable process for incorporating waste-heat harvesting thermoelectric devices in both industrial and commercial heat exchangers. The geometric flexibility allows the capturing device to conform to the shape of the heat source to improve the system-level conversion efficiency. The technique can be deployed on any commercial LBPF systems with zero modifications, thus poses minimal adoption barrier for any manufacturer that already employed AM technology. Beyond bismuth-telluride, the machine-learning guided optimization protocol can be used in the future to reduce both the time and cost of process development for AM of other energy conversion and harvesting materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ternary Potassium‐Bismuth‐Telluride Intermetallic Support Promotes Electrochemical Stability in Potassium Metal Anodes

We employed accumulative roll bonding to fabricate self‐standing metallurgical composite of in situ formed alkaline potassium‐bismuth‐telluride intermetallic K 2 (Bi 2/6 Te 3/6 Vac 1/6 ) embedded in potassium metal. This newly discovered thermodynamically stable potassiophilic crystal, termed “KBT”, is fcc antifluorite with K 2 Te archetype. Symmetric cells achieve 880 h of cycling at 0.5 mA cm −2 and 0.5 mAh cm −2 . Potassium metal battery (KMB) with Prussian blue (PB) cathode in carbonate electrolyte retains 80% capacity after 200 cycles at 1C. In ether‐based electrolyte with organic cathode, it achieves 80% retention after 900 cycles at 2C. Combined synchrotron X‐ray nano‐tomography, cryogenic‐focused ion beam microscopy (Cryo‐FIB) and sputter‐down X‐ray photoelectron spectroscopy (XPS) demonstrate uniform electrodeposits, versus baseline of potassium filaments intermixed with pores and coarse SEI. Binary K 3 Bi‐K and K 2 Te‐K intermetallic supports also provide improved electrochemical performance, albeit to lesser extent. Multiscale simulation provides insight into role of support structure in adatom energetics, film nucleation, early‐stage SEI morphology and interfacial stability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Laser Additive Manufacturing Process Development for Bismuth Telluride Thermoelectric Material

Thermoelectric generators have strong potential for aerospace and aircraft applications. However, traditional manufacturing methods for thermoelectric devices severely limit device adaptability and consequently marketability. Laser powder bed fusion is an additive manufacturing method which shows promising potential for producing thermoelectric devices. Thermoelectric materials pose unique challenges compared to metals because of low thermal conductivity, brittle fracture characteristics, and irregular powder particle morphologies. Herein, we communicate processing procedures to fabricate thermoelectric parts of Bi 2 Te 3 through laser powder bed fusion. In this study, we identify the successful combination of key process parameters—laser power, scan speed, hatch distance, and powder layer thickness—to achieve high-quality parts in terms of density and physical properties, and we demonstrate the impact of process parameter variation on built part quality. While it cannot exclusively determine part quality, the volumetric energy density is a useful guide in deciding process parameters for thermoelectric materials, and the optimal value is between 9 and 11 J/mm 3 for Bi 2 Te 3 . We demonstrate successful fabrication of different freeform geometries of Bi 2 Te 3 powders. The results suggest it is possible to extend this method more broadly to other semiconductor materials, including thermoelectric power generation materials applicable for space applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enhanced thermoelectric efficiency in nanocrystalline bismuth telluride nanotubes

We report on the thermal and thermoelectric properties of individual nanocrystalline Bi 2 Te 3 nanotubes synthesized by the solution phase method using 3ω method and a microfabricated testbench. Measurements show that the nanotubes offer improved ZT compared to bulk Bi 2 Te 3 near room temperature due to an enhanced Seebeck coefficient and suppressed thermal conductivity. This improvement in ZT originates from the nanocrystalline nature and low dimensionality of the nanotubes. Domain boundary filtering of low-energy electrons provides an enhanced Seebeck coefficient. The scattering of phonons at the surface of the nanotube leads to suppressed thermal conductivity. These have been theoretically analyzed using the Boltzmann equation based on the relaxation time approximation and Landauer approach. This work clearly demonstrates the possibility of achieving enhancement in thermoelectric efficiency by combining nanocrystalline and low-dimensional systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Prediction of Bi 2 Te 3 -Sb 2 Te 3 Interfacial Conductance and Superlattice Thermal Conductivity Using Molecular Dynamics Simulations

Bismuth telluride (Bi 2 Te 3 ) and its alloys with antimony telluride (Sb 2 Te 3 ) have long been considered to be the best room-temperature bulk thermoelectric (TE) materials. In recent decades, proof-of-concept demonstrations on Bi 2 Te 3 -Sb 2 Te 3 nanostructures have shown high TE performance due to reduction in lattice thermal conductivities. Particularly, ultra-low thermal conductivities have been observed in Bi 2 Te 3 -Sb 2 Te 3 1D superlattices, leading to thermoelectric figures of merit (ZT) as high as 2.4. In contrast, very few computational studies have been performed to provide insight into the phonon transport across these nanostructures. In this work, we use non-equilibrium molecular dynamics simulations with previously developed force fields to simulate thermal transport across Bi 2 Te 3 -Sb 2 Te 3 interfaces and superlattices. We first calculate the thermal conductance associated with a Bi 2 Te 3 -Sb 2 Te 3 interface across a temperature range of 200–400 K. Furthermore, the values are also compared with thermal conductances calculated by a modified Landauer transport formalism using phonon transmission coefficients obtained from the diffuse mismatch model. Our results show that inelastic scattering processes contribute to an increase in interfacial thermal conductance at higher temperatures. Finally, we calculate the thermal conductivities of Bi 2 Te 3 -Sb 2 Te 3 superlattices with varying period lengths from 2 to 18 nm. A minimum thermal conductivity of 0.27 W/mK is observed at a period length of 4 nm, which is attributed to the competition between incoherent and coherent phonon transport regimes. In comparison with previous experimental measurements in the literature, our results show good agreement with respect to the range of thermal conductivity values and the period length corresponding to the minimum superlattice thermal conductivity.

42 ENGINEERING↗
DOE OSTI2024

The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter space for a bismuth telluride thermoelectric (TE) material. In doing so, we determined process parameters that created crack-free, highly dense (>99 %) n-type bismuth telluride (Bi 2 Te 2.7 Se 0.3 ) parts through laser powder bed fusion (LPBF). Furthermore, the ML-assisted understanding of the processing space allowed for the identification of build parameters that successfully yielded geometrically enhanced Bi 2 Te 2.7 Se 0.3 parts with reduced build times and no increase in experimental effort.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Ex Situ Photoelectron Emission Microscopy of Polycrystalline Bismuth and Antimony Telluride Surfaces Exposed to Ambient Oxidation

The surfaces of textured polycrystalline N-type bismuth telluride and P-type antimony telluride materials were investigated using ex situ photoelectron emission microscopy (PEEM). PEEM enabled imaging of the work function for different oxidation times due to exposure to air across sample surfaces. The spatially averaged work function was also tracked as a function of air exposure time. N-type bismuth telluride showed an increase in the work function around grain boundaries relative to grain interiors during the early stages of air exposure-driven oxidation. At longer time exposure to air, the surface became homogenous after a ~5 nm-thick oxide formed. X-ray photoemission spectroscopy was used to correlate changes in PEEM imaging in real space and work function evolution to the progressive growth of an oxide layer. The observed work function contrast is consistent with the pinning of electronic surface states due to the defects at a grain boundary.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Accelerating Additive Manufacturing Process Design for Energy Conversion Materials using In-situ Sensing and Machine Learning

One promising candidate for manufacturing of the bismuth Telluride thermoelectric legs is laser powder bed fusion (LPBF) additive Manufacturing (AM). AM processing parameters highly influence the material properties, however current processing parameter development methods in AM are costly and time consuming. In-situ sensors allow for the capture of physically relevant process information on a layer-by-layer basis and will be used to aide process development. To optimize the AM process for the best thermoelectric performance, process variables, in-situ process sensor data and ex-situ material characterization data are collected. Several different interpretable machine learning (ML) approaches are used, and the performance of each method are assessed. Significant input process variables include laser focus, hatch spacing and laser power. The best performing models are used to determine the manufacturing parameters that maximize the power factor. AM of bismuth telluride material provides the ability to create complex geometries enabling more efficient energy conversion.

30 DIRECT ENERGY CONVERSION↗