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One of the main challenges facing the expansion of Additive Manufacturing (AM) is the minimum feature sizes which these processes are able to achieve. Microscale Selective Laser Sintering (μ-SLS) is a novel Additive Manufacturing process created to meet this limitation by precisely laser sintering nanoparticles to give a better control over feature sizes. With the development of this new process, there is a concurrent need for models, which can predict the material properties of the sintering nanoparticles. To this end, this paper presents a novel simulation created to predict the electrical resistivity of sintered copper nanoparticles. Understanding the electrical resistivity of nanoparticles under sintering is useful for quantifying the rate of sintering and has applications such as predicting how the nanoparticles will fuse together when subjected to laser irradiation. Such a prediction allows for in situ corrections to be made to the sintering process to account for heat spreading beyond the intended laser irradiation targets. For these applications, it is important to ensure that the predictions of electrical resistivity from the simulations are accurate. This validation must be done against experimental data and since such experimental data does not currently exist, this paper also presents electrical resistivity data for the laser sintering of copper nanoparticles. In summary, this paper details the simulation methodology for predicting electrical resistivity of laser-sintered copper nanoparticles as well as validation of these simulations using electrical resistivity data from original sintering experiments. The key findings of this work are that the simulations can be used to predict electrical resistivity measurements for sintering of actual copper nanoparticles when the copper nanoparticles do not include other materials such as polymer coatings.
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Resistivity and weather data at Dry Creek Experimental Watershed in 2023
This dataset contains the electrical resistivity and weather data collected from the Lower Deer Point site of the Dry Creek Experimental Watershed for the calendar year 2023. Hourly monitored data is available for the weather conditions. Daily data is available for the calculation of evapotranspiration and subsurface storage. We have ten electrical resistivity surveys, and the related data of each survey include the measured apparent resistivity measurements and inverted resistivity images with RES2DINV software.
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- Award ID(s):
- 2330004
- PAR ID:
- 10544528
- Publisher / Repository:
- Hydroshare
- Date Published:
- Format(s):
- Medium: X
- Location:
- Dry Creek Experimental Watershed, Idaho
- Institution:
- Boise State University
- Sponsoring Org:
- National Science Foundation
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