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Appelhans, Leah

Publications and source records attributed to Appelhans, Leah.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing photochemical kinetics and mechanisms using photoNMR

Nuclear magnetic resonance spectroscopy (NMR) is a form of spectroscopy that yields detailed mechanistic information about chemical structures, reactions, and processes. Photochemistry has widespread use across many industries and holds excellent utility for additive manufacturing (AM) processes. Here, we use photoNMR to investigate three photochemical processes spanning AM relevant timescales. We first investigate the photodecomposition of a photobase generator on the slow timescale, then the photoactivation of a ruthenium catalyst on the intermediate timescale, and finally the radical polymerization of an acrylate system on the fast timescale. In doing so, we gain fundamental insights to mission relevant photochemistries and develop a new spectroscopic capability at SNL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Use of latent metathesis polymerization systems for additive manufacturing

The invention is directed to latent metathesis-active resin compositions and additive manufacturing approaches. The latent metathesis-active compositions comprise at least one latent metathesis catalyst, at least one curable metathesis-active monomer, and optional fillers, photosensitizers, and/or a secondary monomer resin system for dual-cure. A method comprises extruding a latent metathesis-active resin from a dispensing apparatus and spatially triggering a ring-opening metathesis polymerization by directed irradiation with light or directed application of a thermal stimuli for direct-ink write printing of objects. Another method uses a latent metathesis-active resin for vat photopolymerization and stereolithographic printing of objects.

Appelhans, Leah↗
DOE OSTI2023

Use of latent metathesis polymerization systems for photopolymerization-based additive manufacturing

Photolatent olefin metathesis catalysts can be used in combination with metathesis-active monomers or resins for photopolymerization-based additive manufacturing via ring-opening metathesis polymerization (ROMP). A variety of alternative catalysts and photosensitizers are commercially available that allows for tuning of thermomechanical properties, potlifes, activation rates, and irradiation wavelengths. As an example, two optimized catalyst/photosensitizer systems were demonstrated in the rapid stereolithographic printing of complex, multidimensional poly-dicyclopentadiene structures with microscale features under ambient conditions.

Leguizamon, Samuel↗
DOE OSTI2023

Crosslinked polymers with tunable coefficients of thermal expansion

Curatives and their resulting thermosets and other crosslinked polymers can reduce thermal expansion mismatch between an encapsulant and objects that are encapsulated. This can be accomplished by incorporating a negative CTE moiety into the thermoset resin or polymer backbone. The negative CTE moiety can be a thermal contractile unit that shrinks as a result of thermally induced conversion from a twist-boat to chair or cis/trans isomerization upon heating. Beyond CTE matching, other potential uses for these crosslinked polymers and thermosets include passive energy generation, energy absorption at high strain rates, mechanophores, actuators, and piezoelectric applications.

Redline, Erica Marie↗
DOE OSTI2023

Invertible neural networks for real-time control of extrusion additive manufacturing

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

36 MATERIALS SCIENCE↗