Nano-FFA: ink formulation and process optimization in multiphoton 3D laser printing using full factorial analysis [data]
Multiphoton 3D laser printing (MPLP) offers a unique combination of sub-micron resolution, geometrical freedom, and property variability. While this technique opens an extensive parameter space to develop new materials, it poses a significant challenge to disentangle and optimize the interrelated ef...
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| Main Authors: | , , |
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| Format: | Database Research Data |
| Language: | English |
| Published: |
Heidelberg
Universität
2025-10-29
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| DOI: | 10.11588/DATA/5G4P3G |
| Subjects: | |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.11588/DATA/5G4P3G Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/DATA/5G4P3G 
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| Author Notes: | Clara Vazquez-Martel, Samantha Catt, Eva Blasco |
MARC
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| 520 | |a Multiphoton 3D laser printing (MPLP) offers a unique combination of sub-micron resolution, geometrical freedom, and property variability. While this technique opens an extensive parameter space to develop new materials, it poses a significant challenge to disentangle and optimize the interrelated effects of chemical composition, process parameters, and resulting material properties. In this context, data analysis through full factorial analysis (FFA) can serve as a crucial tool for the systematic examination of how multiple factors interact and influence the final material properties of 3D printed microstructures, resulting in the identification of key parameters. In this work we propose a three-step approach, called ‘nano-FFA’, that involves: (1) evaluation of the printability of selected inks via scanning electron microscopy (SEM); (2) characterization of 3D printed structures using nanoindentation and vibrational spectroscopy; and (3) identification of interactions between ink formulation and printing parameters via FFA. Three scenarios have been investigated using the three-step nano-FFA approach: Scenario I focuses on the effect of the photoinitiator concentration. Scenario II examines the influence of different photoinitiator species and Scenario III evaluates the effect of the crosslinker. Across all scenarios, a significant interaction is observed between ink composition—i.e. photoinitiator concentration, photoinitiator type, and crosslinker—and the laser power (LP) printing parameter. This finding demonstrates that the properties of the final structures can be tailored by precisely selecting these two factors. The results of this study highlight the value of integrating statistical data analysis methods, such as FFA, into 3D printing material optimization toolboxes. Implementation of this new nano-FFA approach can provide a practical method for streamlining ink formulation and process optimization in MPLP, allowing rational ink development over a wide range of applications. | ||
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