DOI: 10.6084/m9.figshare.28716182
title: Data set for 3D Printing of Chewable Tablets Manuscript

description: <b>Figure 1.</b><b> </b>Formulation and characterization of nanosuspension inks: SEM images of griseofulvin (GF), sodium starch glycolate (SSG), and milled nanosuspension, and TEM image of the nanosuspension. Data set: particle size distribution (vol.%) of the milled nanosuspension, water content (wt.%) as a function of incubation time at 35°C during preparation of various ink formulations, and mass of water evaporated as a function of time as well as % mass loss with temperature (from TGA).<b>Figure 2.</b> Rheology of ink formulations: Shear viscosity with shear rate, shear stress with shear rate, shear modulus with frequency, shear modulus with shear strain, and shear modulus with time at repetitive low (0.05%) and high (300%) strain.<b>Figure 3. </b>Characterization of the printability of ink formulations. B) Printed grid designs, including photographs of the scaffolds, optical images of the pores, and thresholded images of pores. D) Printed solid discs, including top-view images, cross-sectional images, and thresholded images used for contact angle calculation. F) Optical images of printed struts for the 40% ink under varying print pressures (400-600 kPa) and print speeds (3-15 mm/s) (scale bars = 500 mm). Data set: Printability index (Pr) plotted for each ink formulation, contact angle values for each ink formulation, and measured strut width for all conditions.<b>Figure 4.</b> Data set including change in ink flow rate (calculated and experimentally measured) with print pressure (<i>P</i>), experimentally measured line width and predicted line width for <i>P</i> = 400, 500, and 600 kPa and nozzle offset = 400, 500, and 700 mm, and experimentally measured line width with predicted values for all conditions.<b>Figure 5.</b> Data set: summary of machine learning outcomes: Model efficiency and validation for Gradient Boosting, K-Neigbors, Random Forest, and Linear Regressor models, inverse prediction of printing parameters from the target line width using the General Bossting Regressor model, actual line width measured for struts printed using a single set of predicted print parameters, actual line width measured for struts printed using multiple sets of predicted print parameters.<b>Figure 6.</b><b> </b>Imaging and characterization of 3D-printed tablets alongside compressed powder mixture (PM) and griseofulvin (GF): Micro-CT images of 3D-printed tablets (using 40% ink) with 6 mm diameter (100% and 50% infill) and 9 mm diameter (50% infill), along with control PM and GF samples. Data set including XRD profiles of HPC, GF, PM, and 3D-printed dose, and DSC profiles for HPC, GF, PM, and 3D-printed dose. <b>Figure 7. </b>Intra-tablet homogeneity and operator effects on process quality: UV-VIS data analysis of GF content across the same layers, and as-printed weight, dried weight and GF weight (determined by UV-VIS) for 3D-printed tablets produced by operators with varying levels of expertise.<b>Figure 8.</b><b> </b>(A) Average compressive modulus of each sample, including 3D-printed (3DP) dose, swollen 3DP dose, compressed powder mixture (PM), GF powder, and a commercial gummy. (B) Percent dissolved GF over time for 3D-printed (3DP) tablets with 100% infill (6 mm diameter), 50% infill (6 and 9 mm diameter), as well as compressed PM and GF (Data are presented as mean ± std. for n = 3). <br>