Data set for 3D Printing of Chewable Tablets Manuscript

<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>

<b>图1.</b> 纳米混悬油墨的制备与表征：灰黄霉素（griseofulvin, GF）、羧甲淀粉钠（sodium starch glycolate, SSG）的扫描电子显微镜（Scanning Electron Microscopy, SEM）图像、研磨后纳米混悬液的扫描电子显微镜图像，以及纳米混悬液的透射电子显微镜（Transmission Electron Microscopy, TEM）图像。数据集包含：研磨后纳米混悬液的粒径分布（体积百分比）、不同油墨配方制备过程中35℃下孵育时间与含水率（质量百分比）的关系、水分蒸发量随时间的变化曲线，以及热重分析（Thermogravimetric Analysis, TGA）得到的质量损失随温度的变化曲线。<b>图2.</b> 油墨配方的流变学表征：剪切黏度随剪切速率的变化、剪切应力随剪切速率的变化、剪切模量随频率的变化、剪切模量随剪切应变的变化，以及在重复低应变（0.05%）和高应变（300%）条件下剪切模量随时间的变化。<b>图3.</b> 油墨配方的可印刷性表征。B) 印刷网格结构：包括支架的照片、孔隙的光学图像以及用于孔隙分析的二值化图像。D) 印刷实心圆盘：包括俯视图、截面图以及用于接触角计算的二值化图像。F) 40%浓度油墨在不同印刷压力（400~600 kPa）和印刷速度（3~15 mm/s）下的印刷支柱光学图像（比例尺=500 mm）。数据集包含：各油墨配方的可印刷性指数（Pr）、各油墨配方的接触角值，以及所有实验条件下测得的支柱宽度。<b>图4.</b> 数据集包含：油墨流速（计算值与实验测量值）随印刷压力*P*的变化；当印刷压力*P*为400、500、600 kPa且喷嘴偏移量为400、500、700 mm时的实验测量线宽与预测线宽；以及所有实验条件下的实验测量线宽与对应预测值。<b>图5.</b> 数据集：机器学习结果汇总：梯度提升树（Gradient Boosting）、K近邻（K-Neighbors）、随机森林（Random Forest）以及线性回归器（Linear Regressor）模型的模型效率与验证结果；使用通用梯度提升回归器模型从目标线宽反向预测印刷参数；使用单组预测印刷参数印刷的支柱的实际线宽测量结果；使用多组预测印刷参数印刷的支柱的实际线宽测量结果。<b>图6.</b> 3D打印片剂以及压粉混合物（Powder Mixture, PM）、灰黄霉素（GF）的成像与表征：使用40%浓度油墨印刷的直径6 mm（填充率100%和50%）、直径9 mm（填充率50%）的3D打印片剂的显微计算机断层扫描（Micro-CT）图像，以及对照组PM和GF样品的图像。数据集包含：羟丙基纤维素（Hydroxypropyl Cellulose, HPC）、GF、PM以及3D打印制剂的X射线衍射（X-ray Diffraction, XRD）图谱；以及HPC、GF、PM以及3D打印制剂的差示扫描量热法（Differential Scanning Calorimetry, DSC）图谱。<b>图7.</b> 片内均匀性及操作人员对工艺质量的影响：同一层内灰黄霉素含量的紫外-可见分光光度法（Ultraviolet-Visible Spectroscopy, UV-VIS）数据分析结果，以及由不同熟练程度操作人员制备的3D打印片剂的印刷后重量、干燥后重量以及通过UV-VIS测定的GF重量。<b>图8.</b> (A) 各样品的平均压缩模量，包括3D打印（3D Printing, 3DP）制剂、溶胀态3D打印制剂、压粉混合物（PM）、GF粉末以及市售软糖。(B) 100%填充率（直径6 mm）、50%填充率（直径6 mm和9 mm）的3D打印（3DP）片剂的灰黄霉素溶出百分比随时间的变化，以及压粉PM和GF的溶出结果（数据以均值±标准差表示，*n*=3）。