Solidworks 2019

All the samples are
modeled using SOLIDWORKS2019 (Dassault Systems),
exported as “.stl” files, and then converted into “.gcode”
files through the software Ultimaker Cura.

Agarose microwell plates (61 wells) were fabricated using a 3D-printed mold and a polydimethylsiloxane (PDMS) mold, as illustrated in Supplementary Fig. S2. First, a microwell plate was designed using SolidWorks 2019 (Dassault Systèmes SolidWorks Corporation). A 3D-printed mold of a microwell plate was made with a printer (QIDI TECH Shadow 5.5S printer), poly(ethylene glycol) diacrylate (PEGDA, Mn: 250) as a resin, 1%(w/w) photoinitiator (Omnirad 819), and 1% (w/w) photosensitizer (2-Isopropylthioxanthone). The mold was immersed in ca.100 % ethanol for more than 1 min and was exposed to UV light for 4 min, then heated at 80 °C overnight. The mold was coated with Parylene (DPXC, CAS No. 28804-46-8; Parylane Japan) using a PDS-2010 Labcoter 2 (Specialty Coating Systems Inc.). Parylene was used for making PDMS (Silpot184; Toray-Dow Corning) easy to peel off. A PDMS mold was fabricated by conventional soft lithography. PDMS (elastomer: curing agent = 10:1) was cast into the parylene-coated molds and heated to solidify them. The solidified object was taken off from the molds. Agarose (2%) (SeaKem GTG Agarose, Lonza) was poured into the PDMS mold. After solidifying the agarose, the agarose microwell plate was removed from the mold and then immersed in TS basal medium until use.

An ideal symmetric lower airway model (Fig. 2) was constructed by using SolidWorks 2019 (Dassault Systems, France) and is based on Weibel’s respiratory system model, which was constructed from his measurements of human lung morphology⁴⁰ . The airway model was constructed to represent the airway tree from generation-0 to generation-2 and had five components: the trachea (G0), first branch (B1), first-generation bronchus (G1), second branch (B2), and second-generation bronchus (G2). The left and right sides were designated by L and R, respectively (e.g., G1R represented the first-generation bronchus on the right side). The diameter and length of each bronchus were taken from Weibel’s model. Following Rajendran et al.^{41 (link)}, the radii of curvature at B1 and B2 were set to 7d₁ and 4.5d₂, respectively, where d₁ was the diameter of G1 and d₂ was the radius of curvature of G2. The lower end of G2 was extended by 30d₂ to represent the entrance region of the airflow. The inlet was at each G2, and the outlet was at G0. All sides of the model were treated as walls of the airway.Figure 2

Airway model: (left) name of each part; (right) diameter, length, and angle.

The three-dimensional models of apertures for the various shapes were constructed in the 3D modeling software of Solidworks 2019 (Dassault Systèmes SOLIDWORKS Corp., Waltham, MA, USA) in this study, which exhibited the research objects intuitively.

Tri-axial IMUs (ProMove MINI, Inertia Technology, Enschede, The Netherlands; ±16 g primary, ±100 g secondary, ±34.91 rad/s, 1000 Hz) were secured to a centrifuge (ClearPath MCVC, Teknic, Victor, NY, USA) with custom 3-D printed jigs (SOLIDWORKS 2019, Dassault Systèmes, Vélizy-Villacoublay, France) and calibrated in 6 orientations at 16 known accelerations (from 0 to 41.42 g, where 1 g = 9.8 m/s² [47 ,48 ]) and angular velocities (from 0 to 78.54 rad/s). Adapting methods from Coolbaugh et al. [49 (link)], known data (K) from the centrifuge and measured data (M) from the IMU were used to calculate 3 × 7 calibration matrices (C; 3 signed magnitude terms, 3 absolute magnitude terms, and one bias term per axis) and quantify sensor accuracy after subtracting out biases observed during a quiet period (B) (Equation (1)). After calibration, IMU primary accelerometer errors were ≤0.01 ± 0.04 g, secondary accelerometer errors were ≤0.05 ± 0.07 g, and gyroscope errors were ≤0.01 ± 0.01 rad/s.

The prepared BBR-NPs (after filtration using 0.45 μm filter) were suspended in the above-mentioned resin solution, maintaining the final composition of polymers and TPO as a photo initiator (PI) in the resin solution (25% PEGDA, 3% PEO 100 k, 0.03% TPO, 0.12% SPS). Here, PEO also acts as a viscosity enhancer and a suspending agent to ensure homogenous dispersion of BBR-NPs across the resin solution. The fortified resin solution was poured onto the resin tank of the Form 2 SLA printer (Formlabs, UK), and the templates used to print the nanocomposite units were designed using SolidWorks 2019 (Dassault Systems) and exported as a .stl file into the 3D printer software (Preform Software v. 1.9.1, Formlabs, UK). The printer was operated in open mode with a clear resin selected using Preform software. A 3D printed pill with 7.50 mm diameter and 5.00 mm thickness with a layer height of 50 μm was uploaded into the 3D printer (Fig. 1). Seven nanocomposite units were printed to achieve batch uniformity. The obtained nanocomposite units were rinsed in DI water to remove the unreacted resin from the surface.
Fig. 1

CAD designed 3D model of a nanocomposite drug delivery system with dimensions