ABSTRACT
Two-photon lithography (TPL) is a laser-based additive manufacturing technique that enables the printing of arbitrarily complex cm-scale polymeric 3D structures with sub-micron features. Although various approaches have been investigated to enable the printing of fine features in TPL, it is still challenging to achieve rapid sub-100 nm 3D printing. A key limitation is that the physical phenomena that govern the theoretical and practical limits of the minimum feature size are not well known. Here, we investigate these limits in the projection TPL (P-PTL) process, which is a high-throughput variant of TPL, wherein entire 2D layers are printed at once. We quantify the effects of the projected feature size, optical power, exposure time, and photoinitiator concentration on the printed feature size through finite element modeling of photopolymerization. Simulations are performed rapidly over a vast parameter set exceeding 10,000 combinations through a dynamic programming scheme, which is implemented on high-performance computing resources. We demonstrate that there is no physics-based limit to the minimum feature sizes achievable with a precise and well-calibrated P-TPL system, despite the discrete nature of illumination. However, the practically achievable minimum feature size is limited by the increased sensitivity of the degree of polymer conversion to the processing parameters in the sub-100 nm regime. The insights generated here can serve as a roadmap towards fast, precise, and predictable sub-100 nm 3D printing.
ABSTRACT
Tissue engineered bracket materials provide essential support for the physiological protection and therapeutics of patients. Unfortunately, the implantation process of such devices poses the risk of surgical complications and infection. In this study, an upconversion nanoparticles (UCNPs)-assisted 3D bioprinting approach is developed to realize in vivo molding that is free from invasive surgery. Reasonably designed UCNPs, which convert near-infrared (NIR) photons that penetrate skin tissues into blue-violet emission (300-500 nm), induce a monomer polymerization curing procedure in vivo. Using a fused deposition modeling coordination framework, a precisely predetermined trajectory of the NIR laser enables the manufacture of implantable medical devices with tailored shapes. A proof of the 3D bioprinting of a noninvasive fracture fixation scaffold is achieved successfully, thus demonstrating an entirely new method of in vivo molding for biomedical treatment.
Subject(s)
Bioprinting , Nanoparticles , Humans , Light , Prostheses and ImplantsABSTRACT
This document describes the collection and organization of a dataset that consists of raw videos and extracted sub-images from video frames of a promising additive manufacturing technique called Two-Photon Lithography (TPL). Four unprocessed videos were collected, with each video capturing the printing process of different combinations of 3D parts on different photoresists at varying light dosages. These videos were further trimmed to obtain short clips that are organized by experimental parameters. Additionally, this dataset also contains a python script to reproduce an organized directory of cropped video frames extracted from the trimmed videos. These cropped frames focus on a region of interest around the parts being printed. We envision that the raw videos and cropped frames provided in this dataset will be used to train various computer vision and machine learning algorithms for applications such as object segmentation and localization applications. The cropped video frames were manually labelled by an expert to determine the quality of the printed part and for printing parameter optimization as presented in "Automated Detection of Part Quality during Two-Photon Lithography via Deep Learning" [1].