Polydiolcitrate-MoS 2 Composite for 3D Printing Radio-Opaque, Bioresorbable Vascular Scaffolds.
Beata Maria SzydłowskaYonghui DingConnor MooreZizhen CaiCarlos G Torres-CastendoCaralyn P CollinsEvan JonesMark C HersamHao F ZhangGuillermo A AmeerPublished in: ACS applied materials & interfaces (2024)
Implantable polymeric biodegradable devices, such as biodegradable vascular scaffolds, cannot be fully visualized using standard X-ray-based techniques, compromising their performance due to malposition after deployment. To address this challenge, we describe a new radiopaque and photocurable liquid polymer-ceramic composite (mPDC-MoS 2 ) consisting of methacrylated poly(1,12 dodecamethylene citrate) (mPDC) and molybdenum disulfide (MoS 2 ) nanosheets. The composite was used as an ink with microcontinuous liquid interface production (μCLIP) to fabricate bioresorbable vascular scaffolds (BVS). Prints exhibited excellent crimping and expansion mechanics without strut failures and, importantly, with X-ray visibility in air and muscle tissue. Notably, MoS 2 nanosheets displayed physical degradation over time in phosphate-buffered saline solution, suggesting the potential for producing radiopaque, fully bioresorbable devices. mPDC-MoS 2 is a promising bioresorbable X-ray-visible composite material suitable for 3D printing medical devices, such as vascular scaffolds, that require noninvasive X-ray-based monitoring techniques for implantation and evaluation. This innovative biomaterial composite system holds significant promise for the development of biocompatible, fluoroscopically visible medical implants, potentially enhancing patient outcomes and reducing medical complications.
Keyphrases
- quantum dots
- reduced graphene oxide
- tissue engineering
- high resolution
- transition metal
- room temperature
- visible light
- highly efficient
- dual energy
- drug delivery
- ionic liquid
- healthcare
- gold nanoparticles
- drug release
- physical activity
- mental health
- electron microscopy
- machine learning
- skeletal muscle
- risk assessment
- magnetic resonance imaging
- deep learning
- human health
- big data