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Updated: Jun 21, 2025

Designing Silk-silk Protein Alloy Materials for Biomedical Applications
Published on: August 13, 2014
Conformational transitions in redissolved silk fibroin films and application for printable self-powered multistate
Valeria Libera1, Rocco Malaspina1, Silvia Bittolo Bon1
1Dipartimento di Fisica e Geologia, Università degli Studi di Perugia Via A. Pascoli 06123 Perugia Italy.
Abstract:
3D printing of water stable proteins with elastic properties offers a broad range of applications including self-powered biomedical devices driven by piezoelectric biomaterials. Here, we present a study on water-soluble silk fibroin (SF) films. These films were prepared by mixing degummed silk fibers and calcium chloride (CaCl2) in formic acid, resulting in a silk I-like conformation, which was then converted into silk II by redissolving in phosphate buffer (PBS). Circular dichroism, Raman and infrared (IR) spectroscopies were used to investigate the transitions of secondary structure in silk I and silk II as the pH of the solvent and the sonication time were changed. We showed that a solvent with low pH (e.g. 4) maintains the silk I β-turn structure; in contrast solvent with higher pH (e.g. 7.4) promotes β-sheet features of silk II. Ultrasonic treatment facilitates the transition to water stable silk II only for the SF redissolved in PBS. SF from pH 7.4 solution has been printed using extrusion-based 3D printing. A self-powered memristor was realized, comprising an SF-based electric generator and an SF 3D-printed memristive unit connected in series. By exploiting the piezoelectric properties of silk II with higher β-sheet content and Ca2+ ion transport phenomena, the application of an input voltage driven by a SF generator to SF 3D printed holey structures induces a variation from an initial low resistance state (LRS) to a high resistance state (HRS) that recovers in a few minutes, mimicking the transient memory, also known as short-term memory. Thanks to this holistic approach, these findings can contribute to the development of self-powered neuromorphic networks based on biomaterials with memory capabilities.

