Silk-based intelligent fibers and textiles: structures, properties, and applications.

Multifunctional fibers represent a cornerstone of human civilization, playing a pivotal role in numerous aspects of societal development. Natural biomaterials, in contrast to synthetic alternatives, offer environmental sustainability, biocompatibility, and biodegradability. Among these biomaterials, natural silk is favored in biomedical applications and smart fiber technology due to its accessibility, superior mechanical properties, diverse functional groups, controllable structure, and exceptional biocompatibility. This review delves into the intricate structure and properties of natural silk fibers and their extensive applications in biomedicine and smart fiber technology. It highlights the critical significance of silk fibers in the development of multifunctional materials, emphasizing their mechanical strength, biocompatibility, and biodegradability. A detailed analysis of the hierarchical structure of silk fibers elucidates how these structural features contribute to their unique properties. The review also encompasses the biomedical applications of silk fibers, including surgical sutures, tissue engineering, and drug delivery systems, along with recent advancements in smart fiber applications such as sensing, optical technologies, and energy storage. The enhancement of functional properties of silk fibers through chemical or physical modifications is discussed, suggesting broader high-end applications. Additionally, the review addresses current challenges and future directions in the application of silk fibers in biomedicine and smart fiber technologies, underscoring silk's potential in driving contemporary technological innovations. The versatility and sustainability of silk fibers position them as pivotal elements in contemporary materials science and technology, fostering the development of next-generation smart materials.

An efficiency function model of segmented gamma scanning for measuring radioactive waste drum.

Segmented gamma scanning (SGS) is a fast and effective method for measuring radioactive waste drum. The efficiency calibration is directly related to the accuracy of reconstructed radioactivity. An efficiency function model and SGS efficiency calibration method are proposed for solving existing SGS efficiency calibration problems such as time lag, limited by experimental sources or difficult to effectively combine with SGS system. The SGS system model is established by the Geant4 to calculate the segment efficiency under different linear attenuation coefficients of medium and gamma energies. The efficiency calibration function is established with the function model and parameters. Waste drum samples are constructed with the polyethylene and point sources 137Cs/60Co to complete SGS experimental measurement, efficiency calibration and radioactivity reconstruction. The result shows that the relative deviation of the reconstructed activity of a single point source at different locations in the drum is -50.48% to 43.69% and it of multi-point sources in a segment or a drum is -27.88% to 3.57%. Experimental results verify the effectiveness of this efficiency function model and SGS calibration method.

Fast adaptive particle spectrum fitting algorithm based on moment-estimated initial parameters.

An algorithm based on moment estimation is presented to determine the initial parameters of the particle spectrum peak shape function for the iteration fitting procedure. The algorithm calculates the mean value, variance, and third-order central moment by using the spectrum peak data, solves the parameters of the fitting function, and then provides them as the initial values to the Levenberg-Marquardt algorithm to ensure convergence and optimized fitting. The effectiveness of the proposed algorithm was tested by gamma and alpha spectra. The algorithm can be used in automated peak curve fitting and spectral analysis.