Four-armed poly(ethylene glycol) (PEG)s are essential hydrophilic polymers extensively utilized to prepare PEG hydrogels, which are valuable tissue scaffolds. When hydrogels are used in vivo, they eventually dissociate due to the cleavage of the backbone structure. When the cleavage occurs at the cross-linking point, the hydrogel elutes as an original polymer unit, i.e., four-armed PEG. Although four-armed PEGs have been utilized as subcutaneously implanted biomaterials, the diffusion, biodistribution, and clearance behavior of four-armed PEG from the skin are essential. This paper investigates time-wise diffusion from the skin, biodistribution to distant organs, and clearance of fluorescence-labeled four-armed PEGs with molecular weight (Mw) ranging from 5-40 kg/mol subcutaneously injected into the back of mice. Changes over time indicated that the fate of subcutaneously injected PEGs is Mw-dependent. Four-armed PEGs with Mw less than or equal to 10 kg/mol gradually diffused to deep adipose tissue beneath the injection site and distributed dominantly to distant organs, such as the kidney. PEGs with Mw greater than or equal to 20 kg/mol stagnated in the skin and deep adipose tissue, and were mainly delivered to the heart, lung, and liver. The fundamental understanding of the Mw-dependent behavior of four-armed PEGs is beneficial for preparing biomaterials using PEGs, providing a reference in the field of tissue engineering.

A bio-inspired two-mode structural health monitoring (SHM) system based on the Naïve Bayes (NB) classification method is discussed in this paper. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype was developed, which consists of three separate modules namely, the input sensing module, the transmission module, and the SHM platform. The vibration data is first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.