In this study, we demonstrate a simple approach to fabricate a high-performance random laser from the natural inverse photonic glass structure of Artemia eggshells. Herein, the three-dimensional structures of Artemia eggshells provide an ideal scattering medium with a significantly high-reflectance stopband which facilitates resonance feedback for random lasing action. By doping organic dye molecules into the Artemia eggshells, random lasers are realized by optical pumping with a threshold of 79 μJ/mm2, and a quality (Q) factor of 2328. In comparison with other works on random lasers from natural photonic crystals such as butterfly wings, our random lasers demonstrate a significantly lower lasing threshold and a comparable Q factor. Our results indicate that the natural inverse photonic glass structure is not only served as an effective scattering medium for random lasing but also paves a novel approach in designing and fabricating bio-controlled photonic devices.

This paper presents to study the performance of machine learning techniques consisting of Multivariate Adaptive Regression Spline (MARS), Feed Forward Neural Network-Back Propagation (FFNN-BP), and Decision Tree Regression (DTR) for estimating physico-chemical properties groundwater in coastal plain area in Vinh Linh and Gio Linh districts of Quang Tri province of Vietnam. With the amount of 290 groundwater samples collected in two districts, this study has identified three main elements CO2, Ca, CaCO3 for simulation. Quantitative analysis results have shown that these three components are such as CaCO3 with from 0 to 25.8 mg/l, Ca from 0 to 87.55 mg/l and CO2 from 0 to 12 mg/l. In the present examination, groundwater quality index (GQI) values and their representative categories have been referred by the Vietnam Groundwater Standard (QCVN01). Furthermore, the statistical accuracy parameters were used to compare among models. To deploy the FFNN-BP and DTR, different types of transfer and kernel functions were tested, respectively. Determining the results of MARS, FFNN-BP and DTR showed that three models have suitable carrying out for forecasting water quality components. Comparison of outcomes of MARS model with FFNN-BP, DTR models indicated that this model has good performance for forecasting the elements of water quality, its level of accuracy was slightly more than other. To assess the accurate values of the models according to the measurement parameters for training phase illustrated that order models were MARS to give the best result, followed by DTR and finally FFNN-BP, respectively.

Adjustably biodegradable materials have gained much attention in biomedical applications. Among of them, various hydrogel-based scaffolds have applied for regenerating soft and hard tissues. In this study, according to differently biological properties of gelatin or chitosan as well as biphasic calcium phosphate nanoparticles (BCPNPs), several injectable nanocomposite hydrogels (INgel) were enzymatically fabricated from a phenolic chitosan derivative (PCD), phenolic gelatin derivative (PGD) and BCPNPs. According the change of H2O2 concentration with follow-up the time, the in situ formation of INgel was varied from 35 to 80 s. The degradation rate of the nanocomposite materials significantly related to in presence of collagenase that expended from 3 days to over one month depending on amount of the formulated PCD. The BCPNPs-encapsulated PCD-PGD INgel enhanced mineralization in the simulated biofluid. Fluorescent cytotoxicity assay indicated that the INgel was fabricated from a higher amount of the PGD resulting in a significant proliferation of bone marrow mesenchymal stem cells. These preliminary results exhibited a great potential of the INgel for bone regeneration.

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