A High-Power Continuous-Wave Mid-Infrared Optical Parametric Oscillator Module

Selecting appropriate cellulose morphology to enhance the nitrogen content of nitrocellulose

This study aims to determine the effect of cellulose morphology on enhancing the nitration of cellulose to achieve nitrocellulose with a high nitrogen content. Cotton linter was employed as a point of reference, and Luffa cylindrica and coffee pulp cellulose were used for comparison. Luffa cylindrica and coffee pulp cellulose were used considering their distinctive morphological characteristics compared to cotton linter. They were nitrated at room temperature for 60 minutes using a mixture of technical grade nitric acid and sulfuric acid with a nitric acid : sulfuric acid ratio of 1 : 3 and cellulose : nitric acid ratio of 1 : 45. The results showed that luffa cellulose is similar in characteristics to cotton linter, which exhibits short, thin-walled tubular ribbon-like microfibrils with 75.09% crystallinity and a specific surface area of 0.70 m2 g-1. Nitration of luffa cellulose resulted in nitrocellulose with a nitrogen content as high as 13.67%, which is higher than that of cotton linter with a value of 13.49%. This value of nitrogen content was high enough to be applied even in military-grade applications. Morphology was revealed as the most influential characteristic of nitration, which allows for the preparation of nitrocellulose with qualities similar to cotton linter-based nitrocellulose. Thus, it was demonstrated that nitrocellulose with a high nitrogen content could be achieved despite using a technical grade nitration reagent by selecting cellulose with an appropriate morphology.

In situ stress estimation in quantitative micro-elastography

In quantitative micro-elastography (QME), a pre-characterized compliant layer with a known stress-strain curve is utilized to map stress at the sample surface. However, differences in the boundary conditions of the compliant layer when it is mechanically characterized and when it is used in QME experiments lead to inconsistent stress estimation and consequently, inaccurate elasticity measurements. Here, we propose a novel in situ stress estimation method using an optical coherence tomography (OCT)-based uniaxial compression testing system integrated with the QME experimental setup. By combining OCT-measured axial strain with axial stress determined using a load cell in the QME experiments, we can estimate in situ stress for the compliant layer, more accurately considering its boundary conditions. Our proposed method shows improved accuracy, with an error below 10%, compared to 85% using the existing QME technique with no lubrication. Furthermore, demonstrations on hydrogels and cells indicate the potential of this approach for improving the characterization of the micro-scale mechanical properties of cells and their interactions with the surrounding biomaterial, which has potential for application in cell mechanobiology.

Electrospun wound dressings containing bioactive natural products: physico-chemical characterization and biological assessment

Background

Current research on skin tissue engineering has been focusing on novel therapies for the effective management of chronic wounds. A critical aspect is to develop matrices that promote growth and uniform distribution of cells across the wound area, and at the same time offer protection, as well as deliver drugs that help wound healing and tissue regeneration. In this context, we aimed at developing electrospun scaffolds that could serve as carriers for the bioactive natural products alkannin and shikonin (A/S).

Methods

A series of polymeric nanofibers composed of cellulose acetate (CA) or poly(ε-caprolactone) (PCL) and varying ratios of a mixture of A/S derivatives, has been successfully fabricated and their physico-chemical and biological properties have been explored.

Results

Scanning electron microscopy revealed a uniform and bead-free morphology for CA scaffolds, while for PCL beads along the fibers were observed. The average diameters for all nanofibers ranged between 361 ± 47 and 487 ± 88 nm. During the assessment of physicochemical characteristics, CA fiber mats exhibited a more favored profile, while the assessment of the biological properties of the scaffolds showed that CA samples containing A/S mixture up to 1 wt.% achieved to facilitate attachment, survival and migration of Hs27 fibroblasts. With respect to the antimicrobial properties of the scaffolds, higher drug-loaded (1 and 5 wt.%) samples succeeded in inhibiting the growth of Staphylococcus epidermidis and S. aureus around the edges of the fiber mats. Finally, carrying out a structure-activity relationship study regarding the biological activities (fibroblast toxicity/proliferation and antibacterial activity) of pure A/S compounds – present in the A/S mixture – we concluded that A/S ester derivatives and the dimeric A/S augmented cell proliferation after 3 days, whereas shikonin proved to be toxic at 500 nM and 1 μM and alkannin only at 1 μM. Additionally, alkannin, shikonin and acetyl-shikonin showed more pronounced antibacterial properties than the other esters, the dimeric derivative and the A/S mixture itself.

Conclusions

Taken together, these findings indicate that embedding A/S derivatives into CA nanofibers might be an advantageous drug delivery system that could also serve as a potential candidate for biomedical applications in the field of skin tissue engineering.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40824-021-00223-9.

Comparison of Novel Seismic Protection Devices to Attenuate the Earthquake Induced Energy

This study addresses an alternative use of viscous dampers (VDs) associated with buckling restrained braces (BRBs) as innovative seismic protection devices. For this purpose, 4-, 8- and 12-story steel bare frames were designed with 6.5 m equal span length and 4 m story height. Thereafter, they were seismically improved by mounting the VDs and BRBs in three patterns, namely outer bays, inner bays, and all bays over the frame heights. The structures were modeled using SAP 2000 software and evaluated by the nonlinear time history analyses subjected to the six natural ground motions. The seismic responses of the structures were investigated for the lateral displacement, interstory drift, absolute acceleration, maximum base shear, and time history of roof displacement. The results clearly indicated that the VDs and BRBs reduced seismic demands significantly compared to the bare frame. Moreover, the all-bay pattern performed better than the others.

A compact and high efficiency intracavity OPO based on periodically poled lithium niobate

We demonstrate a compact, high efficiency and widely tunable intracavity singly resonant optical parametric oscillator (IC-OPO) based on multichannel periodically poled lithium niobate (PPLN). The IC-OPO is composed of 808 nm pump laser diode (LD), Nd:YVO₄ laser and linear OPO. The continuous-wave (CW) mid-infrared (MIR) output laser is tunable from 2.25 to 4.79 μm. The maximum output power exceeds 1.08 W at 3.189 μm at 9.1 W LD pump power and the conversion efficiency is 11.88%. We also build up a prototype with volume of 145 × 85 × 42.5 mm³ and its total weight is less than 2 kg. The measured power stability is 1.3% Root Meat Square (RMS) for a 3 h duration under simulated high temperature conditions of 40 °C. RMS is 2.6% for a 4 h duration when simulated temperature is − 40 °C.

Multiclass EEG signal classification utilizing Rényi min-entropy-based feature selection from wavelet packet transformation

This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI). Usually, wavelet packet transformation (WPT) is extensively used for feature extraction from electro-encephalogram (EEG) signals. For the case of multiple-class problem, classification accuracy solely depends on the effective feature selection from the WPT features. In conventional approaches, Shannon entropy and mutual information methods are often used to select the features. In this work, we have shown that our proposed Rényi min-entropy-based approach outperforms the conventional methods for multiple EEG signal classification. The dataset of BCI competition-IV (contains 4-class motor imagery EEG signal) is used for this experiment. The data are preprocessed and separated as the classes and used for the feature extraction using WPT. Then, for feature selection Shannon entropy, mutual information, and Rényi min-entropy methods are applied. With the selected features, four-class motor imagery EEG signals are classified using several machine learning algorithms. The results suggest that the proposed method is better than the conventional approaches for multiple-class BCI.

A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.