Single Cell Bioprinting with Ultrashort Laser Pulses

Tissue engineering requires the precise positioning of mammalian cells and biomaterials on substrate surfaces or in preprocessed scaffolds. Although the development of 2D and 3D bioprinting technologies has made substantial progress in recent years, precise, cell-friendly, easy to use, and fast technologies for selecting and positioning mammalian cells with single cell precision are still in need. A new laser-based bioprinting approach is therefore presented, which allows the selection of individual cells from complex cell mixtures based on morphology or fluorescence and their transfer onto a 2D target substrate or a preprocessed 3D scaffold with single cell precision and high cell viability (93–99% cell survival, depending on cell type and substrate). In addition to precise cell positioning, this approach can also be used for the generation of 3D structures by transferring and depositing multiple hydrogel droplets. By further automating and combining this approach with other 3D printing technologies, such as two-photon stereolithography, it has a high potential of becoming a fast and versatile technology for the 2D and 3D bioprinting of mammalian cells with single cell resolution.     

Efficient Visible-to-UV Photon Upconversion Systems Based on CdS Nanocrystals Modified with Triplet Energy Mediators

Developing high-performance visible-to-UV photon upconversion systems based on triplet–triplet annihilation photon upconversion (TTA-UC) is highly desired, as it provides a potential approach for UV light-induced photosynthesis and photocatalysis. However, the quantum yield and spectral range of visible-to-UV TTA-UC based on nanocrystals (NCs) are still far from satisfactory. Here, three different sized CdS NCs are systematically investigated with triplet energy transfer to four mediators and four annihilators, thus substantially expanding the available materials for visible-to-UV TTA-UC. By improving the quality of CdS NCs, introducing the mediator via a direct mixing fashion, and matching the energy levels, a high TTA-UC quantum yield of 10.4% (out of a 50% maximum) is achieved in one case, which represents a record performance in TTA-UC based on NCs without doping. In another case, TTA-UC photons approaching 4 eV are observed, which is on par with the highest energies observed in optimized organic systems. Importantly, the in-depth investigation reveals that the direct mixing approach to introduce the mediator is a key factor that leads to close to unity efficiencies of triplet energy transfer, which ultimately governs the performance of NC-based TTA-UC systems. These findings provide guidelines for the design of high-performance TTA-UC systems toward solar energy harvesting.     

FEMAP二次开发技术介绍与应用

为提高CAE建模和分析效率,对FEMAP常见的几种二次开发方式进行介绍和对比分析.采用Visual Basic.NET语言,通过进程外开发方式,结合工程实例,以船舶水动力网格的数据接口开发详细介绍开发过程.研究表明,FEMAP二次开发技术实用且高效,尤其是进程外开发方式,能为工程实际问题定制快速解决方案,有利于提高工作效率.     

Spatiotemporal Magnetocaloric Microenvironment for Guiding the Fate of Biodegradable Polymer Implants

The degradation behavior of implants is significantly important for bone repair. However, it is still unprocurable to spatiotemporally regulate the degradation of the implants to match bone ingrowth. In this paper, a magneto-controlled biodegradation model is established to explore the degradation behavior of magnetic scaffolds in a magnetothermal microenvironment generated by an alternating magnetic field (AMF). The results demonstrate that the scaffolds can be heated by magnetic nanoparticles (NPs) under AMF, which dramatically accelerated scaffold degradation. Especially, magnetic NPs modified by oleic acid with a better interface compatibility exhibit a greater heating efficiency to further facilitate the degradation. Furthermore, the molecular dynamics simulations reveal that the enhanced motion correlation between magnetic NPs and polymer matrix can accelerate the energy transfer. As a proof-of-concept, the feasibility of magneto-controlled degradation for implants is demonstrated, and an optimizing strategy for better heating efficiency of nanomaterials is provided, which may have great instructive significance for clinical medicine.     

Deep learning-based symbol detection algorithm in IMDD-OOFDM system

In the current research on intensity-modulation and direct-detection optical orthogonal frequency division multiplexing ( IMDD-OOFDM ) system, effective channel compensation is a key factor to improve system performance. In order to improve the efficiency of channel compensation, a deep learning-based symbol detection algorithm is proposed in this paper for IMDD-OOFDM system. Firstly, a high-speed data streams symbol synchronization algorithm based on a training sequence is used to ensure accurate symbol synchronization. Then the traditional channel estimation and channel compensation are replaced by an echo state network (ESN) to restore the transmitted signal. Finally, we collect the data from the system experiment and calculate the signal-to-noise ratio (SNR). The analysis of the SNR optimized by the ESN proves that the ESN-based symbol detection algorithm is effective in compensating nonlinear distortion.     

Self-healing,elastic and deformable novel composite phase change polymer based on thermoplastic elastomer SEBS for wearable devices

Journal of Materials Science - Form-stable composite phase change materials (C-PCMs) prepared by microencapsulation method and porous matrix adsorption method need for compression molding after...