3D-printed polycaprolactone-chitosan based drug delivery implants for personalized administration

Fused deposition molding (FDM) can complete most complex preparation of drug delivery implants to meet the personalized needs of patients. However, the drug activity has strict requirements on processing temperature and preparation method of filaments, the implant also has strict biocompatibility requirements for the materials. In this study, a drug delivery implant was prepared with good biocompatibility, controlled and efficient drug release using FDM printing for personalized administration. Drug-loaded filaments were developed for FDM process by hot-melt extrusion (HME). Polycaprolactone was used as a drug delivery carrier, and ibuprofen as the model drug. Notably, chitosan was dissolved to form controlled and efficient release channels. The printability, changes in physical and chemical properties during HME and FDM processes of the filament, and drug release behavior, mechanism and biocompatibility of the implants were investigated. The results showed that the filament tensile strength decreased with the increase of drug and chitosan content. No obvious degradation and chemical change occurred during the whole process. The drug release efficiency could reach\u3e99% and lasted for 120 h mainly via the diffusion - erosion mechanism. The viability of cells cultured for 24 h in 72 h, 100% implant extract was 75.3%

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data

Wood Nanotechnologies for Transparency, Fire Retardancy and Liquid Separation

In this thesis, wood nanotechnologies for transparent, fire-retardant and hydrophobic/lipophilic wood have been developed. There are two main parts; wood template preparation/processing concepts and materials design using these templates. In the wood template processing part, highly porous nanostructured wood templates are prepared. Relationships between processes and material structures are studied. Three chemical treatment methods are used. Lignin and/or chromophores are removed from cell wall, so that nanoscale pores are formed in the cell wall. For preparation of transparent wood, a lignin-retaining method improves physical properties of the template. The pore structures are characterized by scanning electron microscopy and gas adsorption measurement of specific surface area. The compositions of the templates are characterized. Compared with native wood, these templates have nanoscale porosity which provides opportunity for new types of wood modification. In the materials design part, wood nanotechnologies are used for transparent wood as well as for hydrophobic/lipophilic and fire-retardant wood. Two main strategies are used: i) nanoparticles are embedded inside the cell wall; ii) polymers are impregnated in lumen space, and sometimes also inside the cell wall. The transparent wood is prepared by MMA monomer/oligomer impregnation of lumen space. MMA has similar refractive index to the delignified template, so that scattering is reduced and transparent wood with favorable optical and mechanical properties is obtained. The structure and functional properties are studied. Laminated transparent plywood is designed to modify mechanical properties. Transparent wood and transparent plywood are demonstrated in applications combining loading-bearing properties with optical performance such as luminescent properties. The highly porous wood template cell walls are also impregnated with colloidal montmorillonite clay or epoxy/amine solutions to modify the cell wall and form nanostructured biocomposites. The structure and properties of the two materials are investigated; wood/clay hybrids for flame-retardancy and wood/epoxy biocomposites for oil/water separation.QC 20180221</p

Wood Nanotechnologies for Transparency, Fire Retardancy and Liquid Separation

In this thesis, wood nanotechnologies for transparent, fire-retardant and hydrophobic/lipophilic wood have been developed. There are two main parts; wood template preparation/processing concepts and materials design using these templates. In the wood template processing part, highly porous nanostructured wood templates are prepared. Relationships between processes and material structures are studied. Three chemical treatment methods are used. Lignin and/or chromophores are removed from cell wall, so that nanoscale pores are formed in the cell wall. For preparation of transparent wood, a lignin-retaining method improves physical properties of the template. The pore structures are characterized by scanning electron microscopy and gas adsorption measurement of specific surface area. The compositions of the templates are characterized. Compared with native wood, these templates have nanoscale porosity which provides opportunity for new types of wood modification. In the materials design part, wood nanotechnologies are used for transparent wood as well as for hydrophobic/lipophilic and fire-retardant wood. Two main strategies are used: i) nanoparticles are embedded inside the cell wall; ii) polymers are impregnated in lumen space, and sometimes also inside the cell wall. The transparent wood is prepared by MMA monomer/oligomer impregnation of lumen space. MMA has similar refractive index to the delignified template, so that scattering is reduced and transparent wood with favorable optical and mechanical properties is obtained. The structure and functional properties are studied. Laminated transparent plywood is designed to modify mechanical properties. Transparent wood and transparent plywood are demonstrated in applications combining loading-bearing properties with optical performance such as luminescent properties. The highly porous wood template cell walls are also impregnated with colloidal montmorillonite clay or epoxy/amine solutions to modify the cell wall and form nanostructured biocomposites. The structure and properties of the two materials are investigated; wood/clay hybrids for flame-retardancy and wood/epoxy biocomposites for oil/water separation.QC 20180221</p

Wood Nanotechnologies for Transparency, Fire Retardancy and Liquid Separation

In this thesis, wood nanotechnologies for transparent, fire-retardant and hydrophobic/lipophilic wood have been developed. There are two main parts; wood template preparation/processing concepts and materials design using these templates. In the wood template processing part, highly porous nanostructured wood templates are prepared. Relationships between processes and material structures are studied. Three chemical treatment methods are used. Lignin and/or chromophores are removed from cell wall, so that nanoscale pores are formed in the cell wall. For preparation of transparent wood, a lignin-retaining method improves physical properties of the template. The pore structures are characterized by scanning electron microscopy and gas adsorption measurement of specific surface area. The compositions of the templates are characterized. Compared with native wood, these templates have nanoscale porosity which provides opportunity for new types of wood modification. In the materials design part, wood nanotechnologies are used for transparent wood as well as for hydrophobic/lipophilic and fire-retardant wood. Two main strategies are used: i) nanoparticles are embedded inside the cell wall; ii) polymers are impregnated in lumen space, and sometimes also inside the cell wall. The transparent wood is prepared by MMA monomer/oligomer impregnation of lumen space. MMA has similar refractive index to the delignified template, so that scattering is reduced and transparent wood with favorable optical and mechanical properties is obtained. The structure and functional properties are studied. Laminated transparent plywood is designed to modify mechanical properties. Transparent wood and transparent plywood are demonstrated in applications combining loading-bearing properties with optical performance such as luminescent properties. The highly porous wood template cell walls are also impregnated with colloidal montmorillonite clay or epoxy/amine solutions to modify the cell wall and form nanostructured biocomposites. The structure and properties of the two materials are investigated; wood/clay hybrids for flame-retardancy and wood/epoxy biocomposites for oil/water separation.QC 20180221</p

Transparent wood for functional and structural applications

Optically transparent wood combines mechanical performance with optical functionalities is an emerging candidate for applications in smart buildings and structural optics and photonics. The present review summarizes transparent wood preparation methods, optical and mechanical performance, and functionalization routes, and discusses potential applications. The various challenges are discussed for the purpose of improved performance, scaled-up production and realization of advanced applications. This article is part of a discussion meeting issue 'New horizons for cellulose nanotechnology'.QC 20180111</p

Flueggea acicularis (Phyllanthaceae), a narrow endemic species rediscovered in central China

Flueggea acicularis (Phyllanthaceae) is endemic to the karst region of central China. Male specimens of this species were first collected in 1908. In 1989, female plants of F. acicularis were found for the first time, but misidentified as a new species. Throughout this period the male plants of F. acicularis were mismatched with female plants of other species, and male plants had not been collected since 1908. Then, in March, 2009, the authors rediscovered a wild population of F. acicularis consisting of both male and female plants in Wuxi county, Chongqing municipality, China. Based on field investigation and examination of specimens, we matched the correct female and male plants of this species for the first time since its initial publication a century ago. A complete and accurate morphological description, distribution, habitat and phenology of this species are also provided. Furthermore, the conservation status of F. acicularis is assessed as “Near Threatened” (NT) according to the IUCN Red List criteria

Two-Period Reputation Model of Knowledge Sharing between Enterprises Based on Signal Game Analysis

Knowledge sharing between enterprises is an important way to obtain external research and development (R&D) resources and keep competitiveness. This paper used a reputation model based on a two-period signal game to explore knowledge-sharing micromechanism between enterprises and key influencing factors of enterprises. The results show that reputation effects are an important mechanism that will make knowledge sharing between enterprises operate effectively. Motivated by reputation effects, even those noncooperative enterprises continue to pretend to be cooperative enterprises for knowledge sharing before the end of the game. Finally, we adopt the analytical methods and conclusions given by the model in this article to analyze opportunistic problems in knowledge sharing among cooperative enterprises and put forward some valuable suggestions on the conditions for the effective use of corporate reputation effects

Flueggea acicularis (Phyllanthaceae), a narrow endemic species rediscovered in central China

Flueggea acicularis (Phyllanthaceae) is endemic to the karst region of central China. Male specimens of this species were first collected in 1908. In 1989, female plants of F. acicularis were found for the first time, but misidentified as a new species. Throughout this period the male plants of F. acicularis were mismatched with female plants of other species, and male plants had not been collected since 1908. Then, in March, 2009, the authors rediscovered a wild population of F. acicularis consisting of both male and female plants in Wuxi county, Chongqing municipality, China. Based on field investigation and examination of specimens, we matched the correct female and male plants of this species for the first time since its initial publication a century ago. A complete and accurate morphological description, distribution, habitat and phenology of this species are also provided. Furthermore, the conservation status of F. acicularis is assessed as “Near Threatened” (NT) according to the IUCN Red List criteria

Towards centimeter thick transparent wood through interface manipulation

Transparent wood is an attractive structural material for energy-saving buildings due to its high optical transmittance, good thermal insulation, and high toughness. However, thick highly transparent wood is challenging to realize. In the current work, highly transparent wood (1.5 mm) with a transmittance of 92%, close to that of pure PMMA (95%), is demonstrated. The high transmittance was realized by interface manipulation through acetylation of wood template. Both experiments and electromagnetic modeling support that the improved transmittance is mainly due to elimination of interface debonding gap. By applying this method, a centimeter-thick transparent wood structure was obtained. The transparent wood could be used as a substrate for an optically tunable window by laminating a polymer dispersed liquid crystal (PDLC) film on top. The techniques demonstrated are a step towards the replacement of glass in smart windows and smart buildings