Antibacterial, injectable, and adhesive hydrogel promotes skin healing

With the development of material science, hydrogels with antibacterial and wound healing properties are becoming common. However, injectable hydrogels with simple synthetic methods, low cost, inherent antibacterial properties, and inherent promoting fibroblast growth are rare. In this paper, a novel injectable hydrogel wound dressing based on carboxymethyl chitosan (CMCS) and polyethylenimine (PEI) was discovered and constructed. Since CMCS is rich in -OH and -COOH and PEI is rich in -NH2, the two can interact through strong hydrogen bonds, and it is theoretically feasible to form a gel. By changing their ratio, a series of hydrogels can be obtained by stirring and mixing with 5 wt% CMCS aqueous solution and 5 wt% PEI aqueous solution at volume ratios of 7:3, 5:5, and 3:7. Characterized by morphology, swelling rate, adhesion, rheological properties, antibacterial properties, in vitro biocompatibility, and in vivo animal experiments, the hydrogel has good injectability, biocompatibility, antibacterial (Staphylococcus aureus: 56.7 × 107 CFU/mL in the blank group and 2.5 × 107 CFU/mL in the 5/5 CPH group; Escherichia coli: 66.0 × 107 CFU/mL in the blank group and 8.5 × 107 CFU/mL in the 5/5 CPH group), and certain adhesion (0.71 kPa in the 5/5 CPH group) properties which can promote wound healing (wound healing reached 98.02% within 14 days in the 5/5 CPH group) and repair of cells with broad application prospects

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

PCMs heat transfer performance enhancement with expanded graphite and its thermal stability

AbstractThis work focuses on developing a high performance phase change materials (PCMs) adding with a few expanded graphite (EG). A series of composite phase change materials (paraffin) with various EG concentration are formulated in vacuum condition and ambient pressure respectively. The heat fusion of composite PCMs is measured by DSC. A rig for PCMs heat transfer performance test is built in lab. The heat transfer performance test of composites PCMs are carried out. The heat storage and release processed of pure PCMs and composite significantly improved after EG adding. The composited PCMs formulated in vacuum condition also presents better heat transfer performance. It shows composite PCMs can be well mixed in vacuum. Due to the repeated heating and cooling in PCMs application, thermal stability test is carried out in this work. PCMs are putting in water bath with temperature controlling by cyclic phase change to quantify the rate of degradation heat fusion and thermal charge/discharge process. After cycling 60 times, up to 2% heat fusion decrement is observed. Heat transfer performance studies also show that there is little change. In brief, PCMs with EG has high thermal performance for thermal energy storage process

J. Nanopart. Res.

This work concerns rheological and frictional behaviour of lubricating oils containing platelet molybdenum disulfide (MoS2) nanoparticles (average diameter similar to 50 nm; single layer thickness similar to 3 nm). Stable nano-MoS2 lubricants were formulated and measured for their rheological behaviour and tribological performance. Rheological experiments showed that the nano-MoS2 oils were non-Newtonian following the Bingham plastic fluid model. The viscosity data fitted the classic Hinch-Leal (H-L) model if an agglomeration factor of 1.72 was introduced. Tribological experiments indicated that the use of MoS2 nanoparticles could enhance significantly the tribological performance of the base lubricating oil (reduced frictional coefficient, reduced surface wear and increased stability). Scanning electron microscopy, laser confocal microscope and x-ray energy dispersive spectroscopy analyses suggested that the reduced frictional coefficient and surface wear be associated with surface patching effects. Such patching effects were shown to depend on the concentration of MoS2 nanoparticles, and an effective patching required a concentration over approximately 1 wt%. The increased stability could be attributed to the enhanced heat transfer and lubricating oil film strength due to the presence of nanoparticles.This work concerns rheological and frictional behaviour of lubricating oils containing platelet molybdenum disulfide (MoS2) nanoparticles (average diameter similar to 50 nm; single layer thickness similar to 3 nm). Stable nano-MoS2 lubricants were formulated and measured for their rheological behaviour and tribological performance. Rheological experiments showed that the nano-MoS2 oils were non-Newtonian following the Bingham plastic fluid model. The viscosity data fitted the classic Hinch-Leal (H-L) model if an agglomeration factor of 1.72 was introduced. Tribological experiments indicated that the use of MoS2 nanoparticles could enhance significantly the tribological performance of the base lubricating oil (reduced frictional coefficient, reduced surface wear and increased stability). Scanning electron microscopy, laser confocal microscope and x-ray energy dispersive spectroscopy analyses suggested that the reduced frictional coefficient and surface wear be associated with surface patching effects. Such patching effects were shown to depend on the concentration of MoS2 nanoparticles, and an effective patching required a concentration over approximately 1 wt%. The increased stability could be attributed to the enhanced heat transfer and lubricating oil film strength due to the presence of nanoparticles

Nearest Neighbor Classifier Embedded Network for Active Learning

Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability of the discriminative classifier (the softmax classifier) is questionable when there is a significant distribution bias between the labeled set and the unlabeled set. In this paper, we attempt to replace the softmax classifier in deep neural network with a nearest neighbor classifier, considering its progressive generalization ability within the unknown sub-space. Our proposed active learning approach, termed nearest Neighbor Classifier Embedded network (NCE-Net), targets at reducing the risk of over-estimating unlabeled samples while improving the opportunity to query informative samples. NCE-Net is conceptually simple but surprisingly powerful, as justified from the perspective of the subset information, which defines a metric to quantify model generalization ability in active learning. Experimental results show that, with simple selection based on rejection or confusion confidence, NCE-Net improves state-of-the-arts on image classification and object detection tasks with significant margins