Tannic acid adsorption properties of cellulose nanocrystalline/fish swim bladder gelatin composite sponge.

Foods and beverages with excessive tannins acid (TA) content taste astringent and bitter. The overconsumption of TA could result in nutritional and digestive problems. In this study, the cellulose nanocrystals (CNC)/fish swim bladder gelatin (FG) composite sponge was prepared with glutaraldehyde as a crosslinking agent. The TA adsorption performance of the sponge was discussed. The freeze-dried CNC/FG composite sponge had a porous network structure. CNC was combined into the FG matrix as a reinforcing phase. The mechanical strength, thermal stability, and swelling properties of the composite sponge were improved with the addition of an appropriate amount of CNC. Although CNC decreased the porosity of composite sponge, the increase in active adsorption sites resulted in an overall positive effect on its TA adsorption properties. Under the optimal adsorption conditions, the TA removal rate of 1.0 % CNC composites reached 80.4 %. Furthermore, the sponge retained a TA removal rate of 54 % after five cycles of adsorption and desorption using 50 % ethanol. The results demonstrated that CNC/FG composite sponge has application potential in the field of adsorption materials for TA.

3D Printing of Solar Crystallizer with Polylactic Acid/Carbon Composites for Zero Liquid Discharge of High-Salinity Brine.

Zero liquid discharge (ZLD) is a technique for treating high-salinity brine to obtain freshwater and/or salt using a solar interface evaporator. However, salt accumulation on the surface of the evaporator is a big challenge to maintaining stable water evaporation. In this study, a simple and easy-to-manufacture evaporator, also called a crystallizer, was designed and fabricated by 3D printing. The photothermal layer printed with polylactic acid/carbon composites had acceptable light absorption (93%) within the wavelength zone of 250 nm-2500 nm. The micron-sized voids formed during 3D printing provided abundant water transportation channels inside the crystallizer. After surface hydrophilic modification, the crystallizer had an ultra-hydrophilic channel structure and gravity-assisted salt recovery function. The results revealed that the angles between the photothermal layers affected the efficacy of solar evaporation and the yield of solid salt. The crystallizer with the angle of 90° between two photothermal layers could collect more solid salt than the three other designs with angles of 30°, 60°, and 120°, respectively. The crystallizer has high evaporation and salt crystallization efficiency in a high-salinity brine environment, which is expected to have application potentials in the zero liquid discharge of wastewater and valuable salt recovery.

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data.

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.

Effects of ultrafiltration on Co-Metal Organic Framework/pre-hydrolysis solution carbon materials for supercapacitor energy storage.

Here, a Co-Metal Organic Framework/pre-hydrolysis (Co-MOF/pre-hydrolysis) solution carbon material is prepared by a mild and environmentally-friendly hydrothermal carbonization technique using a pulping pre-hydrolysis solution as the raw material and Co-MOF as the metal dopant. The stable hollow structure provide sufficient space for particle shrinkage and expansion, while the low density and large specific surface area of the long, hairy tentacle structure provide a greater contact area for ions, which shorten the transmission path of electrons and charges. The materials exhibit excellent specific capacitance (400 F/g, 0.5 A/g) and stability (90%, 10,000 cycles). The Change of different concentration ratios in the structures significantly affect the electrochemical performance. The specific surface area of the carbon materials prepared by ultra-filtration increased, but the specific surface area decrease as ultrafiltration concentration increase. The specific capacitance decrease from 336 F/g for C-ZIF-67-1/3 volume ultrafiltration to 258 F/g for C-ZIF-67-1/5 ultrafiltration. The results indicate that energy storage by the carbon materials relied on a synergistic effect between their microporous and mesoporous structures. The micropores provide storage space for the transmission of ions, while the mesopores provide ion transport channels. The separation of large and small molecules after ultrafiltration concentration limit the ion transmission and energy storage of the pores.

Fire Behavior and Failure Model of Multilayered Wood Flour/HDPE/Polycarbonate Composites with a Sandwich Structure.

The flame retardancy of wood-polymer composites significantly affects their potential applications. Thus, multilayered wood flour/high-density polyethylene (HDPE)/polycarbonate (PC) composites were prepared via thermocompression to improve the fire retardancy of wood-polymer composites in this paper. Thermal degradation behavior, flame retardancy, and flexural strengths of the resulting composites were investigated using a thermogravimetric analysis, cone calorimetry, and mechanical testing machine, respectively. Results revealed that the boric acid treatment reduced the heat release rate and total heat release of the wood flour/HDPE composites and increased their mass of residues. However, boric acid reduced the flexural strength of the resulting composites. The combustion test indicated that PC cap layers suppressed the combustion of the resulting composites via the formation of carbon layers. Adding PC layers reduced heat release and increased the flexural strength of the resulting composites. Finally, the failure mode of the multilayered wood flour/HDPE/PC composites in the three-point flexural test was simulated by finite element analysis.

Copper-Doped Carbon Nanodots with Superior Photocatalysis, Directly Obtained from Chromium-Copper-Arsenic-Treated Wood Waste.

An ecofriendly approach was developed for preparing copper-doped carbon dots (CDs) with superior photocatalysis using chromium-copper-arsenic (CCA)-treated wood waste as a precursor. Original wood (W-CDs), CCA-treated wood (C-CDs), and bioremediation CCA wood (Y-CDs) were used as the precursors. The chemical composition and structural, morphological, and optical properties, as well as the photocatalytic ability of the synthesized CDs varied with wood type. The C-CDs and W-CDs had similar characteristics: quasispherical in shape and with a diameter of 2 to 4.5 nm. However, the Y-CDs particles were irregular and stacked together, with a size of 1.5-3 nm. The presence of nitrogen prevented the formation of an aromatic structure for those CDs fabricated from bioremediation CCA wood. The three synthesized CDs showed a broad absorption peak at 260 nm and a weak absorption peak at 320 nm. Proof of the model study for the fabrication of luminescent CDs from CCA wood waste for bioimaging was provided. The degradation rate of CD photocatalytic MB was 97.8% for 30 min. Copper doping gives the CDs electron acceptor properties, improving their photocatalytic efficiency. This study provides novel ways to prepare nanomaterials from decommissioned wood as a nontoxic and low-cost alternative to fluorescent dots.

The emerging development of solar evaporators in materials and structures.

To tackle the increasingly severe freshwater resource scarcity problem, desalination using solar evaporators is potentially an effective approach. This article reviews the research progress of solar evaporators in recent years, including materials, structures, and performance evaluations. In terms of material research, this article introduces the mechanism of photothermal conversion of metallic, semiconductor, polymeric, and carbon-based materials and their applications in the research of solar evaporators. The structure design of solar evaporators that can improve the photothermal conversion efficiency and water transport efficiency are summarized in detail. Regarding the evaluation of the evaporator performance, common evaluation methods for steam efficiency and environmental benefits of solar evaporators were introduced. Finally, this article analyzes the current problems of evaporators and proposes a prospect for the development of new types of high-efficiency evaporators.

Rice Hull-Derived Carbon for Supercapacitors: Towards Sustainable Silicon-Carbon Supercapacitors.

A simple and effective mixing carbonization-activation process was developed to prepare rice hull-derived porous Si-carbon materials. The morphologies and pore structures of the materials were controlled effectively without any loading or additions at various carbonization temperatures. The structures of the samples changed from large pores and thick walls after 800 ∘C carbonization to small pores and thin walls after 1000 ∘C carbonization. An additional alkali activation-carbonization process led to coral reef-like structures surrounded by squama in the sample that underwent 900 ∘C carbonization (Act-RH-900). This optimal material (Act-RH-900) had a large specific surface area (768 m2 g-1), relatively stable specific capacitance (150.8 F g-1), high energy density (31.9 Wh kg-1), and high-power density (309.2 w kg-1) at a current density of 0.5 A g-1 in 1 M KOH electrolyte, as well as a good rate performance and high stability (capacitance retention > 87.88% after 5000 cycles). The results indicated that Act-RH-900 is a promising candidate for capacitive applications. This work overcomes the restrictions imposed by the complex internal structure of biomass, implements a simple reaction environment, and broadens the potential applicability of biomass waste in the field of supercapacitors.

IsoExplorer: an isosurface-driven framework for 3D shape analysis of biomedical volume data.

ABSTRACT: The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods.

Fire Retardancy, Water Absorption, and Viscoelasticity of Borated Wood-Polycarbonate Biocomposites.

Demand for high-performance biocomposites is increasing due to their ease of processing, low environmental impact, and in-service performance. This study investigated the effect of boric acid modification of wood flour on polycarbonate (PC) wood composites' thermal stability, fire retardancy, water absorption, and creep behavior. The composites' fire retardancy increased with increasing wood flour content, and their char residue increased by 102.3% compared to that of pure PC. However, the water absorption of the resulting composites increased due to the hydroxyl groups of the wood flour. Wood flour also improved the composites' anti-creep properties. The excellent fire retardancy and anti-creep properties of wood-PC composites expand their use in the construction sector.

ALA-Net: Adaptive Lesion-Aware Attention Network for 3D Colorectal Tumor Segmentation.

Accurate and reliable segmentation of colorectal tumors and surrounding colorectal tissues on 3D magnetic resonance images has critical importance in preoperative prediction, staging, and radiotherapy. Previous works simply combine multilevel features without aggregating representative semantic information and without compensating for the loss of spatial information caused by down-sampling. Therefore, they are vulnerable to noise from complex backgrounds and suffer from misclassification and target incompleteness-related failures. In this paper, we address these limitations with a novel adaptive lesion-aware attention network (ALA-Net) which explicitly integrates useful contextual information with spatial details and captures richer feature dependencies based on 3D attention mechanisms. The model comprises two parallel encoding paths. One of these is designed to explore global contextual features and enlarge the receptive field using a recurrent strategy. The other captures sharper object boundaries and the details of small objects that are lost in repeated down-sampling layers. Our lesion-aware attention module adaptively captures long-range semantic dependencies and highlights the most discriminative features, improving semantic consistency and completeness. Furthermore, we introduce a prediction aggregation module to combine multiscale feature maps and to further filter out irrelevant information for precise voxel-wise prediction. Experimental results show that ALA-Net outperforms state-of-the-art methods and inherently generalizes well to other 3D medical images segmentation tasks, providing multiple benefits in terms of target completeness, reduction of false positives, and accurate detection of ambiguous lesion regions.

LRVRG: a local region-based variational region growing algorithm for fast mandible segmentation from CBCT images.

OBJECTIVES: To segment the mandible from cone-beam computed tomography (CBCT) images efficiently and accurately for the 3D mandible model is essential for subsequent research and diagnosis. METHODS: This paper proposes a local region-based variational region growing algorithm, which integrates local region and shape prior to segment the mandible accurately. Firstly, we select initial seeds in the CBCT image and then calculate candidate point sets and the local region energy function of each point. If a point reduces the energy, it is selected to be a pixel of the foreground region. By multiple iterations, the mandible segmentation of the slice can be obtained. Secondly, the segmented result of the previous slice is adopted as the shape prior to the next slice until all of the slices in CBCT are segmented. At last, the final mandible model is reconstructed by the Marching Cubes algorithm. RESULTS: The experimental results on CBCT datasets illustrate the LRVRG algorithm can obtain satisfied 3D mandible models from CBCT images and it can solve the fuzzy problem effectively. Furthermore, quantitative comparisons with other methods demonstrate the proposed method achieves the state-of-the-art performance in mandible segmentation. CONCLUSIONS: Experiments demonstrate that our method is efficient and accurate for the mandible model segmentation.

Preparation and Properties of Microcrystalline Cellulose/Fish Gelatin Composite Film.

As a natural macromolecule-based biomaterial, fish gelatin is used in medical materials for its low pathogen infection risk. However, because of poor mechanical properties, its application has been limited. In this study, microcrystalline cellulose-reinforced fish gelatin (FG/MCC) composite films were prepared with a biological cross-linking agent (genipin) under ultrasonic treatment. SEM micrographs showed that the smooth microstructure of FG film became increasingly disordered with the addition of MCC. The infrared spectrum analysis (FTIR) demonstrated the existence of hydrogen bond interaction between MCC and FG. Compared with the pure FG film, the tensile strength (TS) and modulus of elasticity (MOE) of composite films with MCC were improved, and the elongation at break (EAB) and swelling ratios (SR) were decreased. Ultrasonic treatment could further improve TS, MOE, and SR. When the composite film was prepared with 15% MCC and treated with ultrasound, the TS and MOE increased by 115% and 227%, respectively, while the EAB decreased by 35% and the SR decreased by 4% in comparison with pure FG films. Thermo-gravimetric analysis (TGA) showed that the FG/MCC composite films were stable below 100 °C. The above results indicate that the FG/MCC films have optimistic application prospects in the biomedical field.

Deep Learning for Accurate Diagnosis of Liver Tumor Based on Magnetic Resonance Imaging and Clinical Data.

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914-0.979 vs. 0.951; 0.919-0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960-1.000), metastatic tumors (0.998; 0.989-1.000), and other primary malignancies (0.963; 0.896-1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.

Quantitative Assessment of Hippocampal Tau Pathology in AD and PART.

To quantitatively assess the distribution pattern of hippocampal tau pathology in Alzheimer's disease (AD) and primary age-related tauopathy (PART), we investigated the distribution of phosphorylated tau protein (AT8) in 6 anatomically defined subregions of the hippocampal formation and developed a mathematical algorithm to compare the patterns of tau deposition in PART and AD. We demonstrated regional patterns of selective vulnerability as distinguishing features of PART and AD in functionally relevant structures of the hippocampus. In AD cases, tau pathology was high in both CA1 and subiculum, followed by CA2/3, entorhinal cortex (EC), CA4, and dentate gyrus (DG). In PART, the severity of tau pathology in CA1 and subiculum was high, followed by EC, CA2/3, CA4, and DG. There are significant differences between sector DG and CA1, DG and subiculum in both AD and PART.

Exploring Evolution of Dynamic Networks via Diachronic Node Embeddings.

Dynamic networks evolve with their structures changing over time. It is still a challenging problem to efficiently explore the evolution of dynamic networks in terms of both their structural and temporal properties. In this paper, we propose a visual analytics methodology to interactively explore the temporal evolution of dynamic networks in the context of their structure. A novel diachronic node embedding method is first proposed to learn latent representations of the structural and temporal features of nodes in a vector space. Diachronic node embeddings are then used to discover communities with similar structural proximity and temporal evolution patterns. A visual analytics system is designed to enable users to visually explore the evolutions of nodes, communities, and the network as a whole in terms of their structural and temporal properties. We evaluate the effectiveness of our method using artificial and real-world dynamic networks and comparisons with previous methods.

An Additive Manufacturing Method Using Large-Scale Wood Inspired by Laminated Object Manufacturing and Plywood Technology.

Wood-based materials in current additive manufacturing (AM) feedstocks are primarily restricted to the micron scale. Utilizing large-scale wood in existing AM techniques remains a challenge. This paper proposes an AM method-laser-cut veneer lamination (LcVL)-for wood-based product fabrication. Inspired by laminated object manufacturing (LOM) and plywood technology, LcVL bonds wood veneers in a layer-upon-layer manner. As demonstrated by printed samples, LcVL was able to retain the advantageous qualities of AM, specifically, the ability to manufacture products with complex geometries which would otherwise be impossible using subtractive manufacturing techniques. Furthermore, LcVL-product structures designed through adjusting internal voids and wood-texture directionality could serve as material templates or matrices for functional wood-based materials. Numerical analyses established relations between the processing resolution of LcVL and proportional veneer thickness (layer height). LcVL could serve as a basis for the further development of large-scale wood usage in AM.

A Bio-Hygromorph Fabricated with Fish Swim Bladder Hydrogel and Wood Flour-Filled Polylactic Acid Scaffold by 3D Printing.

Non-powered adaptive systems are attractive in the construction of environment actuators, meteorosensitive architectures, biomedical devices, and soft robotics. Combining hydrophilic materials and anisotropic structures to mimic self-morphing plant structures has been demonstrated as an effective approach to creating artificial hygromorphs. The convenience of 3D printing technologies in shaping programmable complex structures facilitates the imitation of complex anisotropic plant structures. In this research, we constructed a bio-hygromorph using fish swim bladder hydrogel as the hydrophilic material and wood flour-filled polylactic acid (WPLA) scaffold, which was printed with fused deposition modeling (FDM) 3D printing technology (3DP). The environmental benign bio-hygromorph displayed morphing abilities triggered by moisture content changes, as the fish swim bladder hydrogel swelled and shrunk during absorption and desorption cycles. The strain disproportion of the two-layered composite structure in the bio-hygromorph drove the bending deformation. Stress analyses performed with finite element analysis (FEA) also revealed the mechanism behind the moisture content driven morphing of the bio-hygromorph. Notably, the bio-hygromorph exhibited faster response times to moisture absorption than desorption, which may donate actuators' different attributes in distinct moisture conditions.

Visual Analytics for Spatial Clusters of Air-Quality Data.

With the rapid development of industrial society, air pollution has become a major issue in the modern world. The development and widespread deployment of sensors has enabled the collection of air-quality datasets with detailed spatial and temporal scales. Analyses of these spatiotemporal air-quality datasets can help decision makers explore the major causes of air pollution and find efficient solutions. The authors designed a visual analytics system that uses multidimensional scaling (MDS) to transform the air-quality data from monitor stations into 2D plots and uses hierarchical clustering, Voronoi diagrams, and storyline visualizations to help experts explore various attributes and time scales in the data.

Development and Application of Wood Flour-Filled Polylactic Acid Composite Filament for 3D Printing.

This paper presents the development of wood flour (WF)-filled polylactic acid (PLA) composite filaments for a fused deposition modeling (FDM) process with the aim of application to 3D printing. The composite filament consists of wood flour (5 wt %) in a PLA matrix. The detailed formulation and characterization of the composite filament were investigated experimentally, including tensile properties, microstructure, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and X-ray diffraction (XRD). The feedstock filaments of this composite were produced and used successfully in an assembled FDM 3D printer. The research concludes that compared with pure PLA filament, adding WF changed the microstructure of material fracture surface, the initial deformation resistance of the composite was enhanced, the starting thermal degradation temperature of the composite decreased slightly, and there were no effects on the melting temperature. The WF/PLA composite filament is suitable to be printed by the FDM process.