Valorization of the treatment of antibiotic and organic contents generated from an in-situ-RAS-like shrimp farming pond by using graphene-quantum-dots deposited graphitic carbon nitride photocatalysts.

In this study, we investigated the possibility of a photocatalytic system that uses graphene-quantum-dot (GQD)-deposited graphitic carbon nitride (g-C3N4) to treat tetracycline (TC) and other organic compounds generated from an in-situ-recirculatory-aquaculture-system (RAS)-like shrimp farming pond. GQDs were successfully deposited on the exfoliated g-C3N4 base through a hydrothermal treatment. The results showed that the incorporation of GQDs into the g-C3N4 enhanced its porosity without aggregating its mesoporous structure. The GQDs-deposited g-C3N4 photocatalysts revealed sheet-like structures with nanopores on their surface that facilitate photocatalysis. More than 90% of the TC was removed by the photocatalysts under UV-LED irradiation. Low loadings of GQDs over g-C3N4 resulted in a faster and more effective photocatalysis of TC, mainly driven by.O2- radicals. The photocatalysts were also applicable in the degradation of organic compounds with 27% of the total organic compounds (TOC) being removed from the wastewater of a RAS-like shrimp farming pond.

Halide Perovskites and Their Derivatives for Efficient, High-Resolution Direct Radiation Detection: Design Strategies and Applications.

The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead-halide perovskites (LHPs). The large mobility-lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well-suited for radiation detectors, especially given the heavy elements present, which is essential for strong X-ray and γ-ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 106 µC Gyair -1 cm-2 ), limit of detection (directly measured values down to 1.5 nGyair s-1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead-free perovskite-inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non-invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite-inspired material detectors, and how their properties and performance link with critical applications in non-invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.

Polarimetric imaging combining optical parameters for classification of mice non-melanoma skin cancer tissue using machine learning.

Polarimetric imaging systems combining machine learning is emerging as a promising tool for the support of diagnosis and intervention decision-making processes in cancer detection/staging. A present study proposes a novel method based on Mueller matrix imaging combining optical parameters and machine learning models for classifying the progression of skin cancer based on the identification of three different types of mice skin tissues: healthy, papilloma, and squamous cell carcinoma. Three different machine learning algorithms (K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM)) are used to construct a classification model using a dataset consisting of Mueller matrix images and optical properties extracted from the tissue samples. The experimental results show that the SVM model is robust to discriminate among three classes in the training stage and achieves an accuracy of 94 % on the testing dataset. Overall, it is provided that polarimetric imaging systems and machine learning algorithms can dynamically combine for the reliable diagnosis of skin cancer.

Machine learning approaches for predicting Cracking Tolerance Index (CTIndex) of asphalt concrete containing reclaimed asphalt pavement.

One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of cracks. Asphalt concrete's crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology's lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML model, which includes the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The RF model is the most effective ML model, according to analysis and evaluation of performance indicators. By SHAPley Additive exPlanations based on RF model, the input Aggregate content passing 4.75 mm sieve (AP4.75) has a significant effect on the variation of CT Index value. In following, the descending order is Reclaimed Asphalt Pavement content (RAP) > Bitumen content (BC) > Flash point (FP) > Softening point > Rejuvenator content (RC) > Aggregate content passing 0.075mm sieve (AP0.075) > Penetration at 25°C (P). The results study contributes to selecting a suitable AI approach to quickly and accurately determine the CT Index of asphalt concrete mixtures.

Rapid and Direct Liquid-Phase Synthesis of Luminescent Metal Halide Superlattices.

The crystallization of nanocrystal building blocks into artificial superlattices has emerged as an efficient approach for tailoring the nanoscale properties and functionalities of novel devices. To date, ordered arrays of colloidal metal halide nanocrystals have mainly been achieved by using post-synthetic strategies. Here, a rapid and direct liquid-phase synthesis is presented to achieve a highly robust crystallization of luminescent metal halide nanocrystals into perfect face-centered-cubic (FCC) superlattices on the micrometer scale. The continuous growth of individual nanocrystals is observed within the superlattice, followed by the disassembly of the superlattices into individually dispersed nanocrystals owing to the highly repulsive interparticle interactions induced by large nanocrystals. Transmission electron microscopy characterization reveals that owing to an increase in solvent entropy, the structure of the superlattices transforms from FCC to hexagonal close-packed (HCP) and the nanocrystals disassemble. The FCC superlattice exhibits a single and slightly redshifted emission, due to the reabsorption-free property of the building block units. Compared to individual nanocrystals, the superlattices have three times higher quantum yield with improved environmental stability, making them ideal for use as ultrabright blue-light emitters. This study is expected to facilitate the creation of metamaterials with ordered nanocrystal structures and their practical applications.

Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.

Significance: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine. Aim: An approach is proposed for the detection of hepatitis B (HB) virus using a combined Mueller matrix imaging technique and deep learning method. Approach: In the proposed approach, Mueller matrix imaging polarimetry is applied to obtain 4 × 4 Mueller matrix images of 138 HBsAg-containing (positive) serum samples and 136 HBsAg-free (negative) serum samples. The kernel estimation density results show that, of the 16 Mueller matrix elements, elements M 22 and M 33 provide the best discriminatory power between the positive and negative samples. Results: As a result, M 22 and M 33 are taken as the inputs to five different deep learning models: Xception, VGG16, VGG19, ResNet 50, and ResNet150. It is shown that the optimal classification accuracy (94.5%) is obtained using the VGG19 model with element M 22 as the input. Conclusions: Overall, the results confirm that the proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HB virus detection.

Recent Developments and Implementations of Conductive Polymer-Based Flexible Devices in Sensing Applications.

Flexible sensing devices have attracted significant attention for various applications, such as medical devices, environmental monitoring, and healthcare. Numerous materials have been used to fabricate flexible sensing devices and improve their sensing performance in terms of their electrical and mechanical properties. Among the studied materials, conductive polymers are promising candidates for next-generation flexible, stretchable, and wearable electronic devices because of their outstanding characteristics, such as flexibility, light weight, and non-toxicity. Understanding the interesting properties of conductive polymers and the solution-based deposition processes and patterning technologies used for conductive polymer device fabrication is necessary to develop appropriate and highly effective flexible sensors. The present review provides scientific evidence for promising strategies for fabricating conductive polymer-based flexible sensors. Specifically, the outstanding nature of the structures, conductivity, and synthesis methods of some of the main conductive polymers are discussed. Furthermore, conventional and innovative technologies for preparing conductive polymer thin films in flexible sensors are identified and evaluated, as are the potential applications of these sensors in environmental and human health monitoring.

Within-host evolutionary dynamics of seasonal and pandemic human influenza A viruses in young children.

The evolution of influenza viruses is fundamentally shaped by within-host processes. However, the within-host evolutionary dynamics of influenza viruses remain incompletely understood, in part because most studies have focused on infections in healthy adults based on single timepoint data. Here, we analyzed the within-host evolution of 82 longitudinally sampled individuals, mostly young children, infected with A/H1N1pdm09 or A/H3N2 viruses between 2007 and 2009. For A/H1N1pdm09 infections during the 2009 pandemic, nonsynonymous minority variants were more prevalent than synonymous ones. For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titers decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution.

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm.

SIGNIFICANCE: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. AIM: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. APPROACH: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. RESULTS: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements m44, m34, m24, and m14 of the Mueller matrix) dominate the linear polarization properties (i.e., elements m13, m31, m22, and m41 of the Mueller matrix) in determining the classification outcome of the trained classifier. CONCLUSIONS: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

Morphology-Dependent Ambient-Condition Growth of Perovskite Nanocrystals for Enhanced Stability in Photoconversion Device.

CsPbBr3 perovskite nanocrystals with two different dimensionalities were synthesized at different temperatures and then integrated as optoelectronic transducers into transistor-type photoconversion devices. Postsynthesis transformation was observed for two-dimensional (2D) nanoplatelets, while the transformation was rarely found in 3D nanocubes. At ambient temperature and pressure, neighboring nanoplatelets made facet-to-facet contact and then fused into larger 2D nanoplatelets (2-5 times) without defects. The coalescence of 2D nanoplatelets at the ambient condition lowered the density of defects at the surface of the nanocrystals and thus could facilitate effective and stable photoconversion behavior in the nanocrystal film integrated into the device. Consequently, the ambient-condition aging of 2D nanoplatelets on device substrate led to 3 times higher retention in photoconversion performance. Importantly, these results provide a new concept of how perovskite nanocrystals can be integrated into a device for enhanced stability in device performance.

Surfactant-in-Polymer Templating for Fabrication of Carbon Nanofibers with Controlled Interior Substructures: Designing Versatile Materials for Energy Applications.

A simple, scalable, surfactant-in-polymer templating approach is demonstrated to create controlled long-range secondary substructures in a primary structure. A metal bis(2-ethylhexyl) sulfosuccinate (MAOT) as the surfactant is shown to be capable of serving as a sacrificial template and metal precursor in carbon nanofibers. The low interfacial tension and controllable dimensions of the MAOT are maintained in the solid-phase polymer, even during electrospinning and heat-treatment processes, allowing for the long-range uniform formation of substructures in the nanofibers. The MAOT content is found to be a critical parameter for tailoring the diameter of the nanofibers and their textural properties, such as size and volume of interior pores. The metal counterion species in the MAOT determine the introduction of metallic phases in the nanofiber interior. The incorporation of MAOT with Na as the counterion into the polymer phase leads to the formation of a built-in pore structure in the nanofibers. In contrast, MAOT with Fe as a counterion generates unique iron-in-pore substructures in the nanofibers (FeCNFs). The FeCNFs exhibit outstanding charge storage and water splitting performances. As a result, the MAOT-in-polymer templating approach can be extended to combinations of various metal precursors and thus create desirable functionalities for different target applications.

Anomalous restoration of sp2 hybridization in graphene functionalization.

The functionalization of nanocarbon materials such as graphene has attracted considerable attention over the past decades. In this work, we designed and synthesized a unique N-heterocyclic carbene compound with a pyrene tail group (NHCp) to investigate how carbene species can be used for the functionalization of graphene. Although the carbene moiety of NHCp has the ability to covalently bond to graphene, the pyrene tail can noncovalently interact with graphene and allows monitoring its surrounding microenvironment. The major characteristics of the resulting nanohybrids were highly dependent on the type of graphene and the NHCp-to-graphene weight ratio. Importantly, despite the covalent functionalization of graphene, an anomalous decrease in the intensity of the Raman D peak and improved conductivity were observed for the nanohybrids. It was found that the covalent bond of NHCp to the graphene edge may allow the hybridization of their orbitals, which affects electronic energy levels and alters the double resonance process that originates the D peak at the edge defect. Importantly, the NHCp compound can act as a π acceptor (not just as a σ donor) via the NHCp-graphene covalent bridge. This is the first report showing that the concept of π-backdonation can be realized in two-dimensional materials, such as graphene, and rationally designed carbene molecules can functionalize graphene without losing their beneficial sp2 hybridization characteristics.

Electrical monitoring of photoisomerization of block copolymers intercalated into graphene sheets.

Insulating polymers have received little attention in electronic applications. Here, we synthesize a photoresponsive, amphiphilic block copolymer (PEO-b-PVBO) and further control the chain growth of the block segment (PVBO) to obtain different degrees of polymerization (DPs). The benzylidene oxazolone moiety in PEO-b-PVBO facilitated chain-conformational changes due to photoisomerization under visible/ultraviolet (UV) light illumination. Intercalation of the photoresponsive but electrically insulating PEO-b-PVBO into graphene sheets enabled electrical monitoring of the conformational change of the block copolymer at the molecular level. The current change at the microampere level was proportional to the DP of PVBO, demonstrating that the PEO-b-PVBO-intercalated graphene nanohybrid (PGNH) can be used in UV sensors. Additionally, discrete signals at the nanoampere level were separated from the first derivative of the time-dependent current using the fast Fourier transform (FFT). Analysis of the harmonic frequencies using the FFT revealed that the PGNH afforded sawtooth-type current flow mediated by Coulomb blockade oscillation.

Exfoliation of 2D Materials for Energy and Environmental Applications.

The fascinating properties of single-layer graphene isolated by mechanical exfoliation have inspired extensive research efforts toward two-dimensional (2D) materials. Layered compounds serve as precursors for atomically thin 2D materials (briefly, 2D nanomaterials) owing to their strong intraplane chemical bonding but weak interplane van der Waals interactions. There are newly emerging 2D materials beyond graphene, and it is becoming increasingly important to develop cost-effective, scalable methods for producing 2D nanomaterials with controlled microstructures and properties. The variety of developed synthetic techniques can be categorized into two classes: bottom-up and top-down approaches. Of top-down approaches, the exfoliation of bulk 2D materials into single or few layers is the most common. This review highlights chemical and physical exfoliation methods that allow for the production of 2D nanomaterials in large quantities. In addition, remarkable examples of utilizing exfoliated 2D nanomaterials in energy and environmental applications are introduced.

Zero reduction luminescence of aqueous-phase alloy core/shell quantum dots via rapid ambient-condition ligand exchange.

Quantum dots (QDs) have been widely studied as promising materials for various applications because of their outstanding photoluminescence (PL). Although ligand exchange methods for QDs have been developed over two decades, the PL quantum yield (QY) of aqueous phase QDs is still lower than that of their organic phase and the mechanism of quenching has not been clearly understood. In this study, we demonstrate for the first time that 3-mercaptopropionic-capped CdZnSeS/ZnS core/shell QDs obtained via ligand exchange in a ternary solvent system containing chloroform/water/dimethyl sulfoxide can enable the fast phase transfer and zero reduction of PL under ambient condition. The new solvent system allows the ligand-exchanged QDs to exhibit enhanced QYs up to 8.1% of that of the organic-phase QDs. Based on both theoretical calculation and experiment, it was found that control over the physical/chemical perturbation between the organic/aqueous phases by choosing appropriate solvents for the ligand exchange process is very important to preserve the optical properties of QDs. We believe that our new technologies and theoretical knowledge offer opportunities for the future design and optimization of highly stable and highly luminescent aqueous-phase QDs for various applications.

Correction: Pine cone mold: a toolbox for fabricating unique metal/carbon nanohybrid electrocatalysts.

Correction for 'Pine cone mold: a toolbox for fabricating unique metal/carbon nanohybrid electrocatalysts' by Hyunwoo Han et al., Nanoscale, 2019, DOI: 10.1039/c9nr06794a.

Pine cone mold: a toolbox for fabricating unique metal/carbon nanohybrid electrocatalysts.

Nature presents delicate and complex materials systems beyond those fathomable by humans, and therefore, extensive effort has been made to utilize or mimic bio-materials and bio-systems in various fields. Biomass, an inexhaustible natural materials source, can also present good opportunities for the development of unprecedented, advanced materials and processing systems. Herein, we demonstrate the use of pine cones as a biomass mold for creating new and useful metal/carbon nanohybrids (MCNHs). The inherent water-induced folding actuation of the cone scales allows the casting of an aqueous solution of a single metal precursor or a binary metal mixture into the cone mold by simply immersing the cone in the solution. The cone actively absorbs aqueous-phase metal precursors through the bract scales and the precursor ions introduced into the cone are anchored to the functional groups of the interior tissues of the cone. Subsequent heat treatment successfully led to the formation of unique MCNHs. Iron, manganese, and cobalt were employed as model metals, binary mixtures of which were also cast into the cone mold to create further versatile MCNHs. Nanoparticulate metals were formed on the carbon supports, where the size, size distribution, and crystallinity of the nanoparticles were highly dependent on the identity of the single-component precursor and the combination of precursors. Consequently, the electrochemical activity of the MCNHs also depended on which metal precursors were cast into the cone mold. The MCNH prepared from the mixture of iron and manganese precursors (MFeMnCNH) showed the best electrochemical activity. As model applications, MFeMnCNH was applied to electrode materials for electrochemical charge storage and the oxygen evolution reaction. An electrochemical capacitor cell based on the MFeMnCNH electrodes showed excellent performance with energy densities of 38.7-54.2 W h kg-1 at power densities of 16 000-160 kW kg-1. In addition, MFeMnCNH demonstrated a low overpotential of 464 mV and fast kinetics with a Tafel slope of 64.6 mV dec-1 as an electrocatalyst for the oxygen evolution reaction in 1.0 M KOH. These results substantiate that pine cones as a biomass mold show great promise for creating versatile MCNHs through further combination of various precursors.

Growth in children infected with HIV receiving anti-retroviral therapy in Vietnam.

BACKGROUND: There are currently two markers used to monitor treatment response to anti-retroviral therapy (ART) in HIV-infected children: CD4 T-cell count and HIV viral load; but analysis of these could be challenging in resource-poor countries. The aim of this study was therefore to determine whether change in growth parameters such as weight-for-age Z score (WAZ), height-for-age Z score (HAZ) and body mass index-for-age Z score (BMIZ) is associated with treatment response in HIV-infected children. METHODS: This was a nested case-control study, in which the data were collected at enrolment and then periodically every 6 months for a total 36 month follow up of 107 HIV-infected children enrolled and treated at National Hospital of Pediatrics, Vietnam. RESULTS: At treatment initiation, WAZ, HAZ and BMIZ were not significantly higher in the treatment success (TS) group compared with the treatment failure (TF) group. After ART initiation, WAZ and HAZ increased, and this was significant in the TS group (from -1.5 to -0.54, P < 0.01 and from -2.06 to -0.84, P < 0.01, respectively). Low HAZ was significantly associated with TF (HR, 0.71; 95% CI: 0.54-0.92). CONCLUSION: Height-for-age Z score was the most sensitive growth parameter in prediction of the treatment response. In order to use growth parameters, particularly HAZ as a prognosis marker for TF in clinical practice, further research should be conducted to evaluate the role of growth parameters and their effects on treatment response.

Combining SWNT and Graphene in Polymer Nanofibers: A Route to Unique Carbon Precursors for Electrochemical Capacitor Electrodes.

It is important to fabricate nanostructured architectures comprised of functional components for a wide variety of applications because precise structural control in the nanometer regime can yield unprecedented, fascinating properties. Owing to their well-defined microstructural characteristics, it has been popular to use carbon nanospecies, such as nanotubes and graphene, in fabricating nanocomposites and nanohybrids. Nevertheless, it still remains hard to control and manipulate nanospecies for specific applications, thus preventing their commercialization. Herein, first, we report unique one-dimensional nanoarchitectures with meso-/macropores, consisting of single-walled nanotubes (SWNTs), graphene, and polyacrylonitrile, in which poly(vinyl alcohol) was employed as a dispersing agent and sacrificial porogen. One-dimensional SWNTs and two-dimensional graphene pieces were combined in the confined interior space of electrospun nanofibers, which led to unique microstructural characteristics such as enhanced ordering of SWNTs, graphene pieces, and polymer chains in the nanofiber interior. Next, the SWNT/graphene-in-polymer nanofiber (SGPNF) structures were converted into carbonized products (SGCNFs) with effective porosity and tunable electrochemical properties. Similar to SGPNFs, the microstructural and electrical properties of the SGCNFs depended on the incorporated amount of SWNT and graphene. At higher SWNT content, the mesopore volume proportion and specific discharge capacitance of the SGCNFs increased by max. 63 and 598%, respectively. The SGCNFs showed strong potential as a high-performance electrode material for electrochemical capacitors (max. capacitance: nonactivated ∼390 F g-1 and activated ∼750 F g-1). Flexible, all solid-state capacitor cells based on SGCNFs were also successfully demonstrated as a model application. The SGCNFs can be further functionalized by various methods, which will impart attractive properties for extended applications.

Elevation of immunoglobulin levels is associated with treatment failure in HIV-infected children in Vietnam.

BACKGROUND: HIV-infected children suffer from higher levels of treatment failure compared to adults. Immunoactivation, including humoral immunoactivation reflected by increased immunoglobulin levels, is believed to occur early during HIV infection. Therefore, we wanted investigate alteration in immunoglobulin levels in association with treatment response in HIV-infected children. METHODS: A nested case-control study was conducted using clinical data collected from 68 HIV-infected children enrolled at the National Hospital of Pediatrics, Vietnam. RESULTS: The results showed that immunoglobulin levels, CD4 T-cell counts, CD4 T-cell percentage, and HIV load were significantly higher in the treatment-failure group than the treatment-success group at treatment initiation. IgG and IgA levels were negatively correlated with CD4 T-cell counts (P=0.049 and P<0.01, respectively) and positively correlated with HIV load (P=0.04 and P=0.02, respectively). In addition, IgG and IgA levels were independently associated with treatment response, analyzed by Cox regression analysis (HR 1.19 [P=0.049] and HR 1.69 [P<0.01], respectively). CONCLUSION: Elevation of IgA levels occurred early during HIV infection, and might have a prognostic role in treatment response.