Superior Heavy Metal Ion Adsorption Capacity in Aqueous Solution by High-Density Thiol-Functionalized Reduced Graphene Oxides.

The preparation of mercapto-reduced graphene oxides (m-RGOs) via a solvothermal reaction using P4S10 as a thionating agent has demonstrated their potential as an absorbent for scavenging heavy metal ions, particularly Pb2+, from aqueous solutions due to the presence of thiol (-SH) functional groups on their surface. The structural and elemental analysis of m-RGOs was conducted using a range of techniques, including X-ray diffraction (XRD), Raman spectroscopy, optical microscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), scanning transmission electron microscopy equipped with energy-dispersive spectroscopy (STEM-EDS), and X-ray photoelectron spectroscopy (XPS). At pH 7 and 25 °C, the maximum adsorption capacity of Pb2+ ions on the surface of m-RGOs was determined to be approximately 858 mg/g. The heavy metal-S binding energies were used to determine the percent removal of the tested heavy metal ions, with Pb2+ exhibiting the highest percentage removal, followed by Hg2+ and Cd2+ ions having the lowest percent removal, and the binding energies observed were Pb-S at 346 kJ/mol, Hg-S at 217 kJ/mol, and Cd-S at 208 kJ/mol. The time-dependent removal study of Pb2+ ions also yielded promising results, with almost 98% of Pb2+ ions being removed within 30 min at pH 7 and 25 °C using a 1 ppm Pb2+ solution as the test solution. The findings of this study clearly demonstrate the potential and efficiency of thiol-functionalized carbonaceous material for the removal of environmentally harmful Pb2+ from groundwater.

Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.

PURPOSE: This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT). METHODS: A total of 19,052 coronal reformatted chest CT images of 110 CT image sets (55 pairs of concordant 16- and 320-row CT image sets) were included and used to train a deep learning algorithm for artifact and noise correction. For internal validation, 4093 coronal reformatted CT images of 25 patients from 16-row CT images underwent correction processing. For external validation, chest CT images of 30 patients (1028 coronal reformatted CT images), acquired in other institutions using different scanners, were subjected to correction processing. For both validations, image quality was compared between original ("CT origin ") and deep learning-based corrected ("CT correct ") CT images. Quantitative analysis for stair-step artifact (coefficient of variance of CT density on coronal reformation), image noise, signal-to-noise ratio, and contrast-to-noise ratio were evaluated. Subjective image quality scores were assigned for image contrast, artifact, and conspicuity of major structures. RESULTS: CT correct showed significantly reduced stair-step artifact (mean coefficient of variance: CT origin 7.35 ± 2.0 vs CT correct 5.17 ± 2.4, P < 0.001) and image noise and improved signal-to-noise ratio and contrast-to-noise ratio in the aorta, pulmonary artery, and liver, compared with those of CT origin ( P < 0.01). On subjective analysis, CT correct had higher image contrast, lower artifact, and better conspicuity than CT origin . Most results of the external validation were consistent with those obtained from the internal validation, except for those concerning the pulmonary artery. CONCLUSIONS: Deep learning-based artifact correction significantly improved the image quality of coronal reformation chest CT by reducing image noise and artifacts.

A fidelity-embedded learning for metal artifact reduction in dental CBCT.

PURPOSE: Dental cone-beam computed tomography (CBCT) has been increasingly used for dental and maxillofacial imaging. However, the presence of metallic inserts, such as implants, crowns, and dental braces, violates the CT model assumption, which leads to severe metal artifacts in the reconstructed CBCT image, resulting in the degradation of diagnostic performance. In this study, we used deep learning to reduce metal artifacts. METHODS: The metal artifacts, appearing as streaks and shadows, are nonlocal and highly associated with various factors, including the geometry of metallic inserts, energy-dependent attenuation, and energy spectrum of the incident X-ray beam, making it difficult to learn their complicated structures directly. To provide a step-by-step environment in which deep learning can be trained, we propose an iterative learning approach in which the network at each iteration step learns the correction error caused by the previous network, while enforcing the data fidelity in the projection domain. To generate a realistic paired training dataset, metal-free CBCT scans were collected from patients without metallic inserts, and then simulated metal projection data were added to generate the corresponding metal-corrupted projection data. RESULTS: The feasibility of the proposed method was investigated in clinical metal-affected CBCT scans, as well as simulated metal-affected CBCT scans. The results show that the proposed method significantly reduces metal artifacts while preserving the morphological structures near metallic objects and outperforms direct image domain learning. CONCLUSION: The proposed fidelity-embedded learning can effectively reduce metal artifacts in dental CBCT compared with direct image domain learning.

Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

OBJECTIVES: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images. MATERIALS AND METHODS: LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set. RESULTS: The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images. CONCLUSION: The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.

Facile Gram-Scale Synthesis of Co3O4 Nanocrystal from Spent Lithium Ion Batteries and Its Electrocatalytic Application toward Oxygen Evolution Reaction.

In this study, we demonstrate a new approach to easily prepare spinel Co3O4 nanoparticles (s-Co3O4 NPs) in the gram-scale from the cathode of spent lithium ion batteries (SLIBs) by the alkali leaching of hexaamminecobalt(III) complex ions. As-obtained intermediate and final products were characterized with powder X-ray diffraction (PXRD), Ultraviolet-Visible (UV-Vis), Fourier transform infrared (FTIR), and Transmission electron microscopy (TEM). Additionally, the synthesized s-Co3O4 NPs showed better electrocatalytic properties toward the oxygen evolution reaction (OER) in comparison to previously reported Co3O4 NPs and nanowires, which could be due to the more exposed electrocatalytic active sites on the s-Co3O4 NPs. Moreover, the electrocatalytic activity of the s-Co3O4 NPs was comparable to the previously reported RuO2 catalysts. By taking advantage of the proposed recycling route, we would expect that various valuable transition metal oxide NPs could be prepared from SLIBs.

3D cephalometric landmark detection by multiple stage deep reinforcement learning.

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.

Easily Processable, Highly Transparent and Conducting Thiol-Functionalized Reduced Graphene Oxides Langmuir-Blodgett Films.

We report synthesis and fabrication of highly thionated reduced graphene oxide and its Langmuir-Blodgett (LB) film without an LB trough. As the synthesized product, mercapto reduced graphene oxide (mRGO) contains high thiol content estimated from XPS, corresponding to a surface coverage of 1.3 SH/nm2. The mRGO LB film shows two electronic transport properties, following Efros-Shklovskii variable-range hopping (VRH) and Mott VRH at low and high temperature, respectively. Optical and band gap of the LB film was estimated from Tauc plot and semi-logarithmic-scale plot of sheet resistance versus temperature to be 0.6 and 0.1 eV, respectively. Additionally, the sheet resistance of the mRGO LB film depends on the quantity of the thiol functional group with the same transmittance at 550 nm (500 kΩ for mRGO, 1.3 MΩ for tRGO with 92% transmittance).

Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.

Diagnostic Performance of a New Convolutional Neural Network Algorithm for Detecting Developmental Dysplasia of the Hip on Anteroposterior Radiographs.

OBJECTIVE: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. MATERIALS AND METHODS: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. RESULTS: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). CONCLUSION: The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy.

BACKGROUND: The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods. METHODS: We retrospectively reviewed the medical records of 358 patients who underwent SWL for urinary stone (kidney and upper-ureter stone) between 2015 and 2018 and evaluated the possible prognostic features, including patient population characteristics, urinary stone characteristics on a non-contrast, computed tomographic image. We performed 80% training set and 20% test set for the predictions of success and mainly used decision tree-based machine learning algorithms, such as random forest (RF), extreme gradient boosting trees (XGBoost), and light gradient boosting method (LightGBM). RESULTS: In machine learning analysis, the prediction accuracies for stone-free were 86.0, 87.5, and 87.9%, and those for one-session success were 78.0, 77.4, and 77.0% using RF, XGBoost, and LightGBM, respectively. In predictions for stone-free, LightGBM yielded the best accuracy and RF yielded the best one in those for one-session success among those methods. The sensitivity and specificity values for machine learning analytics are (0.74 to 0.78 and 0.92 to 0.93) for stone-free and (0.79 to 0.81 and 0.74 to 0.75) for one-session success, respectively. The area under curve (AUC) values for machine learning analytics are (0.84 to 0.85) for stone-free and (0.77 to 0.78) for one-session success and their 95% confidence intervals (CIs) are (0.730 to 0.933) and (0.673 to 0.866) in average of methods, respectively. CONCLUSIONS: We applied a selected machine learning analysis to predict the result after treatment of SWL for urinary stone. About 88% accurate machine learning based predictive model was evaluated. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for SWL success outcomes.

Highly Luminous Ba2SiO4-δN2/3δ:Eu2+ Phosphor for NUV-LEDs: Origin of PL-Enhancement by N3--Substitution.

Ba2SiO4-δN2/3δ:Eu2+ (BSON:Eu2+) materials with different N3- contents were successfully prepared and characterized. Rietveld refinements showed that N3- ions were partially substituted for the O2- ions in the SiO4-tetrahedra because the bond lengths of Si‒(O,N) (average value = 1.689 Å) were slightly elongated compared with those of Si‒O (average value = 1.659 Å), which resulted in the minute compression of the Ba(2)‒O bond lengths from 2.832 to 2.810 Å. The average N3- contents of BSON:Eu2+ phosphors were determined from 100 nm to 2000 nm depth of grain using a secondary ion mass spectrometry (SIMS): 0.064 (synthesized using 100% α-Si3N4), 0.035 (using 50% α-Si3N4 and 50% SiO2), and 0.000 (using 100% SiO2). Infrared (IR) and X-ray photoelectron spectroscopy (XPS) measurements corroborated the Rietveld refinements: the new IR mode at 850 cm-1 (Si‒N stretching vibration) and the binding energy at 98.6 eV (Si-2p) due to the N3- substitution. Furthermore, in UV-region, the absorbance of N3--substituted BSON:Eu2+ (synthesized using 100% α-Si3N4) phosphor was about two times higher than that of BSO:Eu2+ (using 100% SiO2). Owing to the N3- substitution, surprisingly, the photoluminescence (PL) and LED-PL intensity of BSON:Eu2+ (synthesized using 100% α-Si3N4) was about 5.0 times as high as that of BSO:Eu2+ (using 100% SiO2). The compressive strain estimated by the Williamson-Hall (W-H) method, was slightly increased with the higher N3- content in the host-lattice of Ba2SiO4, which warranted that the N3- ion plays an important role in the highly enhanced PL intensity of BSON:Eu2+ phosphor. These phosphor materials could be a bridgehead for developing new phosphors and application in white NUV-LEDs field.

Three-dimensional measurement and registration accuracy of a third-generation optical tracking system for navigational maxillary orthognathic surgery.

OBJECTIVE: The purpose of this study was to evaluate the accuracy of an optical tracking system during reference point localization, measurement, and registration of skull models for navigational maxillary orthognathic surgery. STUDY DESIGN: Accuracy was first evaluated on the basis of the position recording discrepancy at a static point and at 2 points of fixed lengths. Ten reference points were measured on a skull model at 7 different locations, and their measurements were compared with predicted positions by using 4 registration methods. Finally, positional tracking of reference points for simulated maxillary surgery was performed and compared with laser scan data. RESULTS: The average linear measurement discrepancy was 0.28 mm, and the mean measurement discrepancy with the 5 registered cranial points was 1.53 mm. The average measurement discrepancy after maxillary surgery was 1.91 mm (for impaction) and 1.56 mm (for advancement). The registration discrepancy in jitter and point registration on the y-axis was significantly greater than on the other axes. CONCLUSIONS: The optical tracking system seems clinically acceptable for precise tracking of the maxillary position during navigational orthognathic surgery, notwithstanding the chance of greater measurement error on the y-axis.

RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Automatic 3D cephalometric annotation system using shadowed 2D image-based machine learning.

This paper presents a new approach to automatic three-dimensional (3D) cephalometric annotation for diagnosis, surgical planning, and treatment evaluation. There has long been considerable demand for automated cephalometric landmarking, since manual landmarking requires considerable time and experience as well as objectivity and scrupulous error avoidance. Due to the inherent limitation of two-dimensional (2D) cephalometry and the 3D nature of surgical simulation, there is a trend away from current 2D to 3D cephalometry. Deep learning approaches to cephalometric landmarking seem highly promising, but there exist serious difficulties in handling high dimensional 3D CT data, dimension referring to the number of voxels. To address this issue of dimensionality, this paper proposes a shadowed 2D image-based machine learning method which uses multiple shadowed 2D images with various lighting and view directions to capture 3D geometric cues. The proposed method using VGG-net was trained and tested using 2700 shadowed 2D images and corresponding manual landmarkings. Test data evaluation shows that our method achieved an average point-to-point error of 1.5 mm for the seven major landmarks.

Monofacet-Selective Cavitation within Solid-State Silica-Nanoconfinement toward Janus Iron Oxide Nanocube.

Here, a highly selective solid-state nanocrystal conversion strategy is developed toward concave iron oxide (Fe3O4) nanocube with an open-mouthed cavity engraved exclusively on a single face. The strategy is based on a novel heat-induced nanospace-confined domino-type migration of Fe2+ ions from the SiO2-Fe3O4 interface toward the surrounding silica shell and concomitant self-limiting nanoscale phase-transition to the Fe-silicate form. Equipped with the chemically unique cavity, the produced Janus-type concave iron oxide nanocube was further functionalized with controllable density of catalytic Pt-nanocrystals exclusively on concave sites and utilized as a highly diffusive catalytic Janus nanoswimmer for the efficient degradation of pollutant-dyes in water.

Spatially Confined Formation and Transformation of Nanocrystals within Nanometer-Sized Reaction Media.

The extensive research performed in the past two decades has enabled the production of a range of colloidal nanocrystals, mostly through solution-based procedures that generate and transform nanostructures in bulk-phase solutions containing precursors and surfactants. However, the understanding and control of each nanocrystal (trans)formation step during the synthesis are still complicated because of the high complexity of this process, in which multiple diverse events such as nucleation, subsequent growth, attachment, and ripening occur simultaneously in bulk suspensions. Unlike well-established solution-based methods, solid-state reactions, which had been at the forefront of traditional inorganic materials chemistry, are quite rarely utilized in the realm of nanomaterials because of the high temperatures required for most solid-state reactions, as a result of which the clusters and NCs are prone to migrate through the bulk reaction medium and sinter together uncontrollably into large particles. We have been pursuing the "nanospace-confined approach" to explore the use of a variety of solid and hollow silica nanoparticles as either solid-state or solution-phase reaction media to carry out the syntheses and transformations of nanocrystals in a unique microenvironment, partitioning the reactants, intermediates, and transition states from the rest of the bulk reaction medium. Such nanoconfined systems have the potential not only to enable efficient and selective nanocrystal conversion chemistries but also to provide fundamental understanding pertaining to the synthetic evolution of nanostructures and transient mechanistic steps. The unique spaces with sizes of a few tens of nanometers inside nanoconfined systems offer the opportunity to observe and elucidate novel deconvoluted chemical phenomena that are impossible to investigate in bulk systems, and comprehensive understanding of nanoconfined chemistry can be implicated in explaining and controlling the macroscopic chemical behaviors. This Account describes our focused research on developing spatially confined platforms for nanocrystal syntheses and transformations, highlighting our diversity-oriented strategy, namely, the "postdecoration approach", which results in the evolution of new nanocatalytic sites in a preformed cavity for diversifying and controlling their morphologies, number, density and combinations. We discuss key examples of the "nanoconfined solid-state conversion approach" that involve novel reactions of nanocrystals within thermally stable solid silica nanospheres to synthesize and transform complex hybrid nanocrystals with increased complexity and functionality. In addition, an enlightening discussion of the examples of nanocrystal syntheses and conversions in nanoconfined solutions inside enclosed and exposed cavities of silica nanospheres is included. Finally, the important applications of nanospace-confined systems in various fields are also briefly discussed.

Colloids of Holey Gd2 O3 Nanosheets Converted from Exfoliated Gadolinium Hydroxide Layers.

This paper proposes a confined solid-state conversion approach using layered metal-hydroxides for the production of a colloidal suspension of porous 2D crystalline metal oxide layers with superior electrochemical H2 O2 sensing performance. This study investigates the conversion chemistry of delaminated layers of gadolinium hydroxide (LGdH), [Gd2 (OH)5 ]+ , encapsulated in a silica nanoshell that provides an antistacking and antisintering environment during the phase-transition at high temperature. Thermal treatment of the LGdH layers within the protected environment results in a dimensionally confined phase-transition into crystalline Gd2 O3 nanosheets with an isomorphic 2D structure. Furthermore, annealing at higher temperatures leads to the evolution of in-plane mesoporous structure on the Gd2 O3 nanosheet. Based on insight acquired from in-depth investigation, the evolution of in-plane porosity proceeds through the in-plane dominant silicate-formation reaction at the interface with the surrounding silica shell. Their 2D-anisotropic and mesoporous morphological features are preserved, producing a colloidal suspension of holey nanosheets that can be used to fabricate a thin and porous film through wet-coating deposition. This study also demonstrates the superior electrochemical H2 O2 sensing ability of the resultant porous Gd2 O3 film, which represents a ≈1000- and 10-fold enhancement of the detection limit and sensitivity, respectively, in comparison to previously reported Gd2 O3 films.

Spontaneous Pt Deposition on Defective Surfaces of In2O3 Nanocrystals Confined within Cavities of Hollow Silica Nanoshells: Pt Catalyst-Modified ITO Electrode with Enhanced ECL Performance.

Although the deposition of metallic domains on a preformed semiconductor nanocrystal provides an effective pathway to access diverse hybrid nanocrystals with synergistic metal/semiconductor heterojunction interface, those reactions that take place on the surface of semiconductor nanoscrystals have not been investigated thoroughly, because of the impediments caused by the surface-capping organic surfactants. By exploiting the interfacial reactions occurring between the solution and nanoparticles confined with the cavities of hollow nanoparticles, we propose a novel nanospace-confined strategy for assessing the innate reactivity of surfaces of inorganic semiconductor nanoparticles. This strategy was adopted to investigate the newly discovered process of spontaneous Pt deposition on In2O3 nanocrystals. Through an in-depth examination involving varying key reaction parameters, the Pt deposition process was identified to be templated by the defective In2O3 surface via a unique redox process involving the oxygen vacancies in the In2O3 lattice, whose density can be controlled by high-temperature annealing. The product of the Pt-deposition reaction inside the hollow silica nanoparticle, bearing In2O3-supported Pt catalysts inside the cavity protected by a porous silica shell, was proved to be an effective nanoreactor system which selectively and sustainably catalyzed the reduction reaction of small-sized aromatic nitro-compounds. Moreover, the surfactant-free and electroless Pt deposition protocol, which was devised based on the surface chemistry of the In2O3 nanoparticles, was successfully employed to fabricate Pt-catalyst-modified ITO electrodes with enhanced electrogenerated chemiluminescece (ECL) performance.

Equipment-Free Deposition of Graphene-Based Molybdenum Oxide Nanohybrid Langmuir-Blodgett Films for Flexible Electrochromic Panel Application.

The potential electrochromic application of graphene-based nanohybrids is hampered by the challenges in interfacing the electrochromic nanoparticles with graphene at atomic scale and in fabricating their thin film on the substrate through a scalable method. In an effort to overcome these challenges, we demonstrate a highly dispersible graphene-based molybdenum oxide nanohybrid (mRGO-MoO3-x) for flexible electrochromic application. With only a squeeze pipet, mRGO-MoO3-x could be deposited with a high coverage on various substrates through a scalable equipment-free Langmuir-Blodgett film deposition method. By taking advantage of high transmittance benefited from its remarkable thinness, the mRGO-MoO3-x Langmuir-Blodgett film shows a superior reversible electrochromic property with high coloration efficiency on both hard and flexible substrates.

A Reconstruction Method of Blood Flow Velocity in Left Ventricle Using Color Flow Ultrasound.

Vortex flow imaging is a relatively new medical imaging method for the dynamic visualization of intracardiac blood flow, a potentially useful index of cardiac dysfunction. A reconstruction method is proposed here to quantify the distribution of blood flow velocity fields inside the left ventricle from color flow images compiled from ultrasound measurements. In this paper, a 2D incompressible Navier-Stokes equation with a mass source term is proposed to utilize the measurable color flow ultrasound data in a plane along with the moving boundary condition. The proposed model reflects out-of-plane blood flows on the imaging plane through the mass source term. The boundary conditions to solve the system of equations are derived from the dimensions of the ventricle extracted from 2D echocardiography data. The performance of the proposed method is evaluated numerically using synthetic flow data acquired from simulating left ventricle flows. The numerical simulations show the feasibility and potential usefulness of the proposed method of reconstructing the intracardiac flow fields. Of particular note is the finding that the mass source term in the proposed model improves the reconstruction performance.