Rosenthalburt6046

In this work, a multifunctional oxygen deficient titanium oxynitride skeleton featuring a Co-metal-decorated three-dimensional ordered macroporous (3DOM) structure embedded with N-doped carbon nanotubes (Co@TiOxNy/N-CNTs) is fabricated as a sulfur host for lithium-sulfur (Li-S) batteries. Climbazole The unique 3DOM framework provides abundant space for sulfur accommodation and effective pathways for electrolyte infiltration. The robust titanium oxynitride skeleton also ensures good structural integrity during the repeated charge/discharge cycling. Meanwhile, the introduction of oxygen defects not only improves the intrinsic conductivity of the TiO2 skeleton but also enhances its capability for lithium polysulfide (LiPS) trapping. The N-CNTs embedded in the macroporous framework form an ultra-high conductive network and also provide rich micropores for sulfur distribution and physical confinement. The highly dispersed Co nanoparticles uniformly anchored on TiOxNy and N-CNTs act as electrocatalysts promoting the conversion of LiPSs. Attributed to these features, the Co@TiOxNy/N-CNTs/S electrode presents good rate capability and excellent cycling performance. Even under a sulfur loading of 6.34 mg cm-2 and a low electrolyte to sulfur ratio (E/S = 8 μL mg-1), a high area capacity of 5.05 mA h cm-2 can be achieved after 50 cycles. The flexible pouch cell also delivers an impressive discharge capacity of 972 mA h g-1 after 100 cycles under a sulfur loading of 4 mg cm-2. This work offers a rational strategy for the design of advanced sulfur cathodes for Li-S batteries.A C-H bond cleavage-enabled aerobic ring-opening reaction of 2-aminobenzofuran-3(2H)-ones formed in situ by hemiacetals with a variety of amines is reported. This simple one-pot reaction provides an alternative approach to obtain o-hydroxyaryl glyoxylamides in excellent yields of up to 97%. Alkylamines react with hemiacetals via a catalyst-free dehydration condensation to generate 2-aminobenzofuran-3(2H)-ones. The in situ formed semicyclic N,O-acetals undergo the same amine-initiated C-H bond hydroxylation in air under mild conditions to afford ring-opening products. Similarly, arylamines were investigated as substrates for a two-step tandem process involving a DPP-catalyzed condensation followed by a Et2NH-mediated C-H hydroxylation. Unlike the previously reported functionalization of N,O-acetals via a C-O or C-N cleavage, the aerobic oxidative C-H hydroxylation in this reaction, which is promoted by using stoichiometric amounts of alkylamines as both a Lewis base and a reductant at room temperature under atmospheric air, proceeds via α-carbonyl-stabilized carbanion intermediates from the C-H cleavage of N,O-acetals.A novel aza-Prins type cyclization reaction involving N-acyliminium ions and amides is reported for the synthesis of tetrahydropyrimido[2,1-a]isoindole-2,6-dione and 6,6a-dihydroisoindolo[2,1-a]quinazoline-5,11-dione derivatives in excellent yields. The strategy features inexpensive reagents, mild reaction conditions, and metal-free synthesis of N-heterocyclic frameworks. Further, post-synthetic modification results in the unprecedented formation of its triazole, tetracyclic diazacyclopenta[def]phenanthrene-1,4(9a1H)-dione and carbonyl derivatives.We present a scheme to extract the adsorption energy, adsorbate interaction parameter and the saturation coverage from temperature programmed desorption (TPD) experiments. We propose that the coverage dependent adsorption energy can be fit using a functional form including the configurational entropy and linear adsorbate-adsorbate interaction terms. As one example of this scheme, we analyze TPD of CO desorption on Au(211) and Au(310) surfaces. We determine that under atmospheric CO pressure, the steps of both facets adsorb between 0.4-0.9 ML coverage of CO*. We compare this result against energies obtained from five density functionals, RPBE, PBE, PBE-D3, RPBE-D3 and BEEF-vdW. We find that the energies and equilibrium coverages from RPBE-D3 and PBE are closest to the values determined from the TPD.In recent years, Au and Ag nanomaterials have been widely used in the determination of glucose owing to their specific properties such as large specific surface area, high extinction coefficient, strong localized surface plasmon resonance effect and enzyme-mimicking activity. Compared with other methods, colorimetric determination of glucose with Au or Ag nanomaterials features the advantages of simple operation, low cost and easy observation. In this review, several typical synthesis methods of Au and Ag nanomaterials are introduced. Strategies for the colorimetric determination of glucose by Au or Ag nanomaterials are elaborated. The challenges and prospects of the application of Au or Ag nanomaterials for colorimetric detection of glucose are also discussed.A series of iridium hydride complexes featuring dihydrogen bonding are presented and shown to undergo rapid H+/H- exchange (1240 s-1 at 25 °C). We demonstrate that the H+/H- exchange rate can be modified by post-synthetic modification at a remote site using BH3, Zn(C6F5)2, and [Me3O][BF4]. This route provides a complementary strategy to traditional methods that rely on pre-metalation modifications to a metal's primary sphere.Video capsule endoscope (VCE) is an emerging technology that allows examination of the entire gastrointestinal (GI) tract with minimal invasion. While traditional endoscopy with biopsy procedures are the gold standard for diagnosis of most GI diseases, they are limited by how far the scope can be advanced in the tract and are also invasive. VCE allows gastroenterologists to investigate GI tract abnormalities in detail with visualization of all parts of the GI tract. It captures continuous real time images as it is propelled in the GI tract by gut motility. Even though VCE allows for thorough examination, reviewing and analyzing up to eight hours of images (compiled as videos) is tedious and not cost effective. In order to pave way for automation of VCE-based GI disease diagnosis, detecting the location of the capsule would allow for a more focused analysis as well as abnormality detection in each region of the GI tract. In this paper, we compared four deep Convolutional Neural Network models for feature extraction and detection of the anatomical part within the GI tract captured by VCE images. Our results showed that VGG-Net has superior performance with the highest average accuracy, precision, recall and, F1-score compared to other state of the art architectures GoogLeNet, AlexNet and, ResNet.Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. Traditionally proposed stain normalization and color augmentation strategies can handle the human level bias. But deep learning models can easily disentangle the linear transformation used in these approaches, resulting in undesirable bias and lack of generalization. To handle these limitations, we propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.Early diagnosis of Autism Spectrum Disorder (ASD) is crucial for best outcomes to interventions. In this paper, we present a machine learning (ML) approach to ASD diagnosis based on identifying specific behaviors from videos of infants of ages 6 through 36 months. The behaviors of interest include directed gaze towards faces or objects of interest, positive affect, and vocalization. The dataset consists of 2000 videos of 3-minute duration with these behaviors manually coded by expert raters. Moreover, the dataset has statistical features including duration and frequency of the above mentioned behaviors in the video collection as well as independent ASD diagnosis by clinicians. We tackle the ML problem in a two-stage approach. Firstly, we develop deep learning models for automatic identification of clinically relevant behaviors exhibited by infants in a one-on-one interaction setting with parents or expert clinicians. We report baseline results of behavior classification using two methods (1) image based model (2) facial behavior features based model. We achieve 70% accuracy for smile, 68% accuracy for look face, 67% for look object and 53% accuracy for vocalization. Secondly, we focus on ASD diagnosis prediction by applying a feature selection process to identify the most significant statistical behavioral features and a over and under sampling process to mitigate the class imbalance, followed by developing a baseline ML classifier to achieve an accuracy of 82% for ASD diagnosis.The anterior gradient homologue-2 (AGR2) protein is an attractive biomarker for various types of cancer. In pancreatic cancer, it is secreted to the pancreatic juice by premalignant lesions, which would be an ideal stage for diagnosis. Thus, designing assays for the sensitive detection of AGR2 would be highly valuable for the potential early diagnosis of pancreatic and other types of cancer. Herein, we present a biosensor for label-free AGR2 detection and investigate approaches for enhancing the aptasensor sensitivity by accelerating the target mass transfer rate and reducing the system noise. The biosensor is based on a nanostructured porous silicon thin film that is decorated with anti-AGR2 aptamers, where real-time monitoring of the reflectance changes enables the detection and quantification of AGR2, as well as the study of the diffusion and target-aptamer binding kinetics. The aptasensor is highly selective for AGR2 and can detect the protein in simulated pancreatic juice, where its concentration is outnumbered by orders of magnitude by numerous proteins. The aptasensor's analytical performance is characterized with a linear detection range of 0.05-2 mg mL-1, an apparent dissociation constant of 21 ± 1 μM, and a limit of detection of 9.2 μg mL-1 (0.2 μM), which is attributed to mass transfer limitations. To improve the latter, we applied different strategies to increase the diffusion flux to and within the nanostructure, such as the application of isotachophoresis for the preconcentration of AGR2 on the aptasensor, mixing, or integration with microchannels. By combining these approaches with a new signal processing technique that employs Morlet wavelet filtering and phase analysis, we achieve a limit of detection of 15 nM without compromising the biosensor's selectivity and specificity.Herein, we report the origin of unexpected reactivity of bicyclo[4.2.0]oct-6-ene substrates containing an α,β-unsaturated amide moiety in ruthenium-catalyzed alternating ring-opening metathesis polymerization reactions. Specifically, compared with control substrates bearing an ester, alkyl ketone, nitrile, or tertiary amide substituent, α,β-unsaturated substrates with a weakly acidic proton showed increased rates of ring-opening metathesis mediated by Grubbs-type ruthenium catalysts. 1H NMR and IR spectral analyses indicated that deprotonation of the α,β-unsaturated amide substrates resulted in stronger coordination of the carbonyl group to the ruthenium metal center. Principal component analysis identified ring strain and the electron density on the carbonyl oxygen (based on structures optimized by means of ωB97X-D/6311+G(2df,2p) calculations) as the two key contributors to fast ring-opening metathesis of the bicyclo[4.2.0]oct-6-enes; whereas the dipole moment, conjugation, and energy of the highest occupied molecular orbital had little to no effect on the reaction rate.