Abstract
The stoichiometric photocatalytic reaction of CO
2
with H
2
O is one of the great challenges in photocatalysis. Here, we construct a Cu
2
O-Pt/SiC/IrO
x
composite by a controlled ...photodeposition and then an artificial photosynthetic system with Nafion membrane as diaphragm separating reduction and oxidation half-reactions. The artificial system exhibits excellent photocatalytic performance for CO
2
reduction to HCOOH and H
2
O oxidation to O
2
under visible light irradiation. The yields of HCOOH and O
2
meet almost stoichiometric ratio and are as high as 896.7 and 440.7 μmol g
−1
h
−1
, respectively. The high efficiencies of CO
2
reduction and H
2
O oxidation in the artificial system are attributed to both the direct Z-scheme electronic structure of Cu
2
O-Pt/SiC/IrO
x
and the indirect Z-scheme spatially separated reduction and oxidation units, which greatly prolong lifetime of photogenerated electrons and holes and prevent the backward reaction of products. This work provides an effective and feasible strategy to increase the efficiency of artificial photosynthesis.
A marigold-like SiC@MoS2 nanoflower with a unique Z-scheme structure efficiently achieves the overall conversion of gas phase CO2 with H2O (CO2 (g) + 2H2O (g) = CH4 + 2O2) without any sacrificial ...reagents under visible light (λ ≥ 420 nm) irradiation. The CH4 and O2 evolution are 323 and 621 μL·g–1·h–1, and stable throughout 5 cycle reactions of total 40 h. This work demonstrates a breakthrough in artificial photosynthesis with the Z-scheme 1D heterojunction constructed by combining 2D semiconductor and 3D semiconductor based on the transfer balance of photogenerated electron and hole.
Porous silica‐based materials have burgeoning applications ranging from fillers and additives, to adsorbents, catalysts, and recently therapeutic agents and vaccines in nanomedicine. The ...preponderance of these materials is made by sol–gel processing wherein soluble silica precursors are reacted to form amorphous networks composed of siloxane bonds. The facile sol–gel approach allows for an unlimited variety of binary tertiary and more complex chemical compositions including organic ligands and networks resulting in so‐called organic–inorganic hybrid materials. Here, a brief review of the recent progress in sol–gel‐derived silica materials prepared as particles, thin films, biosilica/silica bioreplicas (of molecules, cells and organisms), and their related preparation, properties, and bioapplications is provided. First, it highlights the recent achievements of mesoporous silica nanoparticles in biomedical applications, including therapeutic agent delivery, multimodal imaging and theranostics, and bone tissue engineering and repair. Second, the research in evaporation‐induced self‐assembly (EISA)‐based mesostructured silica thin films and cell‐directed EISA in bio/nano interfaces for various bioapplications, such as bioactive coatings, biosensing, and living cell immobilization, has been reviewed. Third, the pioneering work in biomimetic silicification/immobilization of biomolecules and bio‐organisms and silica bioreplication of complex bio‐organisms is summarized. Finally, it is concluded with personal perspectives on the directions of future work on this field.
The most recent progress in sol–gel‐derived silica materials prepared as particles, thin films, biosilica, and silica bioreplicas (of molecules, cells, and organisms) is reviewed. This includes their related preparation, properties, and potential biological and biomedical applications.
Embryonic development relies on the capacity of progenitor cells to appropriately respond to inductive cues, a cellular property known as developmental competence. Here, we report that epigenetic ...priming of enhancers signifies developmental competence during endodermal lineage diversification. Chromatin mapping during pancreatic and hepatic differentiation of human embryonic stem cells revealed the en masse acquisition of a poised chromatin state at enhancers specific to endoderm-derived cell lineages in gut tube intermediates. Experimentally, the acquisition of this poised enhancer state predicts the ability of endodermal intermediates to respond to inductive signals. Furthermore, these enhancers are first recognized by the pioneer transcription factors FOXA1 and FOXA2 when competence is acquired, while subsequent recruitment of lineage-inductive transcription factors, such as PDX1, leads to enhancer and target gene activation. Together, our results identify the acquisition of a poised chromatin state at enhancers as a mechanism by which progenitor cells acquire developmental competence.
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•A poised enhancer landscape for endodermal organ lineages is established in gut tube•Select enhancers involved in cellular identity are activated in descendent lineages•Poised chromatin at lineage-specific enhancers indicates developmental competence•Pioneer TFs associate with poised enhancers prior to activation by pro-lineage TFs
Embryonic development relies on the capacity of progenitor cells to respond appropriately to inductive signals, an ability termed developmental competence. By mapping enhancer-related histone modifications during pancreatic differentiation of human embryonic stem cells, Wang et al. identify a poised state at enhancers as predictive of developmental competence.
Two-dimensional-layered heterojunctions have attracted extensive interest recently due to their exciting behaviours in electronic/optoelectronic devices as well as solar energy conversion systems. ...However, layered heterojunction materials, especially those made by stacking different monolayers together by strong chemical bonds rather than by weak van der Waal interactions, are still challenging to fabricate. Here the monolayer Bi2WO6 with a sandwich substructure of BiO(+)-WO4(2-)-BiO(+) is reported. This material may be characterized as a layered heterojunction with different monolayer oxides held together by chemical bonds. Coordinatively unsaturated Bi atoms are present as active sites on the surface. On irradiation, holes are generated directly on the active surface layer and electrons in the middle layer, which leads to the outstanding performances of the monolayer material in solar energy conversion. Our work provides a general bottom-up route for designing and preparing novel monolayer materials with ultrafast charge separation and active surface.
A major challenge in the development of anion exchange membranes for fuel cells is the design and synthesis of highly stable (chemically and mechanically) conducting membranes. Membranes that can ...endure highly alkaline environments while rapidly transporting hydroxides are desired. Herein, we present a design using cross-linked polymer membranes containing ionic highways along charge-delocalized pyrazolium cations and homoconjugated triptycenes. These ionic highway membranes show improved performance. Specifically, a conductivity of 111.6 mS cm–1 at 80 °C was obtained with a low 7.9% water uptake and 0.91 mmol g–1 ion exchange capacity. In contrast to existing materials, ionic highways produce higher conductivities at reduced hydration and ionic exchange capacities. The membranes retain more than 75% of their initial conductivity after 30 days of an alkaline stability test. The formation of ionic highways for ion transport is confirmed by density functional theory and Monte Carlo studies. A single cell with platinum metal catalysts at 80 °C showed a high peak density of 0.73 W cm–2 (0.45 W cm–2 from a silver-based cathode) and stable performance throughout 400 h tests.
Purpose The purpose of this study was to evaluate the trends and report on the demographics of patients undergoing hip arthroscopy in the United States. Methods Patients who underwent hip arthroscopy ...from 2004 to 2009 were identified by searching Current Procedural Terminology codes in the PearlDiver Patient Records Database (PearlDiver Technologies, Fort Wayne, IN), a national database of orthopaedic insurance records. The year of procedure, age, gender, and region of the United States were recorded for each patient. Results were reported for each variable as the incidence of procedures identified per 10,000 patients searched in the database. Results In total, 3,447 cases of hip arthroscopy were identified between 2004 and 2009. The incidence of procedures increased significantly over the study period, from 1.20 cases per 10,000 patients in 2004 to 5.58 in 2009 ( P < .001). Hip arthroscopy was performed most commonly in patients aged 20 to 39 years ( P < .05), with an incidence of 4.45 cases in each age group. In contrast to other common arthroscopic procedures searched, no gender differences were observed, with a male-to-female ratio of 0.89 ( P = .18). The greatest incidence of hip arthroscopy was observed in the Western region with an incidence of 5.24 cases identified compared with 2.94, 2.70, and 2.56 in the Northeast, Midwest, and South, respectively ( P < .001). Conclusions A 365% increase in the rate of hip arthroscopy was observed in the examined cohort of patients between 2004 and 2009. The majority of cases were performed in patients aged 20 to 39 years, with no difference in gender. The Western region of the United States was found to have a higher incidence of hip arthroscopy compared with the Midwest, South, and Northeast. Level of Evidence Level IV, cross-sectional study.
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental ...challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.
Objectives
To develop and validate a proof-of-concept convolutional neural network (CNN)–based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.
Methods
A custom ...CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (
n
= 434) and test (
n
= 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.
Results
The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms.
Conclusion
This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances.
Key Points
• Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists.
• Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.
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