scholarly journals Review. Solvothermal Synthesis of Multifunctional Coordination Polymers

2010 ◽  
Vol 65 (8) ◽  
pp. 976-998 ◽  
Author(s):  
Yonggang Zhao ◽  
Kunhao Li ◽  
Jing Li
Keyword(s):  
Coordination Polymers ◽  
Cost Effective ◽  
Building Blocks ◽  
Crystal Formation ◽  
Experimental Conditions ◽  
Different Types ◽  
Temperature And Pressure ◽  
Metal Building ◽  
Solvothermal Methods ◽  
2D And 3D

This review focuses primarily on the past 10 years of our development of multifunctional coordination polymers with 1D, 2D and 3D structures employing low-temperature and cost-effective hydrothermal and solvothermal methods. The effects of the experimental conditions and parameters on the crystal formation and phase separation, including temperature and pressure, reaction pH, solvent and composition, are discussed. Our studies have shown that a variety of different types of network structures may be rationally designed and synthesized by deliberate selection and construction of metal building blocks and organic ligands, which lead to numerous interesting properties and multifunctionality that are promising for applications in gas storage and separation, catalysis and optical sensing

The CSF p-tau181/Aβ42 Ratio Offers a Good Accuracy “In Vivo” in the Differential Diagnosis of Alzheimer’s Dementia

2019 ◽  
Vol 16 (7) ◽  
pp. 587-595 ◽  
Author(s):  
Roberto Santangelo ◽  
Alessandro Dell'Edera ◽  
Arianna Sala ◽  
Giordano Cecchetti ◽  
Federico Masserini ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Clinical Diagnosis ◽  
Fdg Pet ◽  
Amyloid Β ◽  
Cost Effective ◽  
Daily Clinical Practice ◽  
Different Types ◽  
Experimental Trials ◽  
Csf Findings

Background: The incoming disease-modifying therapies against Alzheimer’s disease (AD) require reliable diagnostic markers to correctly enroll patients all over the world. CSF AD biomarkers, namely amyloid-β 42 (Aβ42), total tau (t-tau), and tau phosphorylated at threonine 181 (p-tau181), showed good diagnostic accuracy in detecting AD pathology, but their real usefulness in daily clinical practice is still a matter of debate. Therefore, further validation in complex clinical settings, that is patients with different types of dementia, is needed to uphold their future worldwide adoption. Methods: We measured CSF AD biomarkers’ concentrations in a sample of 526 patients with a clinical diagnosis of dementia (277 with AD and 249 with Other Type of Dementia, OTD). Brain FDG-PET was also considered in a subsample of 54 patients with a mismatch between the clinical diagnosis and the CSF findings. Results: A p-tau181/Aβ42 ratio higher than 0.13 showed the best diagnostic performance in differentiating AD from OTD (86% accuracy index, 74% sensitivity, 81% specificity). In cases with a mismatch between clinical diagnosis and CSF findings, brain FDG-PET partially agreed with the p-tau181/Aβ42 ratio, thus determining an increase in CSF accuracy. Conclusions: The p-tau181/Aβ42 ratio alone might reliably detect AD pathology in heterogeneous samples of patients suffering from different types of dementia. It might constitute a simple, cost-effective and reproducible in vivo proxy of AD suitable to be adopted worldwide not only in daily clinical practice but also in future experimental trials, to avoid the enrolment of misdiagnosed AD patients.

scholarly journals 2D-3D integration of hexagonal boron nitride and a high-κ dielectric for ultrafast graphene-based electro-absorption modulators

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hitesh Agarwal ◽  
Bernat Terrés ◽  
Lorenzo Orsini ◽  
Alberto Montanaro ◽  
Vito Sorianello ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Hafnium Oxide ◽  
High Speed ◽  
Hexagonal Boron Nitride ◽  
Temperature Stability ◽  
Cost Effective ◽  
Fold Increase ◽  
Building Blocks ◽  
High Quality ◽  
New Device

AbstractElectro-absorption (EA) waveguide-coupled modulators are essential building blocks for on-chip optical communications. Compared to state-of-the-art silicon (Si) devices, graphene-based EA modulators promise smaller footprints, larger temperature stability, cost-effective integration and high speeds. However, combining high speed and large modulation efficiencies in a single graphene-based device has remained elusive so far. In this work, we overcome this fundamental trade-off by demonstrating the 2D-3D dielectric integration in a high-quality encapsulated graphene device. We integrated hafnium oxide (HfO2) and two-dimensional hexagonal boron nitride (hBN) within the insulating section of a double-layer (DL) graphene EA modulator. This combination of materials allows for a high-quality modulator device with high performances: a ~39 GHz bandwidth (BW) with a three-fold increase in modulation efficiency compared to previously reported high-speed modulators. This 2D-3D dielectric integration paves the way to a plethora of electronic and opto-electronic devices with enhanced performance and stability, while expanding the freedom for new device designs.

scholarly journals Analyzing AgriFood-Tech e-Business Models

2021 ◽  
Vol 13 (10) ◽  
pp. 5516
Author(s):  
Maro Vlachopoulou ◽  
Christos Ziakis ◽  
Kostas Vergidis ◽  
Michael Madas
Keyword(s):  
Business Models ◽  
Relevant Literature ◽  
Building Blocks ◽  
Distribution Channels ◽  
Digital Environment ◽  
Value Proposition ◽  
Business Model Canvas ◽  
Logistics Services ◽  
Different Types ◽  
Online Searches

The agribusiness sector shows tremendous growth and sustainability prospects by exploiting the challenges of “AgriFood-Tech” business models in the digital environment, by encouraging innovation, accelerating institutional and structural change, enhancing productivity, and introducing new products and services to the market. The purpose of this study is to investigate different types of “AgriFood-Tech” digital models and analyze their role in the agribusiness and AgriFood sector. Based on relevant literature research, the authors present and discuss five indicative examples of “AgriFood-Tech” models, using the Business Model Canvas (BMC) framework. The methodology included the analysis of the components of innovative AgriFood innovative business models paradigms, such as distribution channels, key partnerships, customer selection and relationships, financial viability, and value proposition. The goal was to explore their building blocks and the required decisions that create, deliver, and capture value. Our findings highlight the importance of specific features of the models, including online sharing of information between the stakeholders, online searches of agri-products, and logistics services in the agribusiness sector.

scholarly journals Electrochemical reduction of acetonitrile to ethylamine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Rong Xia ◽  
Dong Tian ◽  
Shyam Kattel ◽  
Bjorn Hasa ◽  
Haeun Shin ◽  
...  
Keyword(s):  
Computational Study ◽  
Building Blocks ◽  
Primary Amines ◽  
Hydrogen Electrode ◽  
Faradaic Efficiency ◽  
Fossil Energy ◽  
Chemical Manufacturing ◽  
Stable Performance ◽  
Temperature And Pressure ◽  
Partial Current

AbstractElectrifying chemical manufacturing using renewable energy is an attractive approach to reduce the dependence on fossil energy sources in chemical industries. Primary amines are important organic building blocks; however, the synthesis is often hindered by the poor selectivity because of the formation of secondary and tertiary amine byproducts. Herein, we report an electrocatalytic route to produce ethylamine selectively through an electroreduction of acetonitrile at ambient temperature and pressure. Among all the electrocatalysts, Cu nanoparticles exhibit the highest ethylamine Faradaic efficiency (~96%) at −0.29 V versus reversible hydrogen electrode. Under optimal conditions, we achieve an ethylamine partial current density of 846 mA cm−2. A 20-hour stable performance is demonstrated on Cu at 100 mA cm−2 with an 86% ethylamine Faradaic efficiency. Moreover, the reaction mechanism is investigated by computational study, which suggests the high ethylamine selectivity on Cu is due to the moderate binding affinity for the reaction intermediates.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Modeling intentional agency: a neo-Gricean framework

Synthese ◽  
2021 ◽  
Author(s):  
Matti Sarkia
Keyword(s):  
Philosophy Of Mind ◽  
General Framework ◽  
Building Blocks ◽  
Theoretical Modeling ◽  
Model Construction ◽  
Coarse Grained ◽  
Scientific Model ◽  
Types Of Knowledge ◽  
Different Types ◽  
Intentional Agency

AbstractThis paper analyzes three contrasting strategies for modeling intentional agency in contemporary analytic philosophy of mind and action, and draws parallels between them and similar strategies of scientific model-construction. Gricean modeling involves identifying primitive building blocks of intentional agency, and building up from such building blocks to prototypically agential behaviors. Analogical modeling is based on picking out an exemplary type of intentional agency, which is used as a model for other agential types. Theoretical modeling involves reasoning about intentional agency in terms of some domain-general framework of lawlike regularities, which involves no detailed reference to particular building blocks or exemplars of intentional agency (although it may involve coarse-grained or heuristic reference to some of them). Given the contrasting procedural approaches that they employ and the different types of knowledge that they embody, the three strategies are argued to provide mutually complementary perspectives on intentional agency.

Spin Crossover in a Series of Non-Hofmann-Type Fe(II) Coordination Polymers Based on [Hg(SeCN)3]− or [Hg(SeCN)4]2– Building Blocks

2021 ◽  
Author(s):  
Tong Cao ◽  
Francisco Javier Valverde-Muñoz ◽  
Xiaoyi Duan ◽  
Mingjian Zhang ◽  
Ping Wang ◽  
...  
Keyword(s):  
Coordination Polymers ◽  
Spin Crossover ◽  
Building Blocks

scholarly journals Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 492
Author(s):  
Valentina Y. Guleva ◽  
Polina O. Andreeva ◽  
Danila A. Vaganov
Keyword(s):  
Social Networks ◽  
Sampling Method ◽  
Building Blocks ◽  
Global Dynamics ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Prediction And Control ◽  
Path Lengths ◽  
And Control ◽  
Diffusion Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.

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