scholarly journals DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

2021 ◽  
Author(s):  
Sterling Baird ◽  
Tran Diep ◽  
Taylor Sparks
Keyword(s):  
High Performance ◽  
Pareto Front ◽  
Screening Tool ◽  
Chemical Space ◽  
Chemical Compositions ◽  
Trade Off ◽  
High Performing ◽  
Chemical Distance ◽  
Weighted Score ◽  
Unique Chemical

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

scholarly journals DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

2021 ◽  
Author(s):  
Sterling Baird ◽  
Tran Diep ◽  
Taylor Sparks
Keyword(s):  
High Performance ◽  
Pareto Front ◽  
Screening Tool ◽  
Chemical Space ◽  
Chemical Compositions ◽  
Trade Off ◽  
High Performing ◽  
Chemical Distance ◽  
Weighted Score ◽  
Unique Chemical

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

scholarly journals DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions

2021 ◽  
Author(s):  
Sterling Baird ◽  
Tran Diep ◽  
Taylor Sparks
Keyword(s):  
High Performance ◽  
Pareto Front ◽  
Screening Tool ◽  
Chemical Space ◽  
Chemical Compositions ◽  
Trade Off ◽  
High Performing ◽  
Chemical Distance ◽  
Weighted Score ◽  
Unique Chemical

We present Descending from Stochastic Clustering Variance Regression (DiSCoVeR), a Python tool for identifying high-performing, chemically unique compositions relative to existing compounds using a combination of a chemical distance metric, density-aware dimensionality reduction, and clustering. We introduce several new metrics for materials discovery and validate DiSCoVeR on Materials Project bulk moduli using compound-wise and cluster-wise validation methods. We visualize these via multiobjective Pareto front plots and assign a weighted score to each composition where this score encompasses the trade-off between performance and density-based chemical uniqueness. We explore an additional uniqueness proxy related to property gradients in chemical space. We demonstrate that DiSCoVeR can successfully screen materials for both performance and uniqueness in order to extrapolate to new chemical spaces.

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals SERS Biosensor Based on Engineered 2D-Aperiodic Nanostructure for In-Situ Detection of Viable Brucella Bacterium in Complex Matrix

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 886
Author(s):  
Massimo Rippa ◽  
Riccardo Castagna ◽  
Domenico Sagnelli ◽  
Ambra Vestri ◽  
Giorgia Borriello ◽  
...  
Keyword(s):  
High Performance ◽  
Foodborne Pathogen ◽  
Surface Enhanced Raman Spectroscopy ◽  
High Performing ◽  
Sensing Platform ◽  
Food Detection ◽  
Raman Enhancement ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
In Situ Detection

Brucella is a foodborne pathogen globally affecting both the economy and healthcare. Surface Enhanced Raman Spectroscopy (SERS) nano-biosensing can be a promising strategy for its detection. We combined high-performance quasi-crystal patterned nanocavities for Raman enhancement with the use of covalently immobilized Tbilisi bacteriophages as high-performing bio-receptors. We coupled our efficient SERS nano-biosensor to a Raman system to develop an on-field phage-based bio-sensing platform capable of monitoring the target bacteria. The developed biosensor allowed us to identify Brucella abortus in milk by our portable SERS device. Upon bacterial capture from samples (104 cells), a signal related to the pathogen recognition was observed, proving the concrete applicability of our system for on-site and in-food detection.

scholarly journals Calculating the optimal hematocrit under the constraint of constant cardiac power

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Michal Sitina ◽  
Heiko Stark ◽  
Stefan Schuster
Keyword(s):  
Blood Flow ◽  
High Performance ◽  
Animal Species ◽  
Normal Values ◽  
Mechanical Load ◽  
The Body ◽  
Trade Off ◽  
Cardiac Power ◽  
Optimal Value ◽  
Good Agreement

AbstractIn humans and higher animals, a trade-off between sufficiently high erythrocyte concentrations to bind oxygen and sufficiently low blood viscosity to allow rapid blood flow has been achieved during evolution. Optimal hematocrit theory has been successful in predicting hematocrit (HCT) values of about 0.3–0.5, in very good agreement with the normal values observed for humans and many animal species. However, according to those calculations, the optimal value should be independent of the mechanical load of the body. This is in contradiction to the exertional increase in HCT observed in some animals called natural blood dopers and to the illegal practice of blood boosting in high-performance sports. Here, we present a novel calculation to predict the optimal HCT value under the constraint of constant cardiac power and compare it to the optimal value obtained for constant driving pressure. We show that the optimal HCT under constant power ranges from 0.5 to 0.7, in agreement with observed values in natural blood dopers at exertion. We use this result to explain the tendency to better exertional performance at an increased HCT.

scholarly journals Decoupling electron and phonon transport in single-nanowire hybrid materials for high-performance thermoelectrics

2021 ◽  
Vol 7 (20) ◽  
pp. eabe6000
Author(s):  
Lin Yang ◽  
Madeleine P. Gordon ◽  
Akanksha K. Menon ◽  
Alexandra Bruefach ◽  
Kyle Haas ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Thermal Transport ◽  
Electrical Transport ◽  
High Performance ◽  
Figure Of Merit ◽  
Phonon Transport ◽  
Core Shell ◽  
Nanowire Diameter ◽  
High Performing ◽  
Polycrystalline Thin Films

Organic-inorganic hybrids have recently emerged as a class of high-performing thermoelectric materials that are lightweight and mechanically flexible. However, the fundamental electrical and thermal transport in these materials has remained elusive due to the heterogeneity of bulk, polycrystalline, thin films reported thus far. Here, we systematically investigate a model hybrid comprising a single core/shell nanowire of Te-PEDOT:PSS. We show that as the nanowire diameter is reduced, the electrical conductivity increases and the thermal conductivity decreases, while the Seebeck coefficient remains nearly constant—this collectively results in a figure of merit, ZT, of 0.54 at 400 K. The origin of the decoupling of charge and heat transport lies in the fact that electrical transport occurs through the organic shell, while thermal transport is driven by the inorganic core. This study establishes design principles for high-performing thermoelectrics that leverage the unique interactions occurring at the interfaces of hybrid nanowires.

scholarly journals Integrating high-performing electrochemical transducers in lateral flow assay

2021 ◽  
Author(s):  
Antonia Perju ◽  
Nongnoot Wongkaew
Keyword(s):  
High Performance ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
Lateral Flow ◽  
Analytical Performance ◽  
Label Free ◽  
High Performing ◽  
Lateral Flow Assays ◽  
Electrochemical Transducers

AbstractLateral flow assays (LFAs) are the best-performing and best-known point-of-care tests worldwide. Over the last decade, they have experienced an increasing interest by researchers towards improving their analytical performance while maintaining their robust assay platform. Commercially, visual and optical detection strategies dominate, but it is especially the research on integrating electrochemical (EC) approaches that may have a chance to significantly improve an LFA’s performance that is needed in order to detect analytes reliably at lower concentrations than currently possible. In fact, EC-LFAs offer advantages in terms of quantitative determination, low-cost, high sensitivity, and even simple, label-free strategies. Here, the various configurations of EC-LFAs published are summarized and critically evaluated. In short, most of them rely on applying conventional transducers, e.g., screen-printed electrode, to ensure reliability of the assay, and additional advances are afforded by the beneficial features of nanomaterials. It is predicted that these will be further implemented in EC-LFAs as high-performance transducers. Considering the low cost of point-of-care devices, it becomes even more important to also identify strategies that efficiently integrate nanomaterials into EC-LFAs in a high-throughput manner while maintaining their favorable analytical performance.

scholarly journals A comparison between enzyme immunoassay and HPLC for ochratoxin A detection in green, roasted and instant coffee

2007 ◽  
Vol 50 (2) ◽  
pp. 349-359 ◽  
Author(s):  
Simone Fujii ◽  
Elisabete Yurie Sataque Ono ◽  
Ricardo Marcelo Reche Ribeiro ◽  
Fernanda Garcia Algarte Assunção ◽  
Cássia Reika Takabayashi ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Ochratoxin A ◽  
High Performance ◽  
Screening Tool ◽  
Correlation Coefficients ◽  
Enzyme Linked Immunosorbent Assay ◽  
Elisa Assay ◽  
Quality And Safety ◽  
Green Coffee ◽  
Instant Coffee

An indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) for ochratoxin A (OTA) detection in green, roasted and instant coffees was developed using anti-OTA monoclonal antibody. Immunological reagents prepared were OTA-BSA (4.76 mg/mL), anti-OTA.7 MAb (2x10³-fold dilution) and HRP-anti IgG (10³-fold dilution). The detection limit was 3.73 ng OTA/g and correlation coefficients (r) between this immunoassay and high performance liquid chromatography were 0.98 for green coffee, 0.98 for roasted and 0.86 for instant. OTA levels detected by ic-ELISA were higher than by HPLC, with ELISA/HPLC ratio of 0.66 - 1.46 (green coffee), 0.96 - 1.11 (roasted) and 0.93 - 1.82 (instant). ELISA recoveries for OTA added to coffee (5 - 70 ng/g) were 81.53 % for green coffee, 46.73 % for roasted and 64.35 % for instant, while recoveries by HPLC were 80.54 %, 45.91 % and 55.15 %, respectively. Matrices interferences were minimized by samples dilution before carrying out the ELISA assay. The results indicate that MAb-based ic-ELISA could be a simple, sensitive and specific screening tool for OTA detection, contributing to quality and safety of coffee products.

scholarly journals Understanding High Performance in Late Second Language (L2) Acquisition—What Is the Secret? A Contrasting Case Study in L2 French

Languages ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Fanny Forsberg Lundell ◽  
Klara Arvidsson
Keyword(s):  
Language Learning ◽  
High Performance ◽  
Native Speaker ◽  
Age Of Onset ◽  
Learning Experience ◽  
Host Community ◽  
L2 Acquisition ◽  
Attribute Importance ◽  
High Performing ◽  
High Performers

Adult L2 acquisition has often been framed within research on the Critical Period Hypothesis, and the age factor is one of the most researched topics of SLA. However, several researchers suggest that while age is the most important factor for differences between child and adult SLA, variation in adult SLA is more dependent on social and psychological factors than on age of onset. The present qualitative study investigates the role of migratory experience, language use/social networks, language learning experience, identity and attitudes for high performance among Swedish L1 French L2 users in France. The study constitutes an in-depth thematic analysis of interviews with six high-performing individuals and four low-performing individuals. The main results show that the high performers differ from the low performers on all dimensions, except for attitudes towards the host community. High performers are above all characterized by self-reported language aptitude and an early interest in languages, which appears to have led to rich exposure to French. Also, they exhibit self-regulatory behaviors and attribute importance to being perceived as a native speaker of French—both for instrumental and existential reasons.

Applications and Advantages of Basalt Assembly in Construction Industry

2011 ◽  
Vol 332-334 ◽  
pp. 1937-1940 ◽  
Author(s):  
Wei Wei Hu ◽  
Hua Wu Liu ◽  
Dang Feng Zhao ◽  
Zong Bin Yang
Keyword(s):  
Construction Industry ◽  
High Performance ◽  
Inorganic Material ◽  
Basalt Fiber ◽  
Fiber Reinforced Polymer ◽  
Chemical Compositions ◽  
Basalt Fibers ◽  
Reinforced Polymer ◽  
Reinforcing Fibers ◽  
Reinforcement Material

Basalt fiber is a novel high-performance inorganic material, recently has been well received as a reinforcement in China. However, the applications in civil engineering have been rather limited. The chemical compositions, the characteristics of basalt fibers, and the typical products of basalt, including chopped yarn of basalt fiber, basalt fiber geo-textiles and basalt fiber reinforced polymer, were introduced．The advantages of basalt fibers as a reinforcement of concrete were explored in comparison with the commonly used reinforcing fibers, which indicates that basalt fiber is the most promising reinforcement material for concrete and will significantly benefit civil construction industries in the future.

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