jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pei Liu | Jacob Madsen | Jakob Schiøtz | Jakob Birkedal Wagner | Thomas W. Hansen
Source: Journal of Physics Materials (article, 2020-03-24)
Field: Materials Science — Materials Chemistry

Reversible and concerted atom diffusion on supported gold nanoparticles Abstract Traditionally, direct imaging of atom diffusion is only available by scanning tunneling microscopy and field ion microscopy on geometry-constrained samples: flat surfaces for STM and needle tips for FIM. Here we show time-resolved atomic-scale HRTEM investigations of CeO 2 -supported Au nanoparticle surfaces to characterize the surface dynamics of atom columns on gold nanoparticles. The observed surface dynamics have been categorized into four types: layer jumping, layer gliding, re-orientation and surface reconstruction. We successfully captured atoms moving in a concerted manner with a time resolution of 0.1 s. A quantitative approach for measuring the dynamics in various gaseous surroundings at elevated temperatures is presented. An approach for measuring quantitative electron beam effects on the surface dynamics is presented by counting atom column occupation as a function of time under a range of dose rates in high vacuum. Atom (system on chip) ; Colloidal gold ; Diffusion ; Nanoparticle ; Nanotechnology ; Materials science ; Chemical physics ; Chemistry ; Thermodynamics ; Physics ; Computer science Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Nanomaterials for catalytic reactions
Authors: P.V. Manu | T.R. Navaneeth Kishan | S. Jayaraj | A. Ramaraju
Source: International Journal of Energy Technology and Policy (review, 2020-12-22)
Field: Materials Science — Materials Chemistry

On-board generation of HHO gas with dry cell electrolyser and its applications: a review In this era of depleting fossil fuels, alternative fuels and their study has gained much importance. One of the methods of using alternative fuels is operating the IC engines in dual fuel mode. This review is an attempt to highlight the investigations on the HHO gas supplemented combustion in the internal combustion engines, particularly the diesel operated ones. HHO gas has better burning characteristics than pure hydrogen since, hydrogen and oxygen does not attain the diatomic state. Hence, ideally HHO gas is having higher energy releasing capability. HHO inducted systems show better performance than that of the ordinary diesel engines in terms of brake thermal efficiency, specific fuel consumption and engine torque. However, the volumetric efficiency of these systems are relatively lesser. HHO inducted systems also show better emission characteristics. The emission of CO and unburned hydrocarbons are almost reduced by 30-40%. One of the major disadvantages of using HHO gas is the possible increase in NOx emission. Fuel cells ; Materials science ; Environmental science ; Engineering ; Chemical engineering Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Hybrid Renewable Energy Systems
Authors: Álvaro Rodríguez-Prieto | Ana María Camacho | Manuel Callejas | Miguel Ángel Sebastián Pérez
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Metals and Alloys

Fitness for Service and Reliability of Materials for Manufacturing Components Intended for Demanding Service Conditions in the Petrochemical Industry A methodology has been developed to quantitatively assess the suitability of use and fitness for service of candidate materials using a novel approach that includes multiple perspectives. As a case study, a carbon steel pipe has been selected for operation in the petrochemical sector. The materials studied were the following: American Petroleum Institute (API) A25, A, B, X42, X46, X52, X56, X60, X65 and X70, as well as American Society for Testing and Materials (ASTM) A-106 Gr. A, B and C. The developed model combines an analytical multiperspective approach with calculation methods based on recognized prestige standards. In the present study, the following material degradation mechanisms have been considered: generalized corrosion, fracture due to mechanical overload and high-temperature degradation. Several novel analysis elements have been incorporated into this new methodology, such as the concept of a suitability matrix and a fitness for service index. The approach allows construction of a decision diagram, and the best alternatives ordered according to the criteria and restrictions that have arisen from the analysis are obtained. Additionally, from the analysis, a series of service limitations are proposed based on the maximum hours of operation of a component. The materials ASTM A-106 Gr. A, API-A, ASTM A-106 Gr. B and API-B maintain the best balance between properties and show greater reliability versus the probability of failure due to the degradation mechanisms considered in this study. In addition, some use limitations such as critical exposure temperature have been determined for these materials (450 °C for ASTM A-106 Gr. A designation and 440 °C for API-B and ASTM A-106 Gr. B designations) to avoid the harmful effects of high-temperature operation on the material mechanical properties. Petrochemical ; Reliability (semiconductor) ; Reliability engineering ; Service (business) ; Computer science ; Service life ; Component (thermodynamics) ; Mean time between failures ; Process engineering ; Forensic engineering ; Materials science ; Engineering ; Waste management ; Failure rate Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability ; Structural Integrity and Reliability Analysis
Authors: Mostafa Hosseini | Amir H. Mohammadi
Source: Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

A Gibbs free energy minimization based model for liquid–liquid equilibrium calculation of a system containing oil, brine, and surfactant Accurate and reliable phase equilibrium calculations of microemulsion systems are of great importance. This study deals with the thermodynamic modeling of Liquid–Liquid Equilibrium (LLE) of a system including oil ( n -decane), brine (containing CaCl 2 salt), and ionic surfactant (sodium dodecyl sulfonate). Two models of UNIQUAC and UNIQUAC + Debye–Hückel were used for thermodynamic calculations. The LLE experimental data were utilized to estimate the binary interaction parameters of UNIQUAC model and the adjustable parameter, b , of the Debye–Hückel model. The thermodynamic model calculates the microemulsion phase’s compositions by minimizing the Gibbs free energy of the LLE system using a combination of genetic algorithm and fmincon function in order to prevent local minima. The thermodynamic modeling results show an appropriate agreement with the experimental data. Accordingly, the presented model of this study can be used as a suitable method to investigate the liquid–liquid equilibrium of systems containing oil, water, and surfactant. UNIQUAC ; Gibbs free energy ; Thermodynamics ; Microemulsion ; Chemistry ; Brine ; Pulmonary surfactant ; Activity coefficient ; Physical chemistry ; Physics ; Aqueous solution ; Non-random two-liquid model Surfactants and Colloidal Systems ; Phase Equilibria and Thermodynamics ; Chemical and Physical Properties in Aqueous Solutions
Authors: Jin Li | Fang Xie
Source: Cellular and Molecular Biology (article, 2020-05-16)
Field: Chemistry — Physical and Theoretical Chemistry

Self-organized criticality of molecular biology and thermodynamic analysis of life system based on optimized particle swarm algorithm In order to improve the thermodynamic analysis and prediction ability of biological self-organized criticality and life system, a prediction model of biological self-organized criticality and thermodynamic characteristics of life system based on particle swarm optimization neural network is proposed. Fuzzy regression parameter fusion model is adopted to rearrange the statistical prior data of biological self-organized criticality and thermodynamic characteristics of life system, neural network training method is adopted to extract principal component characteristics of rearranged biological self-organized criticality and thermodynamic information flow of life system, and optimized particle swarm algorithm is adopted to carry out feature selection and self-organized supervised learning on extracted principal component characteristics, thus realizing accurate prediction of biological self-organized criticality and thermodynamic characteristics of life system. The simulation results show that the prediction accuracy of biological self-organization criticality and thermodynamic characteristics of life system using this model is high, the prior sample knowledge required is relatively small, and the reliability of biological self-organization criticality characteristics analysis is guaranteed. Criticality ; Particle swarm optimization ; Computer science ; Thermodynamic system ; Artificial neural network ; Principal component analysis ; Self-organized criticality ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Thermodynamics thermodynamics and calorimetric analyses
Authors: Saeed Ahmed | Jingsong Pan | Dianqing Li | Pinggui Tang | Xin Shu | Yongjun Feng
Source:  (preprint, 2020-01-09)
Field: Materials Science — Materials Chemistry

Growth and Removal Behavior of Magnesium Oxide Microspheres towards Methyl Orange and Methylene Blue in Aqueous Solution The adsorption of dyes from the industrial influent has attracted great interest to overcome water pollution. In this work, we carefully investigated the growth process of hierarchical MgO microspheres and the difference in the corresponding removal behavior towards two dyes: anionic methyl orange and cationic methylene blue. The MgO microspheres were formed by the self-assembly of thin nanosheets following the reaction time and exhibited the maximum removal capacities are 940.14 mg g -1 for methyl orange and 354.00 mg g -1 for methylene blue. The anionic methyl orange and cationic methylene blue adsorption follow the pseudo-second-order kinetic model, Freundlich isothermal model. The difference in the removal behavior towards two dyes is strongly related to the surface charge of MgO and the charge of pollutants. This work will benefit design and improvement in the removal performance of novel porous adsorbents. Methylene blue ; Methyl orange ; Cationic polymerization ; Adsorption ; Freundlich equation ; Aqueous solution ; Chemistry ; Magnesium ; Orange (colour) ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Catalysis ; Photocatalysis Magnesium Oxide Properties and Applications ; Adsorption and biosorption for pollutant removal
Authors: Wei Du | Ran Pang | Gaoyang Li | Huansheng Cao | Ying Li | Yanchun Liang
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Spectroscopy

DeepUEP: Prediction of Urine Excretory Proteins Using Deep Learning Urine excretory proteins are among the most commonly used biomarkers in body fluids. Computational identification of urine excretory proteins can provide very useful information for identifying targeted disease biomarkers in urine by linking transcriptome or proteomics data. There are few methods based on conventional machine learning algorithms for predicting urine excretory proteins, and most of these methods strongly depend on the extraction of features from urine excretory proteins. An end-to-end model for urine excretory protein prediction, called DeepUEP, is presented using deep neural networks relying on only amino acid sequence information. The model achieves good performance and outperforms existing methods on training and testing sets. By comparing known urinary protein biomarkers with the results of the model, we find that the model can achieve a true-positive rate of over 80% for urinary protein biomarkers that have been detected in more than one study. We also combine our model with transcriptome and proteomics data from lung cancer patients to predict the potential urinary protein biomarkers of lung cancer. A web server is developed for the prediction of urine excretory proteins, and it can be accessed at the following URL: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.csbg-jlu.info/DeepUEP/</uri> . We believe that our prediction model and web server are useful for biomedical researchers who are interested in identifying urinary protein biomarkers, especially for candidate proteins in transcriptome or proteomics analyses of diseased tissues. Proteomics ; Urine ; Excretory system ; Computer science ; Urinary system ; Computational biology ; Transcriptome ; Bioinformatics ; Biology ; Gene ; Biochemistry ; Gene expression ; Endocrinology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics
Authors: Saaketh Desai | Samuel Temple Reeve | Karthik Guda Vishnu | Alejandro Strachan
Source: Journal of Applied Physics (article, 2020-03-27)
Field: Materials Science — Materials Chemistry

Tuning martensitic transformations via coherent second phases in nanolaminates using free energy landscape engineering We explore the possibilities and limitations of using a coherent second phase to engineer the thermo-mechanical properties of a martensitic alloy by modifying the underlying free energy landscape that controls the transformation. We use molecular dynamics simulations of a model atomistic system where the properties of a coherent, nanoscale second phase can be varied systematically. With a base martensitic material that undergoes a temperature-induced transformation from a cubic austenite to a monoclinic martensite, simulations show significant ability to engineer the transformation temperatures, from a ∼50% reduction to a ∼200% increase, with 50 at. % of the cubic second phase. We establish correlations between the properties of the second phase, the transformation characteristics, and the microstructure via the free energy landscape of the two-phase systems. Coherency stresses have a strong influence on the martensitic variants observed and can even cause the non-martensitic second phase to undergo a transformation. Reducing the stiffness of the second phase increases the transformation strain and modifies the martensitic microstructure, increasing the volume fraction of the transformed material. This increase in transformation strain is accompanied by a significant increase in Af and thermal hysteresis, while Ms remains unaltered. Our findings on the tunability of martensitic transformations can be used for informed searches of second phases to achieve desired material properties, such as achieving room temperature, lightweight shape memory alloys. Diffusionless transformation ; Martensite ; Shape-memory alloy ; Energy landscape ; Phase (matter) ; Monoclinic crystal system ; Austenite ; Molecular dynamics ; Microstructure Shape Memory Alloy Transformations ; Solidification and crystal growth phenomena ; Chemical and Physical Properties of Materials
Authors: Ali Hosseingholi Pourasl | Sharifah H. S. Ariffin | Mohammad Taghi Ahmadi | Seyed Saeid Rahimian Koloor | Michal Petrů | Afiq Hamzah
Source: Sensors (article, 2020-01-08)
Field: Materials Science — Materials Chemistry

An Analytical Conductance Model for Gas Detection Based on a Zigzag Carbon Nanotube Sensor Recent advances in nanotechnology have revealed the superiority of nanocarbon species such as carbon nanotubes over other conventional materials for gas sensing applications. In this work, analytical modeling of the semiconducting zigzag carbon nanotube field-effect transistor (ZCNT-FET) based sensor for the detection of gas molecules is demonstrated. We propose new analytical models to strongly simulate and investigate the physical and electrical behavior of the ZCNT sensor in the presence of various gas molecules (CO<sub>2</sub>, H<sub>2</sub>O, and CH<sub>4</sub>). Therefore, we start with the modeling of the energy band structure by acquiring the new energy dispersion relation for the ZCNT and introducing the gas adsorption effects to the band structure model. Then, the electrical conductance of the ZCNT is modeled and formulated while the gas adsorption effect is considered in the conductance model. The band structure analysis indicates that, the semiconducting ZCNT experiences band gap variation after the adsorption of the gases. Furthermore, the bandgap variation influences the conductance of the ZCNT and the results exhibit increments of the ZCNT conductance in the presence of target gases while the minimum conductance shifted upward around the neutrality point. Besides, the <i>I-V</i> characteristics of the sensor are extracted from the conductance model and its variations after adsorption of different gas molecules are monitored and investigated. To verify the accuracy of the proposed models, the conductance model is compared with previous experimental and modeling data and a good consensus is observed. It can be concluded that the proposed analytical models can successfully be applied to predict sensor behavior against different gas molecules. Conductance ; Zigzag ; Materials science ; Electrical resistance and conductance ; Adsorption ; Carbon nanotube ; Band gap ; Molecule ; Dispersion (optics) ; Nanotechnology ; Chemical physics ; Optoelectronics ; Chemistry ; Condensed matter physics ; Physics ; Physical chemistry ; Optics ; Organic chemistry ; Composite material Carbon Nanotubes in Composites ; Gas Sensing Nanomaterials and Sensors ; Graphene research and applications
Authors: Iouri Balachov | Balázs Fekete | Digby D. Macdonald | B.W. Spencer
Source: Nuclear Engineering and Design (article, 2020-09-10)
Field: Materials Science — Materials Chemistry

Lifetime estimation of a BWR core shroud in terms of IGSCC Shroud ; Core (optical fiber) ; Nuclear engineering ; Estimation ; Forensic engineering ; Materials science ; Environmental science ; Engineering ; Structural engineering ; Composite material ; Systems engineering Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear reactor physics and engineering
Authors: Florian Meier | Niklas Köhler | Andreas‐David Brunner | Jean-Marc H. Wanka | Eugenia Voytik | Maximilian T. Strauss | Fabian J. Theis | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-21)
Field: Chemistry — Spectroscopy

Deep learning the collisional cross sections of the peptide universe from a million training samples ABSTRACT The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To explore the nature and utility of the entire peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation – serial fragmentation (PASEF). The scale and precision (CV &lt;1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools library validate the model within a 1.3% median relative error (R &gt; 0.99). Hydrophobicity, position of prolines and histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information. Fragmentation (computing) ; Peptide ; Proteome ; Mass spectrometry ; Sequence (biology) ; Proteomics ; Computer science ; Workflow ; Proteogenomics ; Computational biology ; Chemistry ; Artificial intelligence ; Biology ; Chromatography ; Biochemistry ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Chengfei Zhao | Xuewen Wang | Luying Yu | Lina Wu | Xiaoli Hao | Qicai Liu | Liqing Lin | Zhengjun Huang | Shaohuang Weng | Ai‐Lin Liu | Xinhua Lin
Source: Research Square (preprint, 2020-11-23)
Field: Materials Science — Materials Chemistry

Quaternized carbon quantum dots with broad-spectrum antibacterial activity for treatment of wounds infected with mixed bacteria Broad spectrum ; Bacteria ; Spectrum (functional analysis) ; Quantum dot ; Carbon quantum dots ; Antibacterial activity ; Pathogenic bacteria ; Carbon fibers ; Chemistry ; Nanotechnology ; Microbiology ; Physics ; Materials science ; Mathematics ; Biology ; Quantum mechanics ; Combinatorial chemistry ; Algorithm ; Genetics Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Yanfei Yao | Yanxin Chen
Source: Processes (article, 2020-09-04)
Field: Materials Science — Materials Chemistry

Technical Route to Achieve Ultra-Low Emission of Nitrogen Oxides with Predictive Model of Nitrogen Oxide Background Concentration As the most mature denitration technology in the cement clinker burning process, selective non-catalytic reduction (SNCR) has been unable to meet the requirements of ultra-low nitrogen oxide (NOX) emissions under low ammonia escape, thus a hybrid denitration process based on SNCR was established. The process had three steps: reducing the NOX background concentration (NBC), implementing staged combustion, and optimizing the effect of the SNCR. One of the keys to this process was the real-time acquisition of the NBC. In this paper, a multivariate linear regression model for the prediction of NBC was constructed and applied to one 12,000 t/d production line and one 5000 t/d production line. For the 12,000 t/d production line, NBC had a positive correlation with the temperature of the calciner outlet, the pressure, and the temperature of the kiln hood, and it had a negative correlation with the quantity of the kiln coal, the temperature of the smoke chamber, and the main motor current of the kiln. The influence degree of each parameter on the NBC is gradually weakened according to the above order. The determination coefficient (R2) of the model was 0.771, and the mean absolute error and maximum relative error between the predicted and measured NBC were 6.300 mg/m3 and 18.670 mg/m3 respectively. NOx ; Kiln ; Nitrogen oxide ; Nitrogen ; Combustion ; Correlation coefficient ; Chemistry ; Oxide ; Analytical Chemistry (journal) ; Materials science ; Environmental science ; Process engineering ; Waste management ; Metallurgy ; Engineering ; Environmental chemistry ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; Thermochemical Biomass Conversion Processes ; Advanced Combustion Engine Technologies